Summary In this episode of the Data Engineering Podcast Derek Collison, creator of NATS and CEO of Synadia, talks about the evolution and capabilities of NATS as a multi-paradigm connectivity layer for distributed applications. Derek discusses the challenges and solutions in building distributed systems, and highlights the unique features of NATS that differentiate it from other messaging systems. He delves into the architectural decisions behind NATS, including its ability to handle high-speed global microservices, support for edge computing, and integration with Jetstream for data persistence, and explores the role of NATS in modern data management and its use cases in industries like manufacturing and connected vehicles.

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

In this podcast episode, we talked with Bartosz Mikulski about Data Intensive AI.

About the Speaker: Bartosz is an AI and data engineer. He specializes in moving AI projects from the good-enough-for-a-demo phase to production by building a testing infrastructure and fixing the issues detected by tests. On top of that, he teaches programmers and non-programmers how to use AI. He contributed one chapter to the book 97 Things Every Data Engineer Should Know, and he was a speaker at several conferences, including Data Natives, Berlin Buzzwords, and Global AI Developer Days. 

In this episode, we discuss Bartosz’s career journey, the importance of testing in data pipelines, and how AI tools like ChatGPT and Cursor are transforming development workflows. From prompt engineering to building Chrome extensions with AI, we dive into practical use cases, tools, and insights for anyone working in data-intensive AI projects. Whether you’re a data engineer, AI enthusiast, or just curious about the future of AI in tech, this episode offers valuable takeaways and real-world experiences.

0:00 Introduction to Bartosz and his background 4:00 Bartosz’s career journey from Java development to AI engineering 9:05 The importance of testing in data engineering 11:19 How to create tests for data pipelines 13:14 Tools and approaches for testing data pipelines 17:10 Choosing Spark for data engineering projects 19:05 The connection between data engineering and AI tools 21:39 Use cases of AI in data engineering and MLOps 25:13 Prompt engineering techniques and best practices 31:45 Prompt compression and caching in AI models 33:35 Thoughts on DeepSeek and open-source AI models 35:54 Using AI for lead classification and LinkedIn automation 41:04 Building Chrome extensions with AI integration 43:51 Comparing Cursor and GitHub Copilot for coding 47:11 Using ChatGPT and Perplexity for AI-assisted tasks 52:09 Hosting static websites and using AI for development 54:27 How blogging helps attract clients and share knowledge 58:15 Using AI to assist with writing and content creation

🔗 CONNECT WITH Bartosz LinkedIn: https://www.linkedin.com/in/mikulskibartosz/ Github: https://github.com/mikulskibartosz Website: https://mikulskibartosz.name/blog/

🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ Check other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

Supported by Our Partners • Sentry — Error and performance monitoring for developers. • The Software Engineer’s Guidebook: Written by me (Gergely) – now out in audio form as well. — In today’s episode of The Pragmatic Engineer, I am joined by former Uber colleague, Gautam Korlam. Gautam is the Co-Founder of Gitar, an agentic AI startup that automates code maintenance. Gautam was mobile engineer no. 9 at Uber and founding engineer for the mobile platform team – and so he learned a few things about scaling up engineering teams. We talk about: • How Gautam accidentally deleted Uber’s Java monorepo – really! • Uber's unique engineering stack and why custom solutions like SubmitQueue were built in-house • Monorepo: the benefits and downsides of this approach • From Engineer II to Principal Engineer at Uber: Gautam’s career trajectory • Practical strategies for building trust and gaining social capital  • How the platform team at Uber operated with a product-focused mindset • Vibe coding: why it helps with quick prototyping • How AI tools are changing developer experience and productivity • Important skills for devs to pick up to remain valuable as AI tools spread • And more! — Timestamps (00:00) Intro (02:11) How Gautam accidentally deleted Uber’s Java Monorepo (05:40) The impact of Gautam’s mistake (06:35) Uber’s unique engineering stack (10:15) Uber’s SubmitQueue (12:44) Why Uber moved to a monorepo (16:30) The downsides of a monorepo (18:35) Measurement products built in-house  (20:20) Measuring developer productivity and happiness  (22:52) How Devpods improved developer productivity  (27:37) The challenges with cloud development environments (29:10) Gautam’s journey from Eng II to Principal Engineer (32:00) Building trust and gaining social capital  (36:17) An explanation of Principal Engineer at Uber—and the archetypes at Uber  (45:07) The platform and program split at Uber (48:15) How Gautam and his team supported their internal users  (52:50) Gautam’s thoughts on developer productivity  (59:10) How AI enhances productivity, its limitations, and the rise of agentic AI (1:04:00) An explanation of Vibe coding (1:07:34) An overview of Gitar and all it can help developers with  (1:10:44) Top skills to cultivate to add value and stay relevant (1:17:00) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The Platform and Program split at Uber • How Uber is measuring engineering productivity • Inside Uber’s move to the Cloud • How Uber built its observability platform • Software Architect Archetypes — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

We talked about:

00:00 DataTalks.Club intro

00:00 DataTalks.Club anniversary "Ask Me Anything" event with Alexey Grigorev

02:29 The founding of DataTalks .Club

03:52 Alexey's transition from Java work to DataTalks.Club

04:58 Growth and success of DataTalks.Club courses

12:04 Motivation behind creating a free-to-learn community

24:03 Staying updated in data science through pet projects

26 :37 Hosting a second podcast and maintaining programming skills

28:56 Skepticism about LLMs and their relevance

31:53 Transitioning to DataTalks.Club and personal reflections

33:32 Memorable moments and the first event's success

36:19 Community building during the pandemic

38:31 AI's impact on data analysts and future roles

42:24 Discussion on AI in healthcare

44:37 Age and reflections on personal milestones

47:54 Building communities and personal connections

49:34 Future goals for the community and courses

51:18 Community involvement and engagement strategies

53:46 Ideas for competitions and hackathons

54:20 Inviting guests to the podcast

55:29 Course updates and future workshops

56:27 Podcast preparation and research process

58:30 Career opportunities in data science and transitioning fields

1:01 :10 Book recommendations and personal reading experiences

About the speaker:

Alexey Grigorev is the founder of DataTalks.Club.

Join our slack: https://datatalks.club/slack.html

0:00

hi everyone Welcome to our event this event is brought to you by data dos club which is a community of people who love

0:06

data and we have weekly events and today one is one of such events and I guess we

0:12

are also a community of people who like to wake up early if you're from the states right Christopher or maybe not so

0:19

much because this is the time we usually have uh uh our events uh for our guests

0:27

and presenters from the states we usually do it in the evening of Berlin time but yes unfortunately it kind of

0:34

slipped my mind but anyways we have a lot of events you can check them in the

0:41

description like there's a link um I don't think there are a lot of them right now on that link but we will be

0:48

adding more and more I think we have like five or six uh interviews scheduled so um keep an eye on that do not forget

0:56

to subscribe to our YouTube channel this way you will get notified about all our future streams that will be as awesome

1:02

as the one today and of course very important do not forget to join our community where you can hang out with

1:09

other data enthusiasts during today's interview you can ask any question there's a pin Link in live chat so click

1:18

on that link ask your question and we will be covering these questions during the interview now I will stop sharing my

1:27

screen and uh there is there's a a message in uh and Christopher is from

1:34

you so we actually have this on YouTube but so they have not seen what you wrote

1:39

but there is a message from to anyone who's watching this right now from Christopher saying hello everyone can I

1:46

call you Chris or you okay I should go I should uh I should look on YouTube then okay yeah but anyways I'll you don't

1:53

need like you we'll need to focus on answering questions and I'll keep an eye

1:58

I'll be keeping an eye on all the question questions so um

2:04

yeah if you're ready we can start I'm ready yeah and you prefer Christopher

2:10

not Chris right Chris is fine Chris is fine it's a bit shorter um

2:18

okay so this week we'll talk about data Ops again maybe it's a tradition that we talk about data Ops every like once per

2:25

year but we actually skipped one year so because we did not have we haven't had

2:31

Chris for some time so today we have a very special guest Christopher Christopher is the co-founder CEO and

2:37

head chef or hat cook at data kitchen with 25 years of experience maybe this

2:43

is outdated uh cuz probably now you have more and maybe you stopped counting I

2:48

don't know but like with tons of years of experience in analytics and software engineering Christopher is known as the

2:55

co-author of the data Ops cookbook and data Ops Manifesto and it's not the

3:00

first time we have Christopher here on the podcast we interviewed him two years ago also about data Ops and this one

3:07

will be about data hops so we'll catch up and see what actually changed in in

3:13

these two years and yeah so welcome to the interview well thank you for having

3:19

me I'm I'm happy to be here and talking all things related to data Ops and why

3:24

why why bother with data Ops and happy to talk about the company or or what's changed

3:30

excited yeah so let's dive in so the questions for today's interview are prepared by Johanna berer as always

3:37

thanks Johanna for your help so before we start with our main topic for today

3:42

data Ops uh let's start with your ground can you tell us about your career Journey so far and also for those who

3:50

have not heard have not listened to the previous podcast maybe you can um talk

3:55

about yourself and also for those who did listen to the previous you can also maybe give a summary of what has changed

4:03

in the last two years so we'll do yeah so um my name is Chris so I guess I'm

4:09

a sort of an engineer so I spent about the first 15 years of my career in

4:15

software sort of working and building some AI systems some non- AI systems uh

4:21

at uh Us's NASA and MIT linol lab and then some startups and then um

4:30

Microsoft and then about 2005 I got I got the data bug uh I think you know my

4:35

kids were small and I thought oh this data thing was easy and I'd be able to go home uh for dinner at 5 and life

4:41

would be fine um because I was a big you started your own company right and uh it didn't work out that way

4:50

and um and what was interesting is is for me it the problem wasn't doing the

4:57

data like I we had smart people who did data science and data engineering the act of creating things it was like the

5:04

systems around the data that were hard um things it was really hard to not have

5:11

errors in production and I would sort of driving to work and I had a Blackberry at the time and I would not look at my

5:18

Blackberry all all morning I had this long drive to work and I'd sit in the parking lot and take a deep breath and

5:24

look at my Blackberry and go uh oh is there going to be any problems today and I'd be and if there wasn't I'd walk and

5:30

very happy um and if there was I'd have to like rce myself um and you know and

5:36

then the second problem is the team I worked for we just couldn't go fast enough the customers were super

5:42

demanding they didn't care they all they always thought things should be faster and we are always behind and so um how

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do you you know how do you live in that world where things are breaking left and right you're terrified of making errors

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um and then second you just can't go fast enough um and it's preh Hadoop era

6:02

right it's like before all this big data Tech yeah before this was we were using

6:08

uh SQL Server um and we actually you know we had smart people so we we we

6:14

built an engine in SQL Server that made SQL Server a column or

6:20

database so we built a column or database inside of SQL Server um so uh

6:26

in order to make certain things fast and and uh yeah it was it was really uh it's not

6:33

bad I mean the principles are the same right before Hadoop it's it's still a database there's still indexes there's

6:38

still queries um things like that we we uh at the time uh you would use olap

6:43

engines we didn't use those but you those reports you know are for models it's it's not that different um you know

6:50

we had a rack of servers instead of the cloud um so yeah and I think so what what I

6:57

took from that was uh it's just hard to run a team of people to do do data and analytics and it's not

7:05

really I I took it from a manager perspective I started to read Deming and

7:11

think about the work that we do as a factory you know and in a factory that produces insight and not automobiles um

7:18

and so how do you run that factory so it produces things that are good of good

7:24

quality and then second since I had come from software I've been very influenced

7:29

by by the devops movement how you automate deployment how you run in an agile way how you

7:35

produce um how you how you change things quickly and how you innovate and so

7:41

those two things of like running you know running a really good solid production line that has very low errors

7:47

um and then second changing that production line at at very very often they're kind of opposite right um and so

7:55

how do you how do you as a manager how do you technically approach that and

8:00

then um 10 years ago when we started data kitchen um we've always been a profitable company and so we started off

8:07

uh with some customers we started building some software and realized that we couldn't work any other way and that

8:13

the way we work wasn't understood by a lot of people so we had to write a book and a Manifesto to kind of share our our

8:21

methods and then so yeah we've been in so we've been in business now about a little over 10

8:28

years oh that's cool and uh like what

8:33

uh so let's talk about dat offs and you mentioned devops and how you were inspired by that and by the way like do

8:41

you remember roughly when devops as I think started to appear like when did people start calling these principles

8:49

and like tools around them as de yeah so agile Manifesto well first of all the I

8:57

mean I had a boss in 1990 at Nasa who had this idea build a

9:03

little test a little learn a lot right that was his Mantra and then which made

9:09

made a lot of sense um and so and then the sort of agile software Manifesto

9:14

came out which is very similar in 2001 and then um the sort of first real

9:22

devops was a guy at Twitter started to do automat automated deployment you know

9:27

push a button and that was like 200 Nish and so the first I think devops

9:33

Meetup was around then so it's it's it's been 15 years I guess 6 like I was

9:39

trying to so I started my career in 2010 so I my first job was a Java

9:44

developer and like I remember for some things like we would just uh SFTP to the

9:52

machine and then put the jar archive there and then like keep our fingers crossed that it doesn't break uh uh like

10:00

it was not really the I wouldn't call it this way right you were deploying you

10:06

had a Dey process I put it yeah

10:11

right was that so that was documented too it was like put the jar on production cross your

10:17

fingers I think there was uh like a page on uh some internal Viki uh yeah that

10:25

describes like with passwords and don't like what you should do yeah that was and and I think what's interesting is

10:33

why that changed right and and we laugh at it now but that was why didn't you

10:38

invest in automating deployment or a whole bunch of automated regression

10:44

tests right that would run because I think in software now that would be rare

10:49

that people wouldn't use C CD they wouldn't have some automated tests you know functional

10:56

regression tests that would be the

In this episode, Conor and Bryce chat with Kevlin Henney about the top recommendation from 97 Things Every Programmer Should Know. Link to Episode 194 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest Kevlin Henney is an independent consultant, speaker, writer and trainer. His software development interests are in programming, practice and people. He has been a columnist for various magazines and websites. He is the co-author of A Pattern Language for Distributed Computing and On Patterns and Pattern Languages, two volumes in the Pattern-Oriented Software Architecture series, and editor of 97 Things Every Programmer Should Know and co-editor of 97 Things Every Java Programmer Should Know. Show Notes Date Recorded: 2024-07-11 Date Released: 2024-08-09 97 Things Every Programmer Should Know (GitHub)97 Things Every Programmer Should KnowPattern-Oriented Software Architecture: A Pattern Language for Distributed Computing, 4th VolumePattern Oriented Software Architecture Volume 5: On Patterns and Pattern LanguagesEffective C++ Series by Scott MeyersBeautiful C++: 30 Core Guidelines for Writing Clean, Safe, and Fast CodeIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

In this episode, Bryce chats with Kevlin Henney about Kevlin Henneys. Link to Episode 193 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest Kevlin Henney is an independent consultant, speaker, writer and trainer. His software development interests are in programming, practice and people. He has been a columnist for various magazines and websites. He is the co-author of A Pattern Language for Distributed Computing and On Patterns and Pattern Languages, two volumes in the Pattern-Oriented Software Architecture series, and editor of 97 Things Every Programmer Should Know and co-editor of 97 Things Every Java Programmer Should Know. Show Notes Date Recorded: 2024-07-11 Date Released: 2024-08-02 HPXIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

In this episode, Bryce chats with Kevlin Henney about systems programming and more. Link to Episode 192 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest Kevlin Henney is an independent consultant, speaker, writer and trainer. His software development interests are in programming, practice and people. He has been a columnist for various magazines and websites. He is the co-author of A Pattern Language for Distributed Computing and On Patterns and Pattern Languages, two volumes in the Pattern-Oriented Software Architecture series, and editor of 97 Things Every Programmer Should Know and co-editor of 97 Things Every Java Programmer Should Know. Show Notes Date Recorded: 2024-07-11 Date Released: 2024-07-26 Kevlin Henney ACCU 2024 TalkIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

In this episode, Conor and Bryce chat with Kevlin Henney about algorithms, libraries and many programming languages! Link to Episode 191 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest Kevlin Henney is an independent consultant, speaker, writer and trainer. His software development interests are in programming, practice and people. He has been a columnist for various magazines and websites. He is the co-author of A Pattern Language for Distributed Computing and On Patterns and Pattern Languages, two volumes in the Pattern-Oriented Software Architecture series, and editor of 97 Things Every Programmer Should Know and co-editor of 97 Things Every Java Programmer Should Know. Show Notes Date Recorded: 2024-07-11 Date Released: 2024-07-19 FortranCoarray FortranPascal LanguagepytestNumPyPython pipRust cargoRust crates.ioIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

In this episode, Conor and Bryce chat with Kevlin Henney about C++, Python and more! Link to Episode 190 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest Kevlin Henney is an independent consultant, speaker, writer and trainer. His software development interests are in programming, practice and people. He has been a columnist for various magazines and websites. He is the co-author of A Pattern Language for Distributed Computing and On Patterns and Pattern Languages, two volumes in the Pattern-Oriented Software Architecture series, and editor of 97 Things Every Programmer Should Know and co-editor of 97 Things Every Java Programmer Should Know. Show Notes Date Recorded: 2024-07-11 Date Released: 2024-07-12 When zombies attack! Bristol city council ready for undead invasionACCU Conference97 Things Every Programmer Should Know (GitHub)97 Things Every Programmer Should Know97 Things Every Java Programmer Should KnowC++Now 2018: Ben Deane “Easy to Use, Hard to Misuse: Declarative Style in C++”When to Use a List Comprehension in PythonIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

Summary

Building a data platform that is enjoyable and accessible for all of its end users is a substantial challenge. One of the core complexities that needs to be addressed is the fractal set of integrations that need to be managed across the individual components. In this episode Tobias Macey shares his thoughts on the challenges that he is facing as he prepares to build the next set of architectural layers for his data platform to enable a larger audience to start accessing the data being managed by his team.

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! Developing event-driven pipelines is going to be a lot easier - Meet Functions! Memphis functions enable developers and data engineers to build an organizational toolbox of functions to process, transform, and enrich ingested events “on the fly” in a serverless manner using AWS Lambda syntax, without boilerplate, orchestration, error handling, and infrastructure in almost any language, including Go, Python, JS, .NET, Java, SQL, and more. Go to dataengineeringpodcast.com/memphis today to get started! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'll be sharing an update on my own journey of building a data platform, with a particular focus on the challenges of tool integration and maintaining a single source of truth

Interview

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

existing integrations with dbt switching cost for e.g. SQLMesh de facto standard of Airflow

Single source of truth

permissions management across application layers Database engine Storage layer in a lakehouse Presentation/access layer (BI) Data flows dbt -> table level lineage orchestration engine -> pipeline flows

task based vs. asset based

Metadata platform as the logical place for horizontal view

Contact Info

LinkedIn Website

Parting Questio

In this episode, Conor and Bryce conclude their conversation with Jonathan O’Connor and chat about a plethora of topics: multiparadigm languages, Ratfor, airport lounges, Meeting C++, code::dive and more. Link to Episode 155 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest: Jonathan O’Connor in 1988 joined Glockenspiel, a small Irish company. C++ had no virtual destructors, but it did have a coroutine library! I spent 2 years teaching C++ and OOP. In 2000, he switched over to Java. But by 2010, he started 7 wonderful years writing in Ruby. In 2016, he returned to a completely different C++, where one never had to see a pointer if you didn’t want to. These days he is helping to make the world a better place writing C++ code for LADE GmbH, a company building electric car charging infrastructure.

Show Notes

Date Recorded: 2023-10-18 Date Released: 2023-11-10 Jonathan O’Connor Meeting C++ BioRatforSoftware Tools by Brian Kernighan and P.J. PlaugerADSP Bingo BoardMeeting C++ Conferencecode::dive ConferenceIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

In this episode, Conor and Bryce continue their conversation with Jonathan O’Connor and chat about a plethora of programming languages! Link to Episode 154 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest: Jonathan O’Connor in 1988 joined Glockenspiel, a small Irish company. C++ had no virtual destructors, but it did have a coroutine library! I spent 2 years teaching C++ and OOP. In 2000, he switched over to Java. But by 2010, he started 7 wonderful years writing in Ruby. In 2016, he returned to a completely different C++, where one never had to see a pointer if you didn’t want to. These days he is helping to make the world a better place writing C++ code for LADE GmbH, a company building electric car charging infrastructure.

Show Notes

Date Recorded: 2023-10-18 Date Released: 2023-11-03 Jonathan O’Connor Meeting C++ BioAlgorithms + Data Structures = Programs BookPascal LanguageAda LanguageWhy Did C Succeed Over Pascal?Carbon GithubZig LanguageNim LanguageUiua LanguageEiffel LanguageBertrand MeyerRichard Feldman on TwitterSoftware Unscripted PodcastWhy Isn’t Functional Programming the Norm? – Richard FeldmanJames Gosling Keynote “Thoughts on language evolution” - reClojure 2022Clojure LanguageArrayCast Episode 41: John Earnest and Versions of kIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

In this episode, Conor and Bryce conintue their conversation with Jonathan O’Connor and chat about Pascal, C, Ada and more! Link to Episode 153 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest: Jonathan O’Connor in 1988 joined Glockenspiel, a small Irish company. C++ had no virtual destructors, but it did have a coroutine library! I spent 2 years teaching C++ and OOP. In 2000, he switched over to Java. But by 2010, he started 7 wonderful years writing in Ruby. In 2016, he returned to a completely different C++, where one never had to see a pointer if you didn’t want to. These days he is helping to make the world a better place writing C++ code for LADE GmbH, a company building electric car charging infrastructure.

Show Notes

Date Recorded: 2023-10-18 Date Released: 2023-10-27 Jonathan O’Connor Meeting C++ BioProgtools on TwitterSpicy - aespa エスパ [Music Bank] | KBS WORLD TV 230519Oxide and Friends Episode 93 - Settling BeefAlgorithms + Data Structures = Programs BookStructure and Interpretation of Computer ProgrammingPascal LanguageAda LanguageWhy Did C Succeed Over Pascal?Alan Turing as a RunnerIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

In this episode, Conor and Bryce chat with Jonathan O’Connor about his career path from C++ to Java to Ruby and back to C++, as well as his work in Rwanda and a discussion about quines! Link to Episode 152 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest: Jonathan O’Connor in 1988 joined Glockenspiel, a small Irish company. C++ had no virtual destructors, but it did have a coroutine library! I spent 2 years teaching C++ and OOP. In 2000, he switched over to Java. But by 2010, he started 7 wonderful years writing in Ruby. In 2016, he returned to a completely different C++, where one never had to see a pointer if you didn’t want to. These days he is helping to make the world a better place writing C++ code for LADE GmbH, a company building electric car charging infrastructure.

Show Notes

Date Recorded: 2023-10-18 Date Released: 2023-10-20 Jonathan O’Connor Meeting C++ BioMeeting C++ ConferenceAlices adventures in Template Land - Jonathan O’Connor - Meeting C++ 2018Ruby String to_iRuby Integer to_sRuby Slices ..Number of Automated Teller Machines (ATMs), Country Wide for RwandaPython Index SlicingM-Pesa appCommon LispFranz LispFranz Liszt (composer)DylanPicoLispHistory of Lisps YouTube Video (Structure and Interpretation of Computer Programs - Chapter 1.1Rosetta Code: QuineLightning Talk: How to Write a Quine? - Dmitry Kandalov [ ACCU 2021 ]Quine-Relay (Uroboros)ACL2 LanguageIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

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…

In this episode, Conor and Bryce record live from Slovenia, Croatia and Italy while driving and chat about next year’s 2024 Nigeria Road Trip as well as Bryce’s near death experience. This episode is very light on the technical content (so feel free to skip). Link to Episode 144 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachShow Notes Date Recorded: 2023-06-21 Date Released: 2023-08-25 PiranFireship Java YouTube Video (Java is mounting a huge comeback)Run for the Fun of It PodcastHaskell Programming LanguageClojure Programming LanguageIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

Summary The problems that are easiest to fix are the ones that you prevent from happening in the first place. Sifflet is a platform that brings your entire data stack into focus to improve the reliability of your data assets and empower collaboration across your teams. In this episode CEO and founder Salma Bakouk shares her views on the causes and impacts of "data entropy" and how you can tame it before it leads to failures.

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 Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer. Your host is Tobias Macey and today I’m interviewing Salma Bakouk about achieving data reliability and reducing entropy within your data stack with sifflet

Interview

Introduction How did you get involved in the area of data management? Can you describe what Sifflet is and the st

Summary CreditKarma builds data products that help consumers take advantage of their credit and financial capabilities. To make that possible they need a reliable data platform that empowers all of the organization’s stakeholders. In this episode Vishnu Venkataraman shares the journey that he and his team have taken to build and evolve their systems and improve the product offerings that they are able to support.

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 Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer. Your host is Tobias Macey and today I’m interviewing Vishnu Venkataraman about building the data platform at CreditKarma and the forces that shaped the design

Interview

Introduction How did you get involved in the area of data management? Can you describe what CreditKarma is and the role

Summary Despite the best efforts of data engineers, data is as messy as the real world. Entity resolution and fuzzy matching are powerful utilities for cleaning up data from disconnected sources, but it has typically required custom development and training machine learning models. Sonal Goyal created and open-sourced Zingg as a generalized tool for data mastering and entity resolution to reduce the effort involved in adopting those practices. In this episode she shares the story behind the project, the details of how it is implemented, and how you can use it for your own data projects.

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! 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 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. Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer. Your host is Tobias Macey and today I’m interviewing Sonal Goyal about Zingg, an open source entity resolution frame