Strategies And Tactics For A Successful Master Data Management Implementation

27 jun 2022 · Data Engineering Podcast

01:09:08

Summary

The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Master Data Management (MDM) is the process of building consensus around what the information actually means in the context of the business and then shaping the data to match those semantics. In this episode Malcolm Hawker shares his years of experience working in this domain to explore the combination of technical and social skills that are necessary to make an MDM project successful both at the outset and over the long term.

AnnouncementsHello and welcome to the Data Engineering Podcast, the show about modern data managementWhen you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show!Random data doesn’t do it — and production data is not safe (or legal) for developers to use. What if you could mimic your entire production database to create a realistic dataset with zero sensitive data? Tonic.ai does exactly that. With Tonic, you can generate fake data that looks, acts, and behaves like production because it’s made from production. Using universal data connectors and a flexible API, Tonic integrates seamlessly into your existing pipelines and allows you to shape and size your data to the scale, realism, and degree of privacy that you need. The platform offers advanced subsetting, secure de-identification, and ML-driven data synthesis to create targeted test data for all of your pre-production environments. Your newly mimicked datasets are safe to share with developers, QA, data scientists—heck, even distributed teams around the world. Shorten development cycles, eliminate the need for cumbersome data pipeline work, and mathematically guarantee the privacy of your data, with Tonic.ai. Data Engineering Podcast listeners can sign up for a free 2-week sandbox account, go to dataengineeringpodcast.com/tonic today to give it a try!RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder.Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer.Your host is Tobias Macey and today I’m interviewing Malcolm Hawker about master data management strategies for the enterpriseInterviewIntroductionHow did you get involved in the area of data management?Can you start by giving your definition of what MDM is and the scope of activities/functions that it includes?How have evolutions in the data landscape shifted the conversation around MDM?Can you describe what Profisee is and the story behind it?What was your path to joining Profisee and what is your role in the business?Who are the target customers for Profisee?What are the challenges that they typically experience that leads them to MDM as a solution for their problems?How does the narrative around data observability/data quality from tools such as Great Expectations, Monte Carlo, etc. differ from the data quality benefits of a MDM strategy?How do recent conversations around semantic/metrics layers compare to the way that MDM approaches the problem of domain modeling?What are the steps to defining an MDM strategy for an organization or business unit?Once there is a strategy, what are the tactical elements of the implementation?What is the role of the toolchain in that implementation? (e.g. Spark, dbt, Airflow, etc.)Can you describe how Profisee is implemented?How does the customer base inform the architectural approach that Profisee has taken?Can you describe the adoption process for an organization that is using Profisee for their MDM?Once an organization has defined and adopted an MDM strategy, what are the ongoing maintenance tasks related to the domain models?What are the most interesting, innovative, or unexpected ways that you have seen MDM used?What are the most interesting, unexpected, or challenging lessons that you have learned while working in MDM?When is Profisee the wrong choice?What do you have planned for the future of Profisee?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?Closing AnnouncementsThank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workersLinksProfiseeMDM == Master Data ManagementCRM == Customer Relationship ManagementERP == Enterprise Resource PlanningLevenshtein Distance AlgorithmSoundexCDP == Customer Data Platform

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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Amazon S3: The Backbone of Modern Data Systems
3 jun· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Mai-Lan Tomsen Bukovec, Vice President of Technology at AWS, talks about the evolution of Amazon S3 and its profound impact on data architecture. From her work on compute systems to leading the development and operations of S3, Mylan shares insights on how S3 has become a foundational element in modern data systems, enabling scalable and cost-effective data lakes since its launch alongside Hadoop in 2006. She discusses the architectural patterns enabled by S3, the importance of metadata in data management, and how S3's evolution has been driven by customer needs, leading to innovations like strong consistency and S3 tables.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.This is a pharmaceutical Ad for Soda Data Quality. Do you suffer from chronic dashboard distrust? Are broken pipelines and silent schema changes wreaking havoc on your analytics? You may be experiencing symptoms of Undiagnosed Data Quality Syndrome — also known as UDQS. Ask your data team about Soda. With Soda Metrics Observability, you can track the health of your KPIs and metrics across the business — automatically detecting anomalies before your CEO does. It’s 70% more accurate than industry benchmarks, and the fastest in the category, analyzing 1.1 billion rows in just 64 seconds. And with Collaborative Data Contracts, engineers and business can finally agree on what “done” looks like — so you can stop fighting over column names, and start trusting your data again.Whether you’re a data engineer, analytics lead, or just someone who cries when a dashboard flatlines, Soda may be right for you. Side effects of implementing Soda may include: Increased trust in your metrics, reduced late-night Slack emergencies, spontaneous high-fives across departments, fewer meetings and less back-and-forth with business stakeholders, and in rare cases, a newfound love of data. Sign up today to get a chance to win a $1000+ custom mechanical keyboard. Visit dataengineeringpodcast.com/soda to sign up and follow Soda’s launch week. It starts June 9th.Your host is Tobias Macey and today I'm interviewing Mai-Lan Tomsen Bukovec about the evolutions of S3 and how it has transformed data architectureInterview
IntroductionHow did you get involved in the area of data management?Most everyone listening knows what S3 is, but can you start by giving a quick summary of what roles it plays in the data ecosystem?What are the major generational epochs in S3, with a particular focus on analytical/ML data systems?The first major driver of analytical usage for S3 was the Hadoop ecosystem. What are the other elements of the data ecosystem that helped shape the product direction of S3?Data storage and retrieval have been core primitives in computing since its inception. What are the characteristics of S3 and all of its copycats that led to such a difference in architectural patterns vs. other shared data technologies? (e.g. NFS, Gluster, Ceph, Samba, etc.)How does the unified pool of storage that is exemplified by S3 help to blur the boundaries between application data, analytical data, and ML/AI data?What are some of the default patterns for storage and retrieval across those three buckets that can lead to anti-patterns which add friction when trying to unify those use cases?The age of AI is leading to a massive potential for unlocking unstructured data, for which S3 has been a massive dumping ground over the years. How is that changing the ways that your customers think about the value of the assets that they have been hoarding for so long?What new architectural patterns is that generating?What are the most interesting, innovative, or unexpected ways that you have seen S3 used for analytical/ML/Ai applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on S3?When is S3 the wrong choice?What do you have planned for the future of S3?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
AWS S3KinesisKafkaSQSEMRDrupalWordpressNetflix Blog on S3 as a Source of TruthHadoopMapReduceNasa JPLFINRA == Financial Industry Regulatory AuthorityS3 Object VersioningS3 Cross RegionS3 TablesIcebergParquetAWS KMSIceberg RESTDuckDBNFS == Network File SystemSambaGlusterFSCephMinIOS3 MetadataPhotoshop Generative FillAdobe FireflyTurbotax AI AssistantAWS Access AnalyzerData ProductsS3 Access PointAWS Nova ModelsLexisNexis ProtegeS3 Intelligent TieringS3 Principal Engineering TenetsThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Scaling Data Operations With Platform Engineering
29 mei· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Chakravarthy Kotaru talks about scaling data operations through standardized platform offerings. From his roots as an Oracle developer to leading the data platform at a major online travel company, Chakravarthy shares insights on managing diverse database technologies and providing databases as a service to streamline operations. He explains how his team has transitioned from DevOps to a platform engineering approach, centralizing expertise and automating repetitive tasks with AWS Service Catalog. Join them as they discuss the challenges of migrating legacy systems, integrating AI and ML for automation, and the importance of organizational buy-in in driving data platform success.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.This is a pharmaceutical Ad for Soda Data Quality. Do you suffer from chronic dashboard distrust? Are broken pipelines and silent schema changes wreaking havoc on your analytics? You may be experiencing symptoms of Undiagnosed Data Quality Syndrome — also known as UDQS. Ask your data team about Soda. With Soda Metrics Observability, you can track the health of your KPIs and metrics across the business — automatically detecting anomalies before your CEO does. It’s 70% more accurate than industry benchmarks, and the fastest in the category, analyzing 1.1 billion rows in just 64 seconds. And with Collaborative Data Contracts, engineers and business can finally agree on what “done” looks like — so you can stop fighting over column names, and start trusting your data again.Whether you’re a data engineer, analytics lead, or just someone who cries when a dashboard flatlines, Soda may be right for you. Side effects of implementing Soda may include: Increased trust in your metrics, reduced late-night Slack emergencies, spontaneous high-fives across departments, fewer meetings and less back-and-forth with business stakeholders, and in rare cases, a newfound love of data. Sign up today to get a chance to win a $1000+ custom mechanical keyboard. Visit dataengineeringpodcast.com/soda to sign up and follow Soda’s launch week. It starts June 9th.Your host is Tobias Macey and today I'm interviewing Chakri Kotaru about scaling successful data operations through standardized platform offeringsInterview
IntroductionHow did you get involved in the area of data management?Can you start by outlining the different ways that you have seen teams you work with fail due to lack of structure and opinionated design?Why NoSQL?Pairing different styles of NoSQL for different problemsUseful patterns for each NoSQL style (document, column family, graph, etc.)Challenges in platform automation and scaling edge casesWhat challenges do you anticipate as a result of the new pressures as a result of AI applications?What are the most interesting, innovative, or unexpected ways that you have seen platform engineering practices applied to data systems?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data platform engineering?When is NoSQL the wrong choice?What do you have planned for the future of platform principles for enabling data teams/data applications?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
RiakDynamoDBSQL ServerCassandraScyllaDBCAP TheoremTerraformAWS Service CatalogBlog PostThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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From Data Discovery to AI: The Evolution of Semantic Layers
21 mei· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast, host Tobias Macy welcomes back Shinji Kim to discuss the evolving role of semantic layers in the era of AI. As they explore the challenges of managing vast data ecosystems and providing context to data users, they delve into the significance of semantic layers for AI applications. They dive into the nuances of semantic modeling, the impact of AI on data accessibility, and the importance of business logic in semantic models. Shinji shares her insights on how SelectStar is helping teams navigate these complexities, and together they cover the future of semantic modeling as a native construct in data systems. Join them for an in-depth conversation on the evolving landscape of data engineering and its intersection with AI.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Shinji Kim about the role of semantic layers in the era of AIInterview
IntroductionHow did you get involved in the area of data management?Semantic modeling gained a lot of attention ~4-5 years ago in the context of the "modern data stack". What is your motivation for revisiting that topic today?There are several overlapping concepts – "semantic layer," "metrics layer," "headless BI." How do you define these terms, and what are the key distinctions and overlaps?Do you see these concepts converging, or do they serve distinct long-term purposes?Data warehousing and business intelligence have been around for decades now. What new value does semantic modeling beyond practices like star schemas, OLAP cubes, etc.?What benefits does a semantic model provide when integrating your data platform into AI use cases?How is it different between using AI as an interface to your analytical use cases vs. powering customer facing AI applications with your data?Putting in the effort to create and maintain a set of semantic models is non-zero. What role can LLMs play in helping to propose and construct those models?For teams who have already invested in building this capability, what additional context and metadata is necessary to provide guidance to LLMs when working with their models?What's the most effective way to create a semantic layer without turning it into a massive project? There are several technologies available for building and serving these models. What are the selection criteria that you recommend for teams who are starting down this path?What are the most interesting, innovative, or unexpected ways that you have seen semantic models used?What are the most interesting, unexpected, or challenging lessons that you have learned while working with semantic modeling?When is semantic modeling the wrong choice?What do you predict for the future of semantic modeling?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
SelectStarSun MicrosystemsMarkov Chain Monte CarloSemantic ModelingSemantic LayerMetrics LayerHeadless BICubePodcast EpisodeAtScaleStar SchemaData VaultOLAP CubeRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeKNN == K-Nearest NeighbersHNSW == Hierarchical Navigable Small Worlddbt Metrics LayerSoda DataLookMLHexPowerBITableauSemantic View (Snowflake)Databricks GenieSnowflake Cortex AnalystMalloyThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Balancing Off-the-Shelf and Custom Solutions in Data Engineering
13 mei· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Tulika Bhatt, a senior software engineer at Netflix, talks about her experiences with large-scale data processing and the future of data engineering technologies. Tulika shares her journey into the data engineering field, discussing her work at BlackRock and Verizon before joining Netflix, and explains the challenges and innovations involved in managing Netflix's impression data for personalization and user experience. She highlights the importance of balancing off-the-shelf solutions with custom-built systems using technologies like Spark, Flink, and Iceberg, and delves into the complexities of ensuring data quality and observability in high-speed environments, including robust alerting strategies and semantic data auditing.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Tulika Bhatt about her experiences working on large scale data processing and her insights on the future trajectory of the supporting technologiesInterview
IntroductionHow did you get involved in the area of data management?Can you start by outlining the ways that operating at large scale change the ways that you need to think about the design of data systems?When dealing with small-scale data systems it can be feasible to have manual processes. What are the elements of large scal data systems that demand autopmation?How can those large-scale automation principles be down-scaled to the systems that the rest of the world are operating?A perennial problem in data engineering is that of data quality. The past 4 years has seen a significant growth in the number of tools and practices available for automating the validation and verification of data. In your experience working with high volume data flows, what are the elements of data validation that are still unsolved?Generative AI has taken the world by storm over the past couple years. How has that changed the ways that you approach your daily work?What do you see as the future realities of working with data across various axes of large scale, real-time, etc.?What are the most interesting, innovative, or unexpected ways that you have seen solutions to large-scale data management designed?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data management across axes of scale?What are the ways that you are thinking about the future trajectory of your work??Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
BlackRockSparkFlinkKafkaCassandraRocksDBNetflix Maestro workflow orchestratorPagerdutyIcebergThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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StarRocks: Bridging Lakehouse and OLAP for High-Performance Analytics
5 mei· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Sida Shen, product manager at CelerData, talks about StarRocks, a high-performance analytical database. Sida discusses the inception of StarRocks, which was forked from Apache Doris in 2020 and evolved into a high-performance Lakehouse query engine. He explains the architectural design of StarRocks, highlighting its capabilities in handling high concurrency and low latency queries, and its integration with open table formats like Apache Iceberg, Delta Lake, and Apache Hudi. Sida also discusses how StarRocks differentiates itself from other query engines by supporting on-the-fly joins and eliminating the need for denormalization pipelines, and shares insights into its use cases, such as customer-facing analytics and real-time data processing, as well as future directions for the platform.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Sida Shen about StarRocks, a high performance analytical database supporting shared nothing and shared data patternsInterview
IntroductionHow did you get involved in the area of data management?Can you describe what StarRocks is and the story behind it?There are numerous analytical databases on the market. What are the attributes of StarRocks that differentiate it from other options?Can you describe the architecture of StarRocks?What are the "-ilities" that are foundational to the design of the system?How have the design and focus of the project evolved since it was first created?What are the tradeoffs involved in separating the communication layer from the data layers?The tiered architecture enables the shared nothing and shared data behaviors, which allows for the implementation of lakehouse patterns. What are some of the patterns that are possible due to the single interface/dual pattern nature of StarRocks?The shared data implementation has cacheing built in to accelerate interaction with datasets. What are some of the limitations/edge cases that operators and consumers should be aware of?StarRocks supports management of lakehouse tables (Iceberg, Delta, Hudi, etc.), which overlaps with use cases for Trino/Presto/Dremio/etc. What are the cases where StarRocks acts as a replacement for those systems vs. a supplement to them?The other major category of engines that StarRocks overlaps with is OLAP databases (e.g. Clickhouse, Firebolt, etc.). Why might someone use StarRocks in addition to or in place of those techologies?We would be remiss if we ignored the dominating trend of AI and the systems that support it. What is the role of StarRocks in the context of an AI application?What are the most interesting, innovative, or unexpected ways that you have seen StarRocks used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on StarRocks?When is StarRocks the wrong choice?What do you have planned for the future of StarRocks?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
StarRocksCelerDataApache DorisSIMD == Single Instruction Multiple DataApache IcebergClickHousePodcast EpisodeDruidFireboltPodcast EpisodeSnowflakeBigQueryTrinoDatabricksDremioData LakehouseDelta LakeApache HiveC++Cost-Based OptimizerIceberg Summit Tencent Games PresentationApache PaimonLancePodcast EpisodeDelta UniformApache ArrowStarRocks Python UDFDebeziumPodcast EpisodeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Exploring NATS: A Multi-Paradigm Connectivity Layer for Distributed Applications
28 apr· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Derek Collison, creator of NATS and CEO of Synadia, talks about the evolution and capabilities of NATS as a multi-paradigm connectivity layer for distributed applications. Derek discusses the challenges and solutions in building distributed systems, and highlights the unique features of NATS that differentiate it from other messaging systems. He delves into the architectural decisions behind NATS, including its ability to handle high-speed global microservices, support for edge computing, and integration with Jetstream for data persistence, and explores the role of NATS in modern data management and its use cases in industries like manufacturing and connected vehicles.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Derek Collison about NATS, a multi-paradigm connectivity layer for distributed applications.Interview
IntroductionHow did you get involved in the area of data management?Can you describe what NATS is and the story behind it?How have your experiences in past roles (cloud foundry, TIBCO messaging systems) informed the core principles of NATS?What other sources of inspiration have you drawn on in the design and evolution of NATS? (e.g. Kafka, RabbitMQ, etc.)There are several patterns and abstractions that NATS can support, many of which overlap with other well-regarded technologies. When designing a system or service, what are the heuristics that should be used to determine whether NATS should act as a replacement or addition to those capabilities? (e.g. considerations of scale, speed, ecosystem compatibility, etc.)There is often a divide in the technologies and architecture used between operational/user-facing applications and data systems. How does the unification of multiple messaging patterns in NATS shift the ways that teams think about the relationship between these use cases?How does the shared communication layer of NATS with multiple protocol and pattern adaptaters reduce the need to replicate data and logic across application and data layers?Can you describe how the core NATS system is architected?How have the design and goals of NATS evolved since you first started working on it?In the time since you first began writing NATS (~2012) there have been several evolutionary stages in both application and data implementation patterns. How have those shifts influenced the direction of the NATS project and its ecosystem?For teams who have an existing architecture, what are some of the patterns for adoption of NATS that allow them to augment or migrate their capabilities?What are some of the ecosystem investments that you and your team have made to ease the adoption and integration of NATS?What are the most interesting, innovative, or unexpected ways that you have seen NATS used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on NATS?When is NATS the wrong choice?What do you have planned for the future of NATS?Contact Info
GitHubLinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
NATSNATS JetStreamSynadiaCloud FoundryTIBCOApplied Physics Lab - Johns Hopkins UniversityCray SupercomputerRVCM Certified MessagingTIBCO ZMSIBM MQJMS == Java Message ServiceRabbitMQMongoDBNodeJSRedisAMQP == Advanced Message Queueing ProtocolPub/Sub PatternCircuit Breaker PatternZero MQAkamaiFastlyCDN == Content Delivery NetworkAt Most OnceAt Least OnceExactly OnceAWS KinesisMemcachedSQSSegmentRudderstackPodcast EpisodeDLQ == Dead Letter QueueMQTT == Message Queueing Telemetry TransportNATS Kafka Bridge10BaseT NetworkWeb AssemblyRedPandaPodcast EpisodePulsar FunctionsmTLSAuthZ (Authorization)AuthN (Authentication)NATS Auth CalloutsOPA == Open Policy AgentRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeHome AssistantPodcast.__init__ EpisodeTailscaleOllamaCDC == Change Data CapturegRPCThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Advanced Lakehouse Management With The LakeKeeper Iceberg REST Catalog
21 apr· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Viktor Kessler, co-founder of Vakmo, talks about the architectural patterns in the lake house enabled by a fast and feature-rich Iceberg catalog. Viktor shares his journey from data warehouses to developing the open-source project, Lakekeeper, an Apache Iceberg REST catalog written in Rust that facilitates building lake houses with essential components like storage, compute, and catalog management. He discusses the importance of metadata in making data actionable, the evolution of data catalogs, and the challenges and innovations in the space, including integration with OpenFGA for fine-grained access control and managing data across formats and compute engines.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Viktor Kessler about architectural patterns in the lakehouse that are unlocked by a fast and feature-rich Iceberg catalogInterview
IntroductionHow did you get involved in the area of data management?Can you describe what LakeKeeper is and the story behind it? What is the core of the problem that you are addressing?There has been a lot of activity in the catalog space recently. What are the driving forces that have highlighted the need for a better metadata catalog in the data lake/distributed data ecosystem?How would you characterize the feature sets/problem spaces that different entrants are focused on addressing?Iceberg as a table format has gained a lot of attention and adoption across the data ecosystem. The REST catalog format has opened the door for numerous implementations. What are the opportunities for innovation and improving user experience in that space?What is the role of the catalog in managing security and governance? (AuthZ, auditing, etc.)What are the channels for propagating identity and permissions to compute engines? (how do you avoid head-scratching about permission denied situations)Can you describe how LakeKeeper is implemented?How have the design and goals of the project changed since you first started working on it?For someone who has an existing set of Iceberg tables and catalog, what does the migration process look like?What new workflows or capabilities does LakeKeeper enable for data teams using Iceberg tables across one or more compute frameworks?What are the most interesting, innovative, or unexpected ways that you have seen LakeKeeper used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on LakeKeeper?When is LakeKeeper the wrong choice?What do you have planned for the future of LakeKeeper?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
LakeKeeperSAPMicrosoft AccessMicrosoft ExcelApache IcebergPodcast EpisodeIceberg REST CatalogPyIcebergSparkTrinoDremioHive MetastoreHadoopNATSPolarsDuckDBPodcast EpisodeDataFusionAtlanPodcast EpisodeOpen MetadataPodcast EpisodeApache AtlasOpenFGAHudiPodcast EpisodeDelta LakePodcast EpisodeLance Table FormatPodcast EpisodeUnity CatalogPolaris CatalogApache GravitinoPodcast Episode KeycloakOpen Policy Agent (OPA)Apache RangerApache NiFiThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Simplifying Data Pipelines with Durable Execution
12 apr· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Jeremy Edberg, CEO of DBOS, about durable execution and its impact on designing and implementing business logic for data systems. Jeremy explains how DBOS's serverless platform and orchestrator provide local resilience and reduce operational overhead, ensuring exactly-once execution in distributed systems through the use of the Transact library. He discusses the importance of version management in long-running workflows and how DBOS simplifies system design by reducing infrastructure needs like queues and CI pipelines, making it beneficial for data pipelines, AI workloads, and agentic AI.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Jeremy Edberg about durable execution and how it influences the design and implementation of business logicInterview
IntroductionHow did you get involved in the area of data management?Can you describe what DBOS is and the story behind it?What is durable execution?What are some of the notable ways that inclusion of durable execution in an application architecture changes the ways that the rest of the application is implemented? (e.g. error handling, logic flow, etc.)Many data pipelines involve complex, multi-step workflows. How does DBOS simplify the creation and management of resilient data pipelines? How does durable execution impact the operational complexity of data management systems?One of the complexities in durable execution is managing code/data changes to workflows while existing executions are still processing. What are some of the useful patterns for addressing that challenge and how does DBOS help?Can you describe how DBOS is architected?How have the design and goals of the system changed since you first started working on it?What are the characteristics of Postgres that make it suitable for the persistence mechanism of DBOS?What are the guiding principles that you rely on to determine the boundaries between the open source and commercial elements of DBOS?What are the most interesting, innovative, or unexpected ways that you have seen DBOS used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on DBOS?When is DBOS the wrong choice?What do you have planned for the future of DBOS?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
DBOSExactly Once SemanticsTemporalSempahorePostgresDBOS TransactPython Typescript Idempotency KeysAgentic AIState MachineYugabyteDBPodcast EpisodeCockroachDBSupabaseNeonPodcast EpisodeAirflowThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Overcoming Redis Limitations: The Dragonfly DB Approach
30 mrt· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Roman Gershman, CTO and founder of Dragonfly DB, explores the development and impact of high-speed in-memory databases. Roman shares his experience creating a more efficient alternative to Redis, focusing on performance gains, scalability, and cost efficiency, while addressing limitations such as high throughput and low latency scenarios. He explains how Dragonfly DB solves operational complexities for users and delves into its technical aspects, including maintaining compatibility with Redis while innovating on memory efficiency. Roman discusses the importance of cost efficiency and operational simplicity in driving adoption and shares insights on the broader ecosystem of in-memory data stores, future directions like SSD tiering and vector search capabilities, and the lessons learned from building a new database engine.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Roman Gershman about building a high-speed in-memory database and the impact of the performance gains on data applicationsInterview
IntroductionHow did you get involved in the area of data management?Can you describe what DragonflyDB is and the story behind it?What is the core problem/use case that is solved by making a "faster Redis"?The other major player in the high performance key/value database space is Aerospike. What are the heuristics that an engineer should use to determine whether to use that vs. Dragonfly/Redis?Common use cases for Redis involve application caches and queueing (e.g. Celery/RQ). What are some of the other applications that you have seen Redis/Dragonfly used for, particularly in data engineering use cases?There is a piece of tribal wisdom that it takes 10 years for a database to iron out all of the kinks. At the same time, there have been substantial investments in commoditizing the underlying components of database engines. Can you describe how you approached the implementation of DragonflyDB to arive at a functional and reliable implementation?What are the architectural elements that contribute to the performance and scalability benefits of Dragonfly?How have the design and goals of the system changed since you first started working on it?For teams who migrate from Redis to Dragonfly, beyond the cost savings what are some of the ways that it changes the ways that they think about their overall system design?What are the most interesting, innovative, or unexpected ways that you have seen Dragonfly used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on DragonflyDB?When is DragonflyDB the wrong choice?What do you have planned for the future of DragonflyDB?Contact Info
GitHubLinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
DragonflyDBRedisElasticacheValKeyAerospikeLaravelSidekiqCelerySeastar FrameworkShared-Nothing Architectureio_uringmidi-redisDunning-Kruger EffectRustThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Bringing AI Into The Inner Loop of Data Engineering With Ascend
24 mrt· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Sean Knapp, CEO of Ascend.io, explores the intersection of AI and data engineering. He discusses the evolution of data engineering and the role of AI in automating processes, alleviating burdens on data engineers, and enabling them to focus on complex tasks and innovation. The conversation covers the challenges and opportunities presented by AI, including the need for intelligent tooling and its potential to streamline data engineering processes. Sean and Tobias also delve into the impact of generative AI on data engineering, highlighting its ability to accelerate development, improve governance, and enhance productivity, while also noting the current limitations and future potential of AI in the field.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Sean Knapp about how Ascend is incorporating AI into their platform to help you keep up with the rapid rate of changeInterview
IntroductionHow did you get involved in the area of data management?Can you describe what Ascend is and the story behind it?The last time we spoke was August of 2022. What are the most notable or interesting evolutions in your platform since then?In that same time "AI" has taken up all of the oxygen in the data ecosystem. How has that impacted the ways that you and your customers think about their priorities?The introduction of AI as an API has caused many organizations to try and leap-frog their data maturity journey and jump straight to building with advanced capabilities. How is that impacting the pressures and priorities felt by data teams?At the same time that AI-focused product goals are straining data teams capacities, AI also has the potential to act as an accelerator to their work. What are the roadblocks/speedbumps that are in the way of that capability?Many data teams are incorporating AI tools into parts of their workflow, but it can be clunky and cumbersome. How are you thinking about the fundamental changes in how your platform works with AI at its center?Can you describe the technical architecture that you have evolved toward that allows for AI to drive the experience rather than being a bolt-on?What are the concrete impacts that these new capabilities have on teams who are using Ascend?What are the most interesting, innovative, or unexpected ways that you have seen Ascend + AI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on incorporating AI into the core of Ascend?When is Ascend the wrong choice?What do you have planned for the future of AI in Ascend?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
AscendCursor AI Code EditorDevinGitHub CopilotOpenAI DeepResearchS3 TablesAWS GlueAWS BedrockSnowparkCo-Intelligence: Living and Working with AI by Ethan Mollick (affiliate link)OpenAI o3The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Astronomer's Role in the Airflow Ecosystem: A Deep Dive with Pete DeJoy
16 mrt· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Pete DeJoy, co-founder and product lead at Astronomer, talks about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3. Pete shares his journey into data engineering, discusses Astronomer's contributions to the Airflow project, and highlights the critical role of Airflow in powering operational data products. He covers the evolution of Airflow, its position in the data ecosystem, and the challenges faced by data engineers, including infrastructure management and observability. The conversation also touches on the upcoming Airflow 3 release, which introduces data awareness, architectural improvements, and multi-language support, and Astronomer's observability suite, Astro Observe, which provides insights and proactive recommendations for Airflow users.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Pete DeJoy about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3Interview
IntroductionCan you describe what Astronomer is and the story behind it?How would you characterize the relationship between Airflow and Astronomer?Astronomer just released your State of Airflow 2025 Report yesterday and it is the largest data engineering survey ever with over 5,000 respondents. Can you talk a bit about top level findings in the report?What about the overall growth of the Airflow project over time?How have the focus and features of Astronomer changed since it was last featured on the show in 2017?Astro Observe GA’d in early February, what does the addition of pipeline observability mean for your customers? What are other capabilities similar in scope to observability that Astronomer is looking at adding to the platform?Why is Airflow so critical in providing an elevated Observability–or cataloging, or something simlar - experience in a DataOps platform? What are the notable evolutions in the Airflow project and ecosystem in that time?What are the core improvements that are planned for Airflow 3.0?What are the most interesting, innovative, or unexpected ways that you have seen Astro used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Airflow and Astro?What do you have planned for the future of Astro/Astronomer/Airflow?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
AstronomerAirflowMaxime BeaucheminMongoDBDatabricksConfluentSparkKafkaDagsterPodcast EpisodePrefectAirflow 3The Rise of the Data Engineer blog postdbtJupyter NotebookZapiercosmos library for dbt in AirflowRuffAirflow Custom OperatorSnowflakeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Accelerated Computing in Modern Data Centers With Datapelago
8 mrt· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Rajan Goyal, CEO and co-founder of Datapelago, talks about improving efficiencies in data processing by reimagining system architecture. Rajan explains the shift from hyperconverged to disaggregated and composable infrastructure, highlighting the importance of accelerated computing in modern data centers. He discusses the evolution from proprietary to open, composable stacks, emphasizing the role of open table formats and the need for a universal data processing engine, and outlines Datapelago's strategy to leverage existing frameworks like Spark and Trino while providing accelerated computing benefits.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Rajan Goyal about how to drastically improve efficiencies in data processing by re-imagining the system architectureInterview
IntroductionHow did you get involved in the area of data management?Can you start by outlining the main factors that contribute to performance challenges in data lake environments?The different components of open data processing systems have evolved from different starting points with different objectives. In your experience, how has that un-planned and un-synchronized evolution of the ecosystem hindered the capabilities and adoption of open technologies?The introduction of a new cross-cutting capability (e.g. Iceberg) has typically taken a substantial amount of time to gain support across different engines and ecosystems. What do you see as the point of highest leverage to improve the capabilities of the entire stack with the least amount of co-ordination?What was the motivating insight that led you to invest in the technology that powers Datapelago?Can you describe the system design of Datapelago and how it integrates with existing data engines?The growth in the generation and application of unstructured data is a notable shift in the work being done by data teams. What are the areas of overlap in the fundamental nature of data (whether structured, semi-structured, or unstructured) that you are able to exploit to bridge the processing gap?What are the most interesting, innovative, or unexpected ways that you have seen Datapelago used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datapelago?When is Datapelago the wrong choice?What do you have planned for the future of Datapelago?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Links
DatapelagoMIPS ArchitectureARM ArchitectureAWS NitroMellanoxNvidiaVon Neumann ArchitectureTPU == Tensor Processing UnitFPGA == Field-Programmable Gate ArraySparkTrinoIcebergPodcast EpisodeDelta LakePodcast EpisodeHudiPodcast EpisodeApache GlutenIntermediate RepresentationTuring CompletenessLLVMAmdahl's LawLSTM == Long Short-Term MemoryThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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The Future of Data Engineering: AI, LLMs, and Automation
26 feb· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Gleb Mezhanskiy, CEO and co-founder of DataFold, talks about the intersection of AI and data engineering. He discusses the challenges and opportunities of integrating AI into data engineering, particularly using large language models (LLMs) to enhance productivity and reduce manual toil. The conversation covers the potential of AI to transform data engineering tasks, such as text-to-SQL interfaces and creating semantic graphs to improve data accessibility, and explores practical applications of LLMs in automating code reviews, testing, and understanding data lineage.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Gleb Mezhanskiy about Interview
IntroductionHow did you get involved in the area of data management?modern data stack is deadwhere is AI in the data stack?"buy our tool to ship AI"opportunities for LLM in DE workflowContact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
DatafoldCopilotCursor IDEAI AgentsDataChatAI Engineering Podcast EpisodeMetrics LayerEmacsLangChainLangGraphCrewAIThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Evolving Responsibilities in AI Data Management
16 feb· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Bartosz Mikulski talks about preparing data for AI applications. Bartosz shares his journey from data engineering to MLOps and emphasizes the importance of data testing over software development in AI contexts. He discusses the types of data assets required for AI applications, including extensive test datasets, especially in generative AI, and explains the differences in data requirements for various AI application styles. The conversation also explores the skills data engineers need to transition into AI, such as familiarity with vector databases and new data modeling strategies, and highlights the challenges of evolving AI applications, including frequent reprocessing of data when changing chunking strategies or embedding models.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Bartosz Mikulski about how to prepare data for use in AI applicationsInterview
IntroductionHow did you get involved in the area of data management?Can you start by outlining some of the main categories of data assets that are needed for AI applications?How does the nature of the application change those requirements? (e.g. RAG app vs. agent, etc.)How do the different assets map to the stages of the application lifecycle?What are some of the common roles and divisions of responsibility that you see in the construction and operation of a "typical" AI application?For data engineers who are used to data warehousing/BI, what are the skills that map to AI apps?What are some of the data modeling patterns that are needed to support AI apps?chunking strategies metadata managementWhat are the new categories of data that data engineers need to manage in the context of AI applications?agent memory generation/evolution conversation history managementdata collection for fine tuningWhat are some of the notable evolutions in the space of AI applications and their patterns that have happened in the past ~1-2 years that relate to the responsibilities of data engineers?What are some of the skills gaps that teams should be aware of and identify training opportunities for?What are the most interesting, innovative, or unexpected ways that you have seen data teams address the needs of AI applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI applications and their reliance on data?What are some of the emerging trends that you are paying particular attention to?Contact Info
WebsiteLinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
SparkRayChunking StrategiesHypothetical document embeddingsModel Fine TuningPrompt CompressionThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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CSVs Will Never Die And OneSchema Is Counting On It
13 jan· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Andrew Luo, CEO of OneSchema, talks about handling CSV data in business operations. Andrew shares his background in data engineering and CRM migration, which led to the creation of OneSchema, a platform designed to automate CSV imports and improve data validation processes. He discusses the challenges of working with CSVs, including inconsistent type representation, lack of schema information, and technical complexities, and explains how OneSchema addresses these issues using multiple CSV parsers and AI for data type inference and validation. Andrew highlights the business case for OneSchema, emphasizing efficiency gains for companies dealing with large volumes of CSV data, and shares plans to expand support for other data formats and integrate AI-driven transformation packs for specific industries.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Andrew Luo about how OneSchema addresses the headaches of dealing with CSV data for your businessInterview
IntroductionHow did you get involved in the area of data management?Despite the years of evolution and improvement in data storage and interchange formats, CSVs are just as prevalent as ever. What are your opinions/theories on why they are so ubiquitous?What are some of the major sources of CSV data for teams that rely on them for business and analytical processes?The most obvious challenge with CSVs is their lack of type information, but they are notorious for having numerous other problems. What are some of the other major challenges involved with using CSVs for data interchange/ingestion?Can you describe what you are building at OneSchema and the story behind it?What are the core problems that you are solving, and for whom?Can you describe how you have architected your platform to be able to manage the variety, volume, and multi-tenancy of data that you process?How have the design and goals of the product changed since you first started working on it?What are some of the major performance issues that you have encountered while dealing with CSV data at scale?What are some of the most surprising things that you have learned about CSVs in the process of building OneSchema?What are the most interesting, innovative, or unexpected ways that you have seen OneSchema used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on OneSchema?When is OneSchema the wrong choice?What do you have planned for the future of OneSchema?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
OneSchemaEDI == Electronic Data InterchangeUTF-8 BOM (Byte Order Mark) CharactersSOAPCSV RFCIcebergSSIS == SQL Server Integration ServicesMS AccessDatafusionJSON SchemaSFTP == Secure File Transfer ProtocolThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Breaking Down Data Silos: AI and ML in Master Data Management
3 jan· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Dan Bruckner, co-founder and CTO of Tamr, talks about the application of machine learning (ML) and artificial intelligence (AI) in master data management (MDM). Dan shares his journey from working at CERN to becoming a data expert and discusses the challenges of reconciling large-scale organizational data. He explains how data silos arise from independent teams and highlights the importance of combining traditional techniques with modern AI to address the nuances of data reconciliation. Dan emphasizes the transformative potential of large language models (LLMs) in creating more natural user experiences, improving trust in AI-driven data solutions, and simplifying complex data management processes. He also discusses the balance between using AI for complex data problems and the necessity of human oversight to ensure accuracy and trust.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world; like in their episode “The Secret Sauce Behind McDonald’s Data Strategy”, which digs into how AI-driven tools can be used to support crew efficiency and customer interactions. In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts.Your host is Tobias Macey and today I'm interviewing Dan Bruckner about the application of ML and AI techniques to the challenge of reconciling data at the scale of businessInterview
IntroductionHow did you get involved in the area of data management?Can you start by giving an overview of the different ways that organizational data becomes unwieldy and needs to be consolidated and reconciled?How does that reconciliation relate to the practice of "master data management"What are the scaling challenges with the current set of practices for reconciling data?ML has been applied to data cleaning for a long time in the form of entity resolution, etc. How has the landscape evolved or matured in recent years?What (if any) transformative capabilities do LLMs introduce?What are the missing pieces/improvements that are necessary to make current AI systems usable out-of-the-box for data cleaning?What are the strategic decisions that need to be addressed when implementing ML/AI techniques in the data cleaning/reconciliation process?What are the risks involved in bringing ML to bear on data cleaning for inexperienced teams?What are the most interesting, innovative, or unexpected ways that you have seen ML techniques used in data resolution?What are the most interesting, unexpected, or challenging lessons that you have learned while working on using ML/AI in master data management?When is ML/AI the wrong choice for data cleaning/reconciliation?What are your hopes/predictions for the future of ML/AI applications in MDM and data cleaning?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
TamrMaster Data ManagementCERNLHCMichael StonebrakerConway's LawExpert SystemsInformation RetrievalActive LearningThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Building a Data Vision Board: A Guide to Strategic Planning
23 dec 2024· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Lior Barak shares his insights on developing a three-year strategic vision for data management. He discusses the importance of having a strategic plan for data, highlighting the need for data teams to focus on impact rather than just enablement. He introduces the concept of a "data vision board" and explains how it can help organizations outline their strategic vision by considering three key forces: regulation, stakeholders, and organizational goals. Lior emphasizes the importance of balancing short-term pressures with long-term strategic goals, quantifying the cost of data issues to prioritize effectively, and maintaining the strategic vision as a living document through regular reviews. He encourages data teams to shift from being enablers to impact creators and provides practical advice on implementing a data vision board, setting clear KPIs, and embracing a product mindset to create tangible business impacts through strategic data management.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementIt’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. Your host is Tobias Macey and today I'm interviewing Lior Barak about how to develop your three year strategic vision for dataInterview
IntroductionHow did you get involved in the area of data management?Can you start by giving an outline of the types of problems that occur as a result of not developing a strategic plan for an organization's data systems?What is the format that you recommend for capturing that strategic vision?What are the types of decisions and details that you believe should be included in a vision statement?Why is a 3 year horizon beneficial? What does that scale of time encourage/discourage in the debate and decision-making process?Who are the personas that should be included in the process of developing this strategy document?Can you walk us through the steps and processes involved in developing the data vision board for an organization?What are the time-frames or milestones that should lead to revisiting and revising the strategic objectives?What are the most interesting, innovative, or unexpected ways that you have seen a data vision strategy used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data strategy development?When is a data vision board the wrong choice?What are some additional resources or practices that you recommend teams invest in as a supplement to this strategic vision exercise?Contact Info
LinkedInSubstackParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
Vision Board OverviewEpisode 397: Defining A Strategy For Your Data ProductsMinto Pyramid PrincipleKPI == Key Performance IndicatorOKR == Objectives and Key ResultsPhil Jackson: Eleven Rings (affiliate link)The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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How Orchestration Impacts Data Platform Architecture
16 dec 2024· Data Engineering Podcast
Summary
The core task of data engineering is managing the flows of data through an organization. In order to ensure those flows are executing on schedule and without error is the role of the data orchestrator. Which orchestration engine you choose impacts the ways that you architect the rest of your data platform. In this episode Hugo Lu shares his thoughts as the founder of an orchestration company on how to think about data orchestration and data platform design as we navigate the current era of data engineering.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementIt’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world, from big picture questions like AI governance and data sharing to more nuanced questions like, how do we balance offense and defense in data management? In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts.Your host is Tobias Macey and today I'm interviewing Hugo Lu about the data platform and orchestration ecosystem and how to navigate the available optionsInterview
IntroductionHow did you get involved in building data platforms?Can you describe what an orchestrator is in the context of data platforms?There are many other contexts in which orchestration is necessary. What are some examples of how orchestrators have adapted (or failed to adapt) to the times?What are the core features that are necessary for an orchestrator to have when dealing with data-oriented workflows?Beyond the bare necessities, what are some of the other features and design considerations that go into building a first-class dat platform or orchestration system?There have been several generations of orchestration engines over the past several years. How would you characterize the different coarse groupings of orchestration engines across those generational boundaries?How do the characteristics of a data orchestrator influence the overarching architecture of an organization's data platform/data operations?What about the reverse?How have the cycles of ML and AI workflow requirements impacted the design requirements for data orchestrators?What are the most interesting, innovative, or unexpected ways that you have seen data orchestrators used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data orchestration?When is an orchestrator the wrong choice?What are your predictions and/or hopes for the future of data orchestration?Contact Info
MediumLinkedInParting Question
From your perspective, what is the biggest thing data teams are missing in the technology today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
OrchestraPrevious Episode: Overview Of The State Of Data OrchestrationCronArgoCDDAGKubernetesData MeshAirflowSSIS == SQL Server Integration ServicesPentahoKettleDataVoloNiFiPodcast EpisodeDagstergRPCCoalescePodcast EpisodedbtDataHubPalantirThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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An Exploration Of The Impediments To Reusable Data Pipelines
8 dec 2024· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast the inimitable Max Beauchemin talks about reusability in data pipelines. The conversation explores the "write everything twice" problem, where similar pipelines are built without code reuse, and discusses the challenges of managing different SQL dialects and relational databases. Max also touches on the evolving role of data engineers, drawing parallels with front-end engineering, and suggests that generative AI could facilitate knowledge capture and distribution in data engineering. He encourages the community to share reference implementations and templates to foster collaboration and innovation, and expresses hopes for a future where code reuse becomes more prevalent.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm joined again by Max Beauchemin to talk about the challenges of reusability in data pipelinesInterview
IntroductionHow did you get involved in the area of data management?Can you start by sharing your current thesis on the opportunities and shortcomings of code and component reusability in the data context?What are some ways that you think about what constitutes a "component" in this context?The data ecosystem has arguably grown more varied and nuanced in recent years. At the same time, the number and maturity of tools has grown. What is your view on the current trend in productivity for data teams and practitioners?What do you see as the core impediments to building more reusable and general-purpose solutions in data engineering?How can we balance the actual needs of data consumers against their requests (whether well- or un-informed) to help increase our ability to better design our workflows for reuse?In data engineering there are two broad approaches; code-focused or SQL-focused pipelines. In principle one would think that code-focused environments would have better composability. What are you seeing as the realities in your personal experience and what you hear from other teams?When it comes to SQL dialects, dbt offers the option of Jinja macros, whereas SDF and SQLMesh offer automatic translation. There are also tools like PRQL and Malloy that aim to abstract away the underlying SQL. What are the tradeoffs across those options that help or hinder the portability of transformation logic?Which layers of the data stack/steps in the data journey do you see the greatest opportunity for improving the creation of more broadly usable abstractions/reusable elements?low/no code systems for code reuseimpact of LLMs on reusability/compositionimpact of background on industry practices (e.g. DBAs, sysadmins, analysts vs. SWE, etc.)polymorphic data models (e.g. activity schema)What are the most interesting, innovative, or unexpected ways that you have seen teams address composability and reusability of data components?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data-oriented tools and utilities?What are your hopes and predictions for sharing of code and logic in the future of data engineering?Contact Info
LinkedInParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
Max's Blog PostAirflowSupersetTableauLookerPowerBICohort AnalysisNextJSAirbytePodcast EpisodeFivetranPodcast EpisodeSegmentdbtSQLMeshPodcast EpisodeSparkLAMP StackPHPRelational AlgebraKnowledge GraphPython MarshmallowData Warehouse Lifecycle Toolkit (affiliate link)Entity Centric Data Modeling Blog PostAmplitudeOSACon presentationol-data-platform Tobias' team's data platform codeThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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The Art of Database Selection and Evolution
1 dec 2024· Data Engineering Podcast
Summary
In this episode of the Data Engineering Podcast Sam Kleinman talks about the pivotal role of databases in software engineering. Sam shares his journey into the world of data and discusses the complexities of database selection, highlighting the trade-offs between different database architectures and how these choices affect system design, query performance, and the need for ETL processes. He emphasizes the importance of understanding specific requirements to choose the right database engine and warns against over-engineering solutions that can lead to increased complexity. Sam also touches on the tendency of engineers to move logic to the application layer due to skepticism about database longevity and advises teams to leverage database capabilities instead. Finally, he identifies a significant gap in data management tooling: the lack of easy-to-use testing tools for database interactions, highlighting the need for better testing paradigms to ensure reliability and reduce bugs in data-driven applications.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementIt’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. Your host is Tobias Macey and today I'm interviewing Sam Kleinman about database tradeoffs across operating environments and axes of scaleInterview
IntroductionHow did you get involved in the area of data management?The database engine you use has a substantial impact on how you architect your overall system. When starting a greenfield project, what do you see as the most important factor to consider when selecting a database?points of friction introduced by database capabilitiesembedded databases (e.g. SQLite, DuckDB, LanceDB), when to use and when do they become a bottlenecksingle-node database engines (e.g. Postgres, MySQL), when are they legitimately a problemdistributed databases (e.g. CockroachDB, PlanetScale, MongoDB)polyglot storage vs. general-purpose/multimodal databasesfederated queries, benefits and limitations ease of integration vs. variability of performance and access control
Contact Info
LinkedInGitHubParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links
MongoDBNeonPodcast EpisodeGlareDBNoSQLS3 Conditional WriteEvent driven architectureCockroachDBCouchbaseCassandraThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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Building a Data Vision Board: A Guide to Strategic Planning

How Orchestration Impacts Data Platform Architecture

An Exploration Of The Impediments To Reusable Data Pipelines

The Art of Database Selection and Evolution