Summary

Databases are the core of most applications, whether transactional or analytical. In recent years the selection of database products has exploded, making the critical decision of which engine(s) to use even more difficult. In this episode Tanya Bragin shares her experiences as a product manager for two major vendors and the lessons that she has learned about how teams should approach the process of tool selection.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro. That’s three free boards at dataengineeringpodcast.com/miro. Your host is Tobias Macey and today I'm interviewing Tanya Bragin about her views on the database products market

Interview

Introduction How did you get involved in the area of data management? What are the aspects of the database market that keep you interested as a VP of product?

How have your experiences at Elastic informed your current work at Clickhouse?

What are the main product categories for databases today?

What are the industry trends that have the most impact on the development and growth of different product categories? Which categories do you see growing the fastest?

When a team is selecting a database technology for a given task, what are the types of questions that they should be asking? Transactional engines like Postgres, SQL Server, Oracle, etc. were long used

It’s 2023, why are software engineers still breaking analytics reporting? We’ve all been there, being alerted by an analyst or C-level stakeholders, saying “this report is broken”, only to spend hours determining that an engineer deleted a column on the source database that is now breaking your pipeline and reporting.

At Xometry, the data engineering team wanted to fix this problem at its root and give the engineering teams a clear and repeatable process that allowed them to be the owners of their own database data. Xometry named the process DPICT (data pipeline contract) and built several internal tools that integrated seamlessly with their developer’s microservice toolsets.

Their software engineers mostly build their database microservices using Postgres, and bring in the data using Fivetran. Using that as the baseline, the team created a set of tools that would allow the engineers to quickly build the staging layer of their database in the data warehouse (Snowflake), but also alert them of the consequences of removing a table or column in downstream reporting.

In this talk, Jisan shares the nuts and bolts of the designed solution and process that allowed the team to onboard 13 different microservices seamlessly, working with multiple domains and dozens of developers. The process also helped software engineers to own their own data and realize their impact. The team has saved hundreds hours of data engineering time and resources not having to chase down what changed upstream to break data. Overall, this process has helped to bring transparency to the whole data ecosystem.

Speaker: Jisan Zaman, Data Engineering Manager, Xometry

Register for Coalesce at https://coalesce.getdbt.com

Summary

Building streaming applications has gotten substantially easier over the past several years. Despite this, it is still operationally challenging to deploy and maintain your own stream processing infrastructure. Decodable was built with a mission of eliminating all of the painful aspects of developing and deploying stream processing systems for engineering teams. In this episode Eric Sammer discusses why more companies are including real-time capabilities in their products and the ways that Decodable makes it faster and easier.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES. Your host is Tobias Macey and today I'm interviewing Eric Sammer about starting your stream processing journey with Decodable

Interview

Introduction How did you get involved in the area of data management? Can you describe what Decodable is and the story behind it?

What are the notable changes to the Decodable platform since we last spoke? (October 2021) What are the industry shifts that have influenced the product direction?

What are the problems that customers are trying to solve when they come to Decodable? When you launched your focus was on SQL transformations of streaming data. What was the process for adding full Java support in addition to SQL? What are the developer experience challenges that are particular to working with streaming data?

How have you worked to address that in the Decodable platform and interfaces?

As you evolve the technical and product direction, what is your heuristic for balancing the unification of interfaces and system integration against the ability to swap different components or interfaces as new technologies are introduced? What are the most interesting, innovative, or unexpected ways that you have seen Decodable used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Decodable? When is Decodable the wrong choice? What do you have planned for the future of Decodable?

Contact Info

esammer on GitHub LinkedIn

Parting 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 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-workers

Links

Decodable

Podcast Episode

Understanding the Apache Flink Journey Flink

Podcast Episode

Debezium

Podcast Episode

Kafka Redpanda

Podcast Episode

Kinesis PostgreSQL

Podcast Episode

Snowflake

Podcast Episode

Databricks Startree Pinot

Podcast Episode

Rockset

Podcast Episode

Druid InfluxDB Samza Storm Pulsar

Podcast Episode

ksqlDB

Podcast Episode

dbt GitHub Actions Airbyte Singer Splunk Outbox Pattern

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Neo4J:

NODES 2023 is a free online conference focused on graph-driven innovations with content for all skill levels. Its 24 hours are packed with 90 interactive technical sessions from top developers and data scientists across the world covering a broad range of topics and use cases. The event tracks: - Intelligent Applications: APIs, Libraries, and Frameworks – Tools and best practices for creating graph-powered applications and APIs with any software stack and programming language, including Java, Python, and JavaScript - Machine Learning and AI – How graph technology provides context for your data and enhances the accuracy of your AI and ML projects (e.g.: graph neural networks, responsible AI) - Visualization: Tools, Techniques, and Best Practices – Techniques and tools for exploring hidden and unknown patterns in your data and presenting complex relationships (knowledge graphs, ethical data practices, and data representation)

Don’t miss your chance to hear about the latest graph-powered implementations and best practices for free on October 26 at NODES 2023. Go to Neo4j.com/NODES today to see the full agenda and register!Rudderstack:

Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstackMaterialize:

You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.

That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI. Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research, Materialize is trusted by data and engineering teams at Ramp, Pluralsight, Onward and more to build real-time data products without the cost, complexity, and development time of stream processing.

Go to materialize.com today and get 2 weeks free!Datafold:

This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare…

We talked about:

Angela's background Angela's role at Sam's Club The usefulness of knowing ML as a data engineer Angela's career path Transitioning from data analyst to data engineer/system designer Best practices for system design and data engineering Working with document databases Working with network-based databases Detecting fraud with a network-based database Selecting the database type to work with Neo4j vs Postgres The importance of having software engineering knowledge in data engineering Data quality check tooling The greatest challenges in data engineering Debugging and finding the root cause of a failed job What kinds of tools Angela uses on a daily basis Working with external data sources Angela's resource recommendations

Links:

LinkedIn: https://www.linkedin.com/in/aramirez1305/ Twitter: https://twitter.com/angelamaria__r Github: https://github.com/aramir62 Previous podcast talk: https://twitter.com/i/spaces/1OwGWwZAZDnGQ?s=20

Free ML Engineering course: http://mlzoomcamp.com

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

Summary

Data persistence is one of the most challenging aspects of computer systems. In the era of the cloud most developers rely on hosted services to manage their databases, but what if you are a cloud service? In this episode Vignesh Ravichandran explains how his team at Cloudflare provides PostgreSQL as a service to their developers for low latency and high uptime services at global scale. This is an interesting and insightful look at pragmatic engineering for reliability and scale.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Vignesh Ravichandran about building an internal database as a service platform at Cloudflare

Interview

Introduction How did you get involved in the area of data management? Can you start by describing the different database workloads that you have at Cloudflare?

What are the different methods that you have used for managing database instances?

What are the requirements and constraints that you had to account for in designing your current system? Why Postgres? optimizations for Postgres

simplification from not supporting multiple engines

limitations in postgres that make multi-tenancy challenging scale of operation (data volume, request rate What are the most interesting, innovative, or unexpected ways that you have seen your DBaaS used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on your internal database platform? When is an internal database as a service the wrong choice? What do you have planned for the future of Postgres hosting at Cloudflare?

Contact Info

LinkedIn Website

Parting 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 Mac

Learn the basics of writing SQL scripts. Using Standard SQL as the starting point, this book teaches writing SQL in various popular dialects, including PostgreSQL, MySQL/MariaDB, Microsoft SQL Server, Oracle, and SQLite. The book starts with a general introduction to writing SQL and covers the basic concepts. Author Mark Simon then covers database principles, and how database tables are designed. He teaches you how to filter data using the WHERE clause, and you will work with NULL, numbers, dates, and strings. You will also understand sorting results using the ORDER BY clause, sorting by calculated columns, and limiting the number of results. By the end of the book, you will know how to insert and update data, and summarize data with aggregate functions and groups. Three appendices cover differences between SQL dialects, working with tables, and a crash course in PDO. What You Will Learn Filter, sort, andcalculate data Summarize data with aggregate functions Modify data with insert, update, and delete statements Study design principles in developing a database Who This Book Is For Developers and analysts working with SQL, as well as web developers who want a stronger understanding of working with databases

ABOUT THE TALK: In this talk, we'll dive into one of the newest Apache Arrow subprojects, Arrow Database Connectivity (ADBC), an API specification for Arrow-based database access.

Over the course of this session, you’ll get a crash course in ADBC and learn how it communicates with different data APIs (like Arrow Flight SQL and Postgres) using Arrow-native in-memory data. By the end, you’ll understand the use cases it can conquer and know where to access the resources you need to get started.

This talk will cover goals, use-cases, and examples of using ADBC to communicate with different Data APIs (such as Flight SQL or postgres) with Arrow Native in-memory data.

ABOUT THE SPEAKER: Matthew Topol is a committer for the Apache Arrow project, frequently enhancing the Golang Arrow and Parquet libraries among other enhancements and helping to grow the Arrow Community. Recently, Matt has joined Voltron Data in order to work on the Apache Arrow libraries full time and grow the Arrow Golang community. In June 2022, Matt's first book was published, which is the first (and currently only) book on Apache Arrow titled "In-Memory Analytics with Apache Arrow".

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

Summary

Data is a team sport, but it's often difficult for everyone on the team to participate. For a long time the mantra of data tools has been "by developers, for developers", which automatically excludes a large portion of the business members who play a crucial role in the success of any data project. Quilt Data was created as an answer to make it easier for everyone to contribute to the data being used by an organization and collaborate on its application. In this episode Aneesh Karve shares the journey that Quilt has taken to provide an approachable interface for working with versioned data in S3 that empowers everyone to collaborate.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions! Your host is Tobias Macey and today I'm interviewing Aneesh Karve about how Quilt Data helps you bring order to your chaotic data in S3 with transactional versioning and data discovery built in

Interview

Introduction How did you get involved in the area of data management? Can you describe what Quilt is and the story behind it?

How have the goals and features of the Quilt platform changed since I spoke with Kevin in June of 2018?

What are the main problems that users are trying to solve when they find Quilt?

What are some of the alternative approaches/products that they are coming from?

How does Quilt compare with options such as LakeFS, Unstruk, Pachyderm, etc.? Can you describe how Quilt is implemented? What are the types of tools and systems that Quilt gets integrated with?

How do you manage the tension between supporting the lowest common denominator, while providing options for more advanced capabilities?

What is a typical workflow for a team that is using Quilt to manage their data? What are the most interesting, innovative, or unexpected ways that you have seen Quilt used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Quilt? When is Quilt the wrong choice? What do you have planned for the future of Quilt?

Contact Info

LinkedIn @akarve on Twitter

Parting 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 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-workers

Links

Quilt Data

Podcast Episode

UW Madison Docker Swarm Kaggle open.quiltdata.com FinOS Perspective LakeFS

Podcast Episode

Pachyderm

Podcast Episode

Unstruk

Podcast Episode

Parquet Avro ORC Cloudformation Troposphere CDK == Cloud Development Kit Shadow IT

Podcast Episode

Delta Lake

Podcast Episode

Apache Iceberg

Podcast Episode

Datasette Frictionless DVC

Podcast.init Episode

The in

Summary

This podcast started almost exactly six years ago, and the technology landscape was much different than it is now. In that time there have been a number of generational shifts in how data engineering is done. In this episode I reflect on some of the major themes and take a brief look forward at some of the upcoming changes.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Your host is Tobias Macey and today I'm reflecting on the major trends in data engineering over the past 6 years

Interview

Introduction 6 years of running the Data Engineering Podcast Around the first time that data engineering was discussed as a role

Followed on from hype about "data science"

Hadoop era Streaming Lambda and Kappa architectures

Not really referenced anymore

"Big Data" era of capture everything has shifted to focusing on data that presents value

Regulatory environment increases risk, better tools introduce more capability to understand what data is useful

Data catalogs

Amundsen and Alation

Orchestration engine

Oozie, etc. -> Airflow and Luigi -> Dagster, Prefect, Lyft, etc. Orchestration is now a part of most vertical tools

Cloud data warehouses Data lakes DataOps and MLOps Data quality to data observability Metadata for everything

Data catalog -> data discovery -> active metadata

Business intelligence

Read only reports to metric/semantic layers Embedded analytics and data APIs

Rise of ELT

dbt Corresponding introduction of reverse ETL

What are the most interesting, unexpected, or challenging lessons that you have learned while working on running the podcast? What do you have planned for the future of the podcast?

Parting 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 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-workers

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

Looking for the simplest way to get the freshest data possible to your teams? Because let's face it: if real-time were easy, everyone would be using it. Look no further than Materialize, the streaming database you already know how to use.

Materialize’s PostgreSQL-compatible interface lets users leverage the tools they already use, with unsurpassed simplicity enabled by full ANSI SQL support. Delivered as a single platform with the separation of storage and compute, strict-serializability, active replication, horizontal scalability and workload isolation — Materialize is now the fastest way to build products with streaming data, drastically reducing the time, expertise, cost and maintenance traditionally associated with implementation of real-time features.

Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses.

Go to materialize.comSupport Data Engineering Podcast

Mastering PostgreSQL 15 provides a deep dive into advanced topics of PostgreSQL database systems, covering installation, configuration, optimization, and administration. By reading this book, you will gain the skills to manage, secure, and optimize PostgreSQL database instances for complex applications. What this Book will help me do Understand and utilize advanced indexing techniques in PostgreSQL to improve query performance. Perform efficient database backup and recovery processes to ensure data safety and integrity. Master replication and failover methods to achieve high availability and data consistency. Implement authentication and encryption to enhance database server security. Troubleshoot PostgreSQL to identify and resolve common operational issues. Author(s) Hans-Jürgen Schönig, the author of Mastering PostgreSQL 15, is a leading expert in PostgreSQL with decades of experience in the field. His passion for databases has driven him to deliver solutions in high-performance database management and large-scale data systems. With a thorough and approachable style, Hans-Jürgen imparts complex technical knowledge effectively to his readers. Who is it for? This book is ideal for experienced PostgreSQL developers and database administrators looking to master PostgreSQL 15. Readers should have a foundational understanding of database systems and are seeking to implement advanced features and best practices in enterprise environments. It's tailored for those aspiring to enhance their operational and architectural skills in managing PostgreSQL systems.

Summary

Business intelligence has gone through many generational shifts, but each generation has largely maintained the same workflow. Data analysts create reports that are used by the business to understand and direct the business, but the process is very labor and time intensive. The team at Omni have taken a new approach by automatically building models based on the queries that are executed. In this episode Chris Merrick shares how they manage integration and automation around the modeling layer and how it improves the organizational experience of business intelligence.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions! Your host is Tobias Macey and today I'm interviewing Chris Merrick about the Omni Analytics platform and how they are adding automatic data modeling to your business intelligence

Interview

Introduction How did you get involved in the area of data management? Can you describe what Omni Analytics is and the story behind it?

What are the core goals that you are trying to achieve with building Omni?

Business intelligence has gone through many evolutions. What are the unique capabilities that Omni Analytics offers over other players in the market?

What are the technical and organizational anti-patterns that typically grow up around BI systems?

What are the elements that contribute to BI being such a difficult product to use effectively in an organization?

Can you describe how you have implemented the Omni platform?

How have the design/scope/goals of the product changed since you first started working on it?

What does the workflow for a team using Omni look like?

What are some of the developments in the broader ecosystem that have made your work possible?

What are some of the positive and negative inspirations that you have drawn from the experience that you and your team-mates have gained in previous businesses?

What are the most interesting, innovative, or unexpected ways that you have seen Omni used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Omni?

When is Omni the wrong choice?

What do you have planned for the future of Omni?

Contact Info

LinkedIn @cmerrick on Twitter

Parting 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 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-workers

Links

Omni Analytics Stitch RJ Metrics Looker

Podcast Episode

Singer dbt

Podcast Episode

Teradata Fivetran Apache Arrow

Podcast Episode

DuckDB

Podcast Episode

BigQuery Snowflake

Podcast Episode

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

Looking for the simplest way to get the freshest data possible to your teams? Because let's face it: if real-time were easy, everyone would be using it. Look no further than Materialize, the streaming database you already know how to use.

Materialize’s PostgreSQL-compatible interface lets users leverage the tools they already use, with unsurpassed simplicity enabled by full ANSI SQL support. Delivered as a single platform with the separation of storage and compute, strict-serializability, active replication, horizontal scalability and workload isolation — Materialize is now the fastest way to build products with streaming data, drastically reducing the time, expertise, cost and maintenance traditionally associated with implementation of real-time features.

Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses.

Go to materialize.comSupport Data Engineering Podcast

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

The most interesting and challenging bugs always happen in production, but recreating them is a constant challenge due to differences in the data that you are working with. Building your own scripts to replicate data from production is time consuming and error-prone. Tonic is a platform designed to solve the problem of having reliable, production-like data available for developing and testing your software, analytics, and machine learning projects. In this episode Adam Kamor explores the factors that make this such a complex problem to solve, the approach that he and his team have taken to turn it into a reliable product, and how you can start using it to replace your own collection of scripts.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions! Data and analytics leaders, 2023 is your year to sharpen your leadership skills, refine your strategies and lead with purpose. Join your peers at Gartner Data & Analytics Summit, March 20 – 22 in Orlando, FL for 3 days of expert guidance, peer networking and collaboration. Listeners can save $375 off standard rates with code GARTNERDA. Go to dataengineeringpodcast.com/gartnerda today to find out more. Your host is Tobias Macey and today I'm interviewing Adam Kamor about Tonic, a service for generating data sets that are safe for development, analytics, and machine learning

Interview

Introduction How did you get involved in the area of data management? Can you describe what Tonic is and the story behind it? What are the core problems that you are trying to solve? What are some of the ways that fake or obfuscated data is used in development and analytics workflows? challenges of reliably subsetting data

impact of ORMs and bad habits developers get into with database modeling

Can you describe how Tonic is implemented?

What are the units of composition that you are building to allow for evolution and expansion of your product? How have the design and goals of the platform evolved since you started working on it?

Can you describe some of the different workflows that customers build on top of your various tools What are the most interesting, innovative, or unexpected ways that you have seen Tonic used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Tonic? When is Tonic the wrong choice? What do you have planned for the future of Tonic?

Contact Info

LinkedIn @AdamKamor on Twitter

Parting 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 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-workers

Links

Tonic

Djinn

Django

Summary

The modern data stack has made it more economical to use enterprise grade technologies to power analytics at organizations of every scale. Unfortunately it has also introduced new overhead to manage the full experience as a single workflow. At the Modern Data Company they created the DataOS platform as a means of driving your full analytics lifecycle through code, while providing automatic knowledge graphs and data discovery. In this episode Srujan Akula explains how the system is implemented and how you can start using it today with your existing data systems.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more. Data and analytics leaders, 2023 is your year to sharpen your leadership skills, refine your strategies and lead with purpose. Join your peers at Gartner Data & Analytics Summit, March 20 – 22 in Orlando, FL for 3 days of expert guidance, peer networking and collaboration. Listeners can save $375 off standard rates with code GARTNERDA. Go to dataengineeringpodcast.com/gartnerda today to find out more. Your host is Tobias Macey and today I'm interviewing Srujan Akula about DataOS, a pre-integrated and managed data platform built by The Modern Data Company

Interview

Introduction How did you get involved in the area of data management? Can you describe what your mission at The Modern Data Company is and the story behind it? Your flagship (only?) product is a platform that you're calling DataOS. What is the scope and goal of that platform?

Who is the target audience?

On your site you refer to the idea of "data as software". What are the principles and ways of thinking that are encompassed by that concept?

What are the platform capabilities that are required to make it possible?

There are 11 "Key Features" listed on your site for the DataOS. What was your process for identifying the "must have" vs "nice to have" features for launching the platform? Can you describe the technical architecture that powers your DataOS product?

What are the core principles that you are optimizing for in the design of your platform? How have the design and goals of the system changed or evolved since you started working on DataOS?

Can you describe the workflow for the different practitioners and stakeholders working on an installation of DataOS? What are the interfaces and escape hatches that are available for integrating with and ext

Data is at the core of every business, but it is useless if nobody can access and analyze it. Learn how to generate business value by making your data accessible with advanced table UIs. This definitive guide teaches you how to bring your data to the fingertips of nontechnical users with advanced features like pagination, sorting, filtering, and infinity scrolling. Build reactive and reuseable table components by leveraging Phoenix LiveView, schemaless changesets, and Ecto query composition. Table UIs are the bread and butter for every web developer, so it is time to learn how to build them right. As a web developer, you have to build tables. Lots and lots of tables. With table UIs making up such a significant part of your daily work, you need to know how to build the right table for the task, with all the needed features. Building a simple table is easy, but tables only become really useful with advanced features like pagination, sorting, and filtering. That;s where building a table can quickly become complicated. This book shows you how to implement advanced table features in a clean and reusable way. You'll build fast and interactive table UIs by leveraging Phoenix LiveView. Make vast amounts of data manageable with common but complex features like pagination, sorting, filtering, and inifinity scrolling. Use SOLID coding principles to make your queries reusable with query composition. Compartmentalize your UI with LiveComponents and learn how to handle user input securely with schemaless changesets. Share your view onto the data painlessly by storing your search parameters in the URL. Data is one of the most valuable assets of your business, but you cannot unlock its potential if you don't know how to make it accessible. This book shows you how to deliver that data to your users' fingertips quickly. What You Need: You'll need Elixir 1.12 or later, Erlang/OTP 24 or later, Phoenix 1.6 or later, and PostgreSQL installed on your machine.

Summary

Managing end-to-end data flows becomes complex and unwieldy as the scale of data and its variety of applications in an organization grows. Part of this complexity is due to the transformation and orchestration of data living in disparate systems. The team at Upsolver is taking aim at this problem with the latest iteration of their platform in the form of SQLake. In this episode Ori Rafael explains how they are automating the creation and scheduling of orchestration flows and their related transforations in a unified SQL interface.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Data and analytics leaders, 2023 is your year to sharpen your leadership skills, refine your strategies and lead with purpose. Join your peers at Gartner Data & Analytics Summit, March 20 – 22 in Orlando, FL for 3 days of expert guidance, peer networking and collaboration. Listeners can save $375 off standard rates with code GARTNERDA. Go to dataengineeringpodcast.com/gartnerda today to find out more. Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more. Your host is Tobias Macey and today I'm interviewing Ori Rafael about the SQLake feature for the Upsolver platform that automatically generates pipelines from your queries

Interview

Introduction How did you get involved in the area of data management? Can you describe what the SQLake product is and the story behind it?

What is the core problem that you are trying to solve?

What are some of the anti-patterns that you have seen teams adopt when designing and implementing DAGs in a tool such as Airlow? What are the benefits of merging the logic for transformation and orchestration into the same interface and dialect (SQL)? Can you describe the technical implementation of the SQLake feature? What does the workflow look like for designing and deploying pipelines in SQLake? What are the opportunities for using utilities such as dbt for managing logical complexity as the number of pipelines scales?

SQL has traditionally been challenging to compose. How did that factor into your design process for how to structure the dialect extensions for job scheduling?

What are some of the complexities that you have had to address in your orchestration system to be able to manage timeliness of operations as volume and complexity of the data scales? What are some of the edge cases that you have had to provide escape hatches for? What are the most interesting, innova

Summary

Making effective use of data requires proper context around the information that is being used. As the size and complexity of your organization increases the difficulty of ensuring that everyone has the necessary knowledge about how to get their work done scales exponentially. Wikis and intranets are a common way to attempt to solve this problem, but they are frequently ineffective. Rehgan Avon co-founded AlignAI to help address this challenge through a more purposeful platform designed to collect and distribute the knowledge of how and why data is used in a business. In this episode she shares the strategic and tactical elements of how to make more effective use of the technical and organizational resources that are available to you for getting work done with data.

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 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! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan's active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more. Your host is Tobias Macey and today I'm interviewing Rehgan Avon about her work at AlignAI to help organizations standardize their technical and procedural approaches to working with data

Interview

Introduction How did you get involved in the area of data management? Can you describe what AlignAI is and the story behind it? What are the core problems that you are focused on addressing?

What are the tactical ways that you are working to solve those problems?

What are some of the common and avoidable ways that analytics/AI projects go wrong?

What are some of the ways that organizational scale and complexity impacts their ability to execute on data and AI projects?

What are the ways that incomplete/unevenly distributed knowledge manifests in project design and execution? Can you describe the design and implementation of the AlignAI platform?

How have the goals and implementation of the product changed since you

Summary

With all of the messaging about treating data as a product it is becoming difficult to know what that even means. Vishal Singh is the head of products at Starburst which means that he has to spend all of his time thinking and talking about the details of product thinking and its application to data. In this episode he shares his thoughts on the strategic and tactical elements of moving your work as a data professional from being task-oriented to being product-oriented and the long term improvements in your productivity that it provides.

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 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! Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. 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 extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder Build Data Pipelines. Not DAGs. That’s the spirit behind Upsolver SQLake, a new self-service data pipeline platform that lets you build batch and streaming pipelines without falling into the black hole of DAG-based orchestration. All you do is write a query in SQL to declare your transformation, and SQLake will turn it into a continuous pipeline that scales to petabytes and delivers up to the minute fresh data. SQLake supports a broad set of transformations, including high-cardinality joins, aggregations, upserts and window operations. Output data can be streamed into a data lake for query engines like Presto, Trino or Spark SQL, a data warehouse like Snowflake or Redshift., or any other destination you choose. Pricing for SQLake is simple. You pay $99 per terabyte ingested into your data lake using SQLake, and run unlimited transformation pipelines for free. That way data engineers and data users can process to their heart’s content without worrying about their cloud bill. For data engineering podcast listeners, we’re offering a 30 day trial with unlimited data, so go to dataengineeringpodcast.com/upsolver today and see for yourself how to avoid DAG hell. Your host is Tobias Macey and today I'm interviewing Vishal Singh about his experience

Summary

Five years of hosting the Data Engineering Podcast has provided Tobias Macey with a wealth of insight into the work of building and operating data systems at a variety of scales and for myriad purposes. In order to condense that acquired knowledge into a format that is useful to everyone Scott Hirleman turns the tables in this episode and asks Tobias about the tactical and strategic aspects of his experiences applying those lessons to the work of building a data platform from scratch.

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 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! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan's active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more. Your host is Tobias Macey and today I'm being interviewed by Scott Hirleman about my work on the podcasts and my experience building a data platform

Interview

Introduction How did you get involved in the area of data management?

Data platform building journey

Why are you building, who are the users/use cases How to focus on doing what matters over cool tools How to build a good UX Anything surprising or did you discover anything you didn't expect at the start How to build so it's modular and can be improved in the future

General build vs buy and vendor selection process

Obviously have a good BS detector - how can others build theirs So many tools, where do you start - capability need, vendor suite offering, etc. Anything surprising in doing much of this at once How do you think about TCO in build versus buy Any advice

Guest call out

Be brave, believe you are good enough to be on the show Look at past episodes and don't pitch the same as what's been on recently And vendors, be smart, work with your customers to come up with a good pitch for them as guests...

Tobias' advice and learnings from building out a data platform:

Advice: when considering a tool, start from what are you act

Summary

Encryption and security are critical elements in data analytics and machine learning applications. We have well developed protocols and practices around data that is at rest and in motion, but security around data in use is still severely lacking. Recognizing this shortcoming and the capabilities that could be unlocked by a robust solution Rishabh Poddar helped to create Opaque Systems as an outgrowth of his PhD studies. In this episode he shares the work that he and his team have done to simplify integration of secure enclaves and trusted computing environments into analytical workflows and how you can start using it without re-engineering your existing systems.

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 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! Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. 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 extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder Build Data Pipelines. Not DAGs. That’s the spirit behind Upsolver SQLake, a new self-service data pipeline platform that lets you build batch and streaming pipelines without falling into the black hole of DAG-based orchestration. All you do is write a query in SQL to declare your transformation, and SQLake will turn it into a continuous pipeline that scales to petabytes and delivers up to the minute fresh data. SQLake supports a broad set of transformations, including high-cardinality joins, aggregations, upserts and window operations. Output data can be streamed into a data lake for query engines like Presto, Trino or Spark SQL, a data warehouse like Snowflake or Redshift., or any other destination you choose. Pricing for SQLake is simple. You pay $99 per terabyte ingested into your data lake using SQLake, and run unlimited transformation pipelines for free. That way data engineers and data users can process to their heart’s content without worrying about their cloud bill. For data engineering podcast listeners, we’re offering a 30 day trial with unlimited data, so go to dataengineeringpodcast.com/upsolver today an