Summary In this episode of the AI Engineering Podcast Mark Brooker, VP and Distinguished Engineer at AWS, talks about how agentic workflows are transforming database usage and infrastructure design. He discusses the evolving role of data in AI systems, from traditional models to more modern approaches like vectors, RAG, and relational databases. Mark explains why agents require serverless, elastic, and operationally simple databases, and how AWS solutions like Aurora and DSQL address these needs with features such as rapid provisioning, automated patching, geodistribution, and spiky usage. The conversation covers topics including tool calling, improved model capabilities, state in agents versus stateless LLM calls, and the role of Lambda and AgentCore for long-running, session-isolated agents. Mark also touches on the shift from local MCP tools to secure, remote endpoints, the rise of object storage as a durable backplane, and the need for better identity and authorization models. The episode highlights real-world patterns like agent-driven SQL fuzzing and plan analysis, while identifying gaps in simplifying data access, hardening ops for autonomous systems, and evolving serverless database ergonomics to keep pace with agentic development.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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 Marc Brooker about the impact of agentic workflows on database usage patterns and how they change the architectural requirements for databasesInterview IntroductionHow did you get involved in the area of data management?Can you describe what the role of the database is in agentic workflows?There are numerous types of databases, with relational being the most prevalent. How does the type and purpose of an agent inform the type of database that should be used?Anecdotally I have heard about how agentic workloads have become the predominant "customers" of services like Neon and Fly.io. How would you characterize the different patterns of scale for agentic AI applications? (e.g. proliferation of agents, monolithic agents, multi-agent, etc.)What are some of the most significant impacts on workload and access patterns for data storage and retrieval that agents introduce?What are the categorical differences in that behavior as compared to programmatic/automated systems?You have spent a substantial amount of time on Lambda at AWS. Given that LLMs are effectively stateless, how does the added ephemerality of serverless functions impact design and performance considerations around having to "re-hydrate" context when interacting with agents?What are the most interesting, innovative, or unexpected ways that you have seen serverless and database systems used for agentic workloads?What are the most interesting, unexpected, or challenging lessons that you have learned while working on technologies that are supporting agentic applications?Contact Info BlogLinkedInParting 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 Aurora DSQLAWS LambdaThree Tier ArchitectureVector DatabaseGraph DatabaseRelational DatabaseVector EmbeddingRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeGraphRAGAI Engineering Podcast EpisodeLLM Tool CallingMCP == Model Context ProtocolA2A == Agent 2 Agent ProtocolAWS Bedrock AgentCoreStrandsLangChainKiroThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

As we look back at 2024, we're highlighting some of our favourite episodes of the year, and with 100 of them to choose from, it wasn't easy! The four guests we'll be recapping with are: Lea Pica - A celebrity in the data storytelling and visualisation space. Richie and Lea cover the full picture of data presentation, how to understand your audience, how to leverage hollywood storytelling and more. Out December 19.Alex Banks - Founder of Sunday Signal. Adel and Alex cover Alex’s journey into AI and what led him to create Sunday Signal, the potential of AI, prompt engineering at its most basic level, chain of thought prompting, the future of LLMs and more. Out December 23.Don Chamberlin - The renowned co-inventor of SQL. Richie and Don explore the early development of SQL, how it became standardized, the future of SQL through NoSQL and SQL++ and more. Out December 26.Tom Tunguz - general Partner at Theory Ventures, a $235m VC firm. Richie and Tom explore trends in generative AI, cloud+local hybrid workflows, data security, the future of business intelligence and data analytics, AI in the corporate sector and more. Out December 30. For our 200th episode, we bring you a special guest and taking a walk down memory lane—to the creation and development of one of the most popular programming languages in the world. Don Chamberlin is renowned as the co-inventor of SQL (Structured Query Language), the predominant database language globally, which he developed with Raymond Boyce in the mid-1970s. Chamberlin's professional career began at IBM Research in Yorktown Heights, New York, following a summer internship there during his academic years. His work on IBM's System R project led to the first SQL implementation and significantly advanced IBM’s relational database technology. His contributions were recognized when he was made an IBM Fellow in 2003 and later a Fellow of the Computer History Museum in 2009 for his pioneering work on SQL and database architectures. Chamberlin also contributed to the development of XQuery, an XML query language, as part of the W3C, which became a W3C Recommendation in January 2007. Additionally, he holds fellowships with ACM and IEEE and is a member of the National Academy of Engineering. In the episode, Richie and Don explore his early career at IBM and the development of his interest in databases alongside Ray Boyce, the database task group (DBTG), the transition to relational databases and the early development of SQL, the commercialization and adoption of SQL, how it became standardized, how it evolved and spread via open source, the future of SQL through NoSQL and SQL++ and much more.  Links Mentioned in the Show: The first-ever journal paper on SQL. SEQUEL: A Structured English Query LanguageDon’s Book: SQL++ for SQL Users: A TutorialSystem R: Relational approach to database managementSQL CoursesSQL Articles, Tutorials and Code-AlongsRelated Episode: Scaling Enterprise Analytics with...

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

Databases are ubiquitous, and you don’t need to be a data practitioner to know that all data everywhere is stored in a database—or is it? While the majority of data around the world lives in a database, the data that helps run the heart of our operating systems—the core functions of our computers— is not stored in the same place as everywhere else. This is due to database storage sitting ‘above’ the operating system, requiring the OS to run before the databases can be used. But what if the OS was built ‘on top’ of a database? What difference could this fundamental change make to how we use computers? Mike Stonebraker is a distinguished computer scientist known for his foundational work in database systems, he is also currently CTO & Co-Founder At DBOS. His extensive career includes significant contributions through academic prototypes and commercial startups, leading to the creation of several pivotal relational database companies such as Ingres Corporation, Illustra, Paradigm4, StreamBase Systems, Tamr, Vertica, and VoltDB. Stonebraker's role as chief technical officer at Informix and his influential research earned him the prestigious 2014 Turing Award. Stonebraker's professional journey spans two major phases: initially at the University of California, Berkeley, focusing on relational database management systems like Ingres and Postgres, and later, from 2001 at the Massachusetts Institute of Technology (MIT), where he pioneered advanced data management techniques including C-Store, H-Store, SciDB, and DBOS. He remains a professor emeritus at UC Berkeley and continues to influence as an adjunct professor at MIT’s Computer Science and Artificial Intelligence Laboratory. Stonebraker is also recognized for his editorial work on the book "Readings in Database Systems." In the episode, Richie and Mike explore the the success of PostgreSQL, the evolution of SQL databases, the shift towards cloud computing and what that means in practice when migrating to the cloud, the impact of disaggregated storage, software and serverless trends, the role of databases in facilitating new data and AI trends, DBOS and it’s advantages for security, and much more.  Links Mentioned in the Show: DBOSPaper: What Goes Around Comes Around[Course] Understanding Cloud ComputingRelated Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of AlteryxRewatch sessions from RADAR: The Analytics Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Over the past 199 episodes of DataFramed, we’ve heard from people at the forefront of data and AI, and over the past year we’ve constantly looked ahead to the future AI might bring. But all of the technologies and ways of working we’ve witnessed have been built on foundations that were laid decades ago. For our 200th episode, we’re bringing you a special guest and taking a walk down memory lane—to the creation and development of one of the most popular programming languages in the world. Don Chamberlin is renowned as the co-inventor of SQL (Structured Query Language), the predominant database language globally, which he developed with Raymond Boyce in the mid-1970s. Chamberlin's professional career began at IBM Research in Yorktown Heights, New York, following a summer internship there during his academic years. His work on IBM's System R project led to the first SQL implementation and significantly advanced IBM’s relational database technology. His contributions were recognized when he was made an IBM Fellow in 2003 and later a Fellow of the Computer History Museum in 2009 for his pioneering work on SQL and database architectures. Chamberlin also contributed to the development of XQuery, an XML query language, as part of the W3C, which became a W3C Recommendation in January 2007. Additionally, he holds fellowships with ACM and IEEE and is a member of the National Academy of Engineering. In the episode, Richie and Don explore his early career at IBM and the development of his interest in databases alongside Ray Boyce, the database task group (DBTG), the transition to relational databases and the early development of SQL, the commercialization and adoption of SQL, how it became standardized, how it evolved and spread via open source, the future of SQL through NoSQL and SQL++ and much more.  Links Mentioned in the Show: The first-ever journal paper on SQL. SEQUEL: A Structured English Query LanguageDon’s Book: SQL++ for SQL Users: A TutorialSystem R: Relational approach to database managementSQL CoursesSQL Articles, Tutorials and Code-AlongsRelated Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of AlteryxRewatch sessions from RADAR: The Analytics Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Bob Muglia likely needs no introduction. The former CEO of Snowflake led the company during its early, transformational years after a long career at Microsoft and Juniper.  Bob recently released the book The Datapreneurs about the arc of innovation in the data industry, starting with the first relational databases all the way to the present craze of LLMs and beyond. In this conversation with Tristan and Julia, Bob shares insights into the future of data engineering and its potential business impact while offering a glimpse into his professional journey.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Mike Stonebraker is a veritable database pioneer and a Turing Award recipient. In addition to teaching at MIT, he is a serial entrepreneur and co-creator of Postgres. Andy Palmer is a veteran business leader who serves as the CEO of Tamr, a company he co-founded with Mike. Through his seed fund Koa Labs, Andy has helped found and/or fund numerous innovative companies in diverse sectors, including health care, technology, and the life sciences.  In this conversation with Tristan and Julia, Mike and Andy take us through the evolution of database technology over 5+ decades. They share unique insights into relational databases, the switch from row-based to columnar databases, and some of the patterns of database adoption they see repeated over time. For full show notes and to read 7+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Summary Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines. SnowflakeDB has been leading the charge to take advantage of cloud services that simplify the separation of compute and storage. In this episode Kent Graziano, chief technical evangelist for SnowflakeDB, explains how it is differentiated from other managed platforms and traditional data warehouse engines, the features that allow you to scale your usage dynamically, and how it allows for a shift in your workflow from ETL to ELT. If you are evaluating your options for building or migrating a data platform, then this is definitely worth a listen.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media and the Python Software Foundation. Upcoming events include the Software Architecture Conference in NYC and PyCOn US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Kent Graziano about SnowflakeDB, the cloud-native data warehouse

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what SnowflakeDB is for anyone who isn’t familiar with it?

How does it compare to the other available platforms for data warehousing? How does it differ from traditional data warehouses?

How does the performance and flexibility affect the data modeling requirements?

Snowflake is one of the data stores that is enabling the shift from an ETL to an ELT workflow. What are the features that allow for that approach and what are some of the challenges that it introduces? Can you describe how the platform is architected and some of the ways that it has evolved as it has grown in popularity?

What are some of the current limitations that you are struggling with?

For someone getting started with Snowflake what is involved with loading data into the platform?

What is their workflow for allocating and scaling compute capacity and running anlyses?

One of the interesting features enabled by your architecture is data sharing. What are some of the most interesting or unexpected uses of that capability that you have seen? What are some other features or use cases for Snowflake that are not as well known or publicized which you think users should know about? When is SnowflakeDB the wrong choice? What are some of the plans for the future of SnowflakeDB?

Contact Info

LinkedIn Website @KentGraziano on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

SnowflakeDB

Free Trial Stack Overflow

Data Warehouse Oracle DB MPP == Massively Parallel Processing Shared Nothing Architecture Multi-Cluster Shared Data Architecture Google BigQuery AWS Redshift AWS Redshift Spectrum Presto

Podcast Episode

SnowflakeDB Semi-Structured Data Types Hive ACID == Atomicity, Consistency, Isolation, Durability 3rd Normal Form Data Vault Modeling Dimensional Modeling JSON AVRO Parquet SnowflakeDB Virtual Warehouses CRM == Customer Relationship Management Master Data Management

Podcast Episode

FoundationDB

Podcast Episode

Apache Spark

Podcast Episode

SSIS == SQL Server Integration Services Talend Informatica Fivetran

Podcast Episode

Matillion Apache Kafka Snowpipe Snowflake Data Exchange OLTP == Online Transaction Processing GeoJSON Snowflake Documentation SnowAlert Splunk Data Catalog

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

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Summary One of the biggest challenges for any business trying to grow and reach customers globally is how to scale their data storage. FaunaDB is a cloud native database built by the engineers behind Twitter’s infrastructure and designed to serve the needs of modern systems. Evan Weaver is the co-founder and CEO of Fauna and in this episode he explains the unique capabilities of Fauna, compares the consensus and transaction algorithm to that used in other NewSQL systems, and describes the ways that it allows for new application design patterns. One of the unique aspects of Fauna that is worth drawing attention to is the first class support for temporality that simplifies querying of historical states of the data. It is definitely worth a good look for anyone building a platform that needs a simple to manage data layer that will scale with your business.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Evan Weaver about FaunaDB, a modern operational data platform built for your cloud

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what FaunaDB is and how it got started? What are some of the main use cases that FaunaDB is targeting?

How does it compare to some of the other global scale databases that have been built in recent years such as CockroachDB?

Can you describe the architecture of FaunaDB and how it has evolved? The consensus and replication protocol in Fauna is intriguing. Can you talk through how it works?

What are some of the edge cases that users should be aware of? How are conflicts managed in Fauna?

What is the underlying storage layer?

How is the query layer designed to allow for different query patterns and model representations?

How does data modeling in Fauna compare to that of relational or document databases?

Can you describe the query format? What are some of the common difficulties or points of confusion around interacting with data in Fauna?

What are some application design patterns that are enabled by using Fauna as the storage layer? Given the ability to replicate globally, how do you mitigate latency when interacting with the database? What are some of the most interesting or unexpected ways that you have seen Fauna used? When is it the wrong choice? What have been some of the most interesting/unexpected/challenging aspects of building the Fauna database and company? What do you have in store for the future of Fauna?

Contact Info

@evan on Twitter LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Fauna Ruby on Rails CNET GitHub Twitter NoSQL Cassandra InnoDB Redis Memcached Timeseries Spanner Paper DynamoDB Paper Percolator ACID Calvin Protocol Daniel Abadi LINQ LSM Tree (Log-structured Merge-tree) Scala Change Data Capture GraphQL

Podcast.init Interview About Graphene

Fauna Query Language (FQL) CQL == Cassandra Query Language Object-Relational Databases LDAP == Lightweight Directory Access Protocol Auth0 OLAP == Online Analytical Processing Jepsen distributed systems safety research

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

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Summary

With the increased ease of gaining access to servers in data centers across the world has come the need for supporting globally distributed data storage. With the first wave of cloud era databases the ability to replicate information geographically came at the expense of transactions and familiar query languages. To address these shortcomings the engineers at Cockroach Labs have built a globally distributed SQL database with full ACID semantics in Cockroach DB. In this episode Peter Mattis, the co-founder and VP of Engineering at Cockroach Labs, describes the architecture that underlies the database, the challenges they have faced along the way, and the ways that you can use it in your own environments today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Peter Mattis about CockroachDB, the SQL database for global cloud services

Interview

Introduction How did you get involved in the area of data management? What was the motivation for creating CockroachDB and building a business around it? Can you describe the architecture of CockroachDB and how it supports distributed ACID transactions?

What are some of the tradeoffs that are necessary to allow for georeplicated data with distributed transactions? What are some of the problems that you have had to work around in the RAFT protocol to provide reliable operation of the clustering mechanism?

Go is an unconventional language for building a database. What are the pros and cons of that choice? What are some of the common points of confusion that users of CockroachDB have when operating or interacting with it?

What are the edge cases and failure modes that users should be aware of?

I know that your SQL syntax is PostGreSQL compatible, so is it possible to use existing ORMs unmodified with CockroachDB?

What are some examples of extensions that are specific to CockroachDB?

What are some of the most interesting uses of CockroachDB that you have seen? When is CockroachDB the wrong choice? What do you have planned for the future of CockroachDB?

Contact Info

Peter

LinkedIn petermattis on GitHub @petermattis on Twitter

Cockroach Labs

@CockroackDB on Twitter Website cockroachdb on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

CockroachDB Cockroach Labs SQL Google Bigtable Spanner NoSQL RDBMS (Relational Database Management System) “Big Iron” (colloquial term for mainframe computers) RAFT Consensus Algorithm Consensus MVCC (Multiversion Concurrency Control) Isolation Etcd GDPR Golang C++ Garbage Collection Metaprogramming Rust Static Linking Docker Kubernetes CAP Theorem PostGreSQL ORM (Object Relational Mapping) Information Schema PG Catalog Interleaved Tables Vertica Spark Change Data Capture

The intro and outro music is from The Hug by The Freak Fandan

Summary

Building an ETL pipeline is a common need across businesses and industries. It’s easy to get one started but difficult to manage as new requirements are added and greater scalability becomes necessary. Rather than duplicating the efforts of other engineers it might be best to use a hosted service to handle the plumbing so that you can focus on the parts that actually matter for your business. In this episode CTO and co-founder of Alooma, Yair Weinberger, explains how the platform addresses the common needs of data collection, manipulation, and storage while allowing for flexible processing. He describes the motivation for starting the company, how their infrastructure is architected, and the challenges of supporting multi-tenancy and a wide variety of integrations.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Yair Weinberger about Alooma, a company providing data pipelines as a service

Interview

Introduction How did you get involved in the area of data management? What is Alooma and what is the origin story? How is the Alooma platform architected?

I want to go into stream VS batch here What are the most challenging components to scale?

How do you manage the underlying infrastructure to support your SLA of 5 nines? What are some of the complexities introduced by processing data from multiple customers with various compliance requirements?

How do you sandbox user’s processing code to avoid security exploits?

What are some of the potential pitfalls for automatic schema management in the target database? Given the large number of integrations, how do you maintain the

What are some challenges when creating integrations, isn’t it simply conforming with an external API?

For someone getting started with Alooma what does the workflow look like? What are some of the most challenging aspects of building and maintaining Alooma? What are your plans for the future of Alooma?

Contact Info

LinkedIn @yairwein on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Alooma Convert Media Data Integration ESB (Enterprise Service Bus) Tibco Mulesoft ETL (Extract, Transform, Load) Informatica Microsoft SSIS OLAP Cube S3 Azure Cloud Storage Snowflake DB Redshift BigQuery Salesforce Hubspot Zendesk Spark The Log: What every software engineer should know about real-time data’s unifying abstraction by Jay Kreps RDBMS (Relational Database Management System) SaaS (Software as a Service) Change Data Capture Kafka Storm Google Cloud PubSub Amazon Kinesis Alooma Code Engine Zookeeper Idempotence Kafka Streams Kubernetes SOC2 Jython Docker Python Javascript Ruby Scala PII (Personally Identifiable Information) GDPR (General Data Protection Regulation) Amazon EMR (Elastic Map Reduce) Sequoia Capital Lightspeed Investors Redis Aerospike Cassandra MongoDB

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

In this podcast, Wayne Eckerson and Joe Caserta discuss data migration, compare cloud offerings from Amazon, Google, and Microsoft, and define and explain artificial intelligence.

You can contact Caserta by visiting caserta.com or by sending him an email to [email protected]. Follow him on Twitter @joe_caserta.

Caserta is President of a New York City-based consulting firm he founded in 2001 and a longtime data guy. In 2004, Joe teamed up with data warehousing legend, Ralph Kimball to write to write the book The Data Warehouse ETL Toolkit. Today he’s now one of the leading authorities on big data implementations. This makes Joe one of the few individuals with in-the-trenches experience on both sides of the data divide, traditional data warehousing on relational databases and big data implementations on Hadoop and the cloud.

In this podcast, Wayne Eckerson and James Serra discuss myths of modern data management. Some of the myths discussed include 'all you need is a data lake', 'the data warehouse is dead', 'we don’t need OLAP cubes anymore', 'cloud is too expensive and latency is too slow', 'you should always use a NoSQL product over a RDBMS.'

Serra is big data and data warehousing solutions architect at Microsoft with over thirty years of IT experience. He is a popular blogger and speaker and has presented at dozens of Microsoft PASS and other events. Prior to Microsoft, Serra was an independent data warehousing and business intelligence architect and developer.

In this podcast, Wayne Eckerson and Joe Caserta discuss what constitutes a modern data platform. Caserta is President of a New York City-based consulting firm he founded in 2001 and a longtime data guy. In 2004, Joe teamed up with data warehousing legend, Ralph Kimball to write to write the book The Data Warehouse ETL Toolkit. Today he’s now one of the leading authorities on big data implementations. This makes Joe one of the few individuals with in-the-trenches experience on both sides of the data divide, traditional data warehousing on relational databases and big data implementations on Hadoop and the cloud. His perspectives are always insightful.