Summary In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Nick Schrock, CTO and founder of Dagster Labs, to discuss Compass - a Slack-native, agentic analytics system designed to keep data teams connected with business stakeholders. Nick shares his journey from initial skepticism to embracing agentic AI as model and application advancements made it practical for governed workflows, and explores how Compass redefines the relationship between data teams and stakeholders by shifting analysts into steward roles, capturing and governing context, and integrating with Slack where collaboration already happens. The conversation covers organizational observability through Compass's conversational system of record, cost control strategies, and the implications of agentic collaboration on Conway's Law, as well as what's next for Compass and Nick's optimistic views on AI-accelerated software engineering.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Nick Schrock about building an AI analyst that keeps data teams in the loopInterview IntroductionHow did you get involved in the area of data management?Can you describe what Compass is and the story behind it?context repository structurehow to keep it relevant/avoid sprawl/duplicationproviding guardrailshow does a tool like Compass help provide feedback/insights back to the data teams?preparing the data warehouse for effective introspection by the AILLM selectioncost managementcaching/materializing ad-hoc queriesWhy Slack and enterprise chat are important to b2b softwareHow AI is changing stakeholder relationshipsHow not to overpromise AI capabilities How does Compass relate to BI?How does Compass relate to Dagster and Data Infrastructure?What are the most interesting, innovative, or unexpected ways that you have seen Compass used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Compass?When is Compass the wrong choice?What do you have planned for the future of Compass?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links DagsterDagster LabsDagster PlusDagster CompassChris Bergh DataOps EpisodeRise of Medium Code blog postContext EngineeringData StewardInformation ArchitectureConway's LawTemporal durable execution frameworkThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast Pete DeJoy, co-founder and product lead at Astronomer, talks about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3. Pete shares his journey into data engineering, discusses Astronomer's contributions to the Airflow project, and highlights the critical role of Airflow in powering operational data products. He covers the evolution of Airflow, its position in the data ecosystem, and the challenges faced by data engineers, including infrastructure management and observability. The conversation also touches on the upcoming Airflow 3 release, which introduces data awareness, architectural improvements, and multi-language support, and Astronomer's observability suite, Astro Observe, which provides insights and proactive recommendations for Airflow users.

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

In this podcast episode, we talked with Agita Jaunzeme about Career choices, transitions and promotions in and out of tech.

About the Speaker:

Agita has designed a career spanning DevOps/DataOps engineering, management, community building, education, and facilitation. She has worked on projects across corporate, startup, open source, and non-governmental sectors. Following her passion, she founded an NGO focusing on the inclusion of expats and locals in Porto. Embodying the values of innovation, automation, and continuous learning, Agita provides practical insights on promotions, career pivots, and aligning work with passion and purpose.

During this event, discussed their career journey, starting with their transition from art school to programming and later into DevOps, eventually taking on leadership roles. They explored the challenges of burnout and the importance of volunteering, founding an NGO to support inclusion, gender equality, and sustainability. The conversation also covered key topics like mentorship, the differences between data engineering and data science, and the dynamics of managing volunteers versus employees. Additionally, the guest shared insights on community management, developer relations, and the importance of product vision and team collaboration.

0:00 Introduction and Welcome 1:28 Guest Introduction: Agita’s Background and Career Highlights 3:05 Transition to Tech: From Art School to Programming 5:40 Exploring DevOps and Growing into Leadership Roles 7:24 Burnout, Volunteering, and Founding an NGO 11:00 Volunteering and Mentorship Initiatives 14:00 Discovering Programming Skills and Early Career Challenges 15:50 Automating Work Processes and Earning a Promotion 19:00 Transitioning from DevOps to Volunteering and Project Management 24:00 Managing Volunteers vs. Employees and Building Organizational Skills 31:07 Personality traits in engineering vs. data roles 33:14 Differences in focus between data engineers and data scientists 36:24 Transitioning from volunteering to corporate work 37:38 The role and responsibilities of a community manager 39:06 Community management vs. developer relations activities 41:01 Product vision and team collaboration 43:35 Starting an NGO and legal processes 46:13 NGO goals: inclusion, gender equality, and sustainability 49:02 Community meetups and activities 51:57 Living off-grid in a forest and sustainability 55:02 Unemployment party and brainstorming session 59:03 Unemployment party: the process and structure

🔗 CONNECT WITH AGITA JAUNZEME Linkedin - /agita

🔗 CONNECT WITH DataTalksClub Join DataTalks.Club: ⁠https://datatalks.club/slack.html⁠ Our events: ⁠https://datatalks.club/events.html⁠ Datalike Substack - ⁠https://datalike.substack.com/⁠ LinkedIn: ⁠  / datatalks-club  

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

5:50

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

5:57

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

Chris Bergh joins me to chat about all things DataOps. We also discuss lean, removing waste from data processes and teams, and much more.

DataKitchen: https://datakitchen.io/

DataOps Manifesto: https://dataopsmanifesto.org/en/

Summary In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Chris Berg, CEO of DataKitchen, to discuss his ongoing mission to simplify the lives of data engineers. Chris explains the challenges faced by data engineers, such as constant system failures, the need for rapid changes, and high customer demands. Chris delves into the concept of DataOps, its evolution, and the misappropriation of related terms like data mesh and data observability. He emphasizes the importance of focusing on processes and systems rather than just tools to improve data engineering workflows. Chris also introduces DataKitchen's open-source tools, DataOps TestGen and DataOps Observability, designed to automate data quality validation and monitor data journeys in production. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. 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'm interviewing Chris Bergh about his tireless quest to simplify the lives of data engineersInterview IntroductionHow did you get involved in the area of data management?Can you describe what DataKitchen is and the story behind it?You helped to define and popularize "DataOps", which then went through a journey of misappropriation similar to "DevOps", and has since faded in use. What is your view on the realities of "DataOps" today?Out of the popularized wave of "DataOps" tools came subsequent trends in data observability, data reliability engineering, etc. How have those cycles influenced the way that you think about the work that you are doing at DataKitchen?The data ecosystem went through a massive growth period over the past ~7 years, and we are now entering a cycle of consolidation. What are the fundamental shifts that we have gone through as an industry in the management and application of data?What are the challenges that never went away?You recently open sourced the dataops-testgen and dataops-observability tools. What are the outcomes that you are trying to produce with those projects?What are the areas of overlap with existing tools and what are the unique capabilities that you are offering?Can you talk through the technical implementation of your new obserability and quality testing platform?What does the onboarding and integration process look like?Once a team has one or both tools set up, what are the typical points of interaction that they will have over the course of their workday?What are the most interesting, innovative, or unexpected ways that you have seen dataops-observability/testgen used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on promoting DataOps?What do you have planned for the future of your work at DataKitchen?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Links DataKitchenPodcast EpisodeNASADataOps ManifestoData Reliability EngineeringData ObservabilitydbtDevOps Enterprise SummitBuilding The Data Warehouse by Bill Inmon (affiliate link)dataops-testgen, dataops-observabilityFree Data Quality and Data Observability CertificationDatabricksDORA MetricsDORA for dataThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Doug Needham is an OG DBA and data architect who built DataOps workflows back in Desert Storm (!) and has managed to stay very current with data to today. We talk about data architecture war stories, the hard work to do generative AI in the enterprise, and much more. Enjoy!

In the fast-paced work environments we are used to, the ability to quickly find and understand data is essential. Data professionals can often spend more time searching for data than analyzing it, which can hinder business progress. Innovations like data catalogs and automated lineage systems are transforming data management, making it easier to ensure data quality, trust, and compliance. By creating a strong metadata foundation and integrating these tools into existing workflows, organizations can enhance decision-making and operational efficiency. But how did this all come to be, who is driving better access and collaboration through data? Prukalpa Sankar is the Co-founder of Atlan. Atlan is a modern data collaboration workspace (like GitHub for engineering or Figma for design). By acting as a virtual hub for data assets ranging from tables and dashboards to models & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Slack, BI tools, data science tools and more. A pioneer in the space, Atlan was recognized by Gartner as a Cool Vendor in DataOps, as one of the top 3 companies globally. Prukalpa previously co-founded SocialCops, world leading data for good company (New York Times Global Visionary, World Economic Forum Tech Pioneer). SocialCops is behind landmark data projects including India’s National Data Platform and SDGs global monitoring in collaboration with the United Nations. She was awarded Economic Times Emerging Entrepreneur for the Year, Forbes 30u30, Fortune 40u40, Top 10 CNBC Young Business Women 2016, and a TED Speaker. In the episode, Richie and Prukalpa explore challenges within data discoverability, the inception of Atlan, the importance of a data catalog, personalization in data catalogs, data lineage, building data lineage, implementing data governance, human collaboration in data governance, skills for effective data governance, product design for diverse audiences, regulatory compliance, the future of data management and much more.  Links Mentioned in the Show: AtlanConnect with Prukalpa[Course] Artificial Intelligence (AI) StrategyRelated Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at SnowflakeSign up to RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

Explore the reasons for data engineers to collaborate with data scientists, machine learning (ML) engineers, and developers on DataOps initiatives that support GenAI. Published at: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-iii-team-collaboration

Companies that adopt DataOps increase the odds of success by making GenAI data pipelines what they should be: modular, scalable, robust, flexible, and governed. Published: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-ii-must-have-characteristics

There's the interview you think you're going to have, then there's the interview you get. This is one of those, in the best way possible. I expected to chat about his time at Snowflake. We didn't even get past his early days building data warehouses because it was so fascinating. Did you know Kent is arguably one of the very first practitioners (probably an accidental inventor) of DataOps?

This is sort of a "prequel" episode. Kent Graziano and I chat about his early days as a data practitioner.

The success of Generative AI depends on fundamental disciplines like DataOps. Published at: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-i-what-and-why

In this episode, I’m chatting with former Gartner analyst Sanjeev Mohan who is the Co-Author of Data Products for Dummies. Throughout our conversation, Sanjeev shares his expertise on the evolution of data products, and what he’s seen as a result of implementing practices that prioritize solving for use cases and business value. Sanjeev also shares a new approach of structuring organizations to best implement ownership and accountability of data product outcomes. Sanjeev and I also explore the common challenges of product adoption and who is responsible for user experience. I purposefully had Sanjeev on the show because I think we have pretty different perspectives from which we see the data product space.

Highlights/ Skip to:

I introduce Sanjeev Mohan, co-author of Data Products for Dummies (00:39) Sanjeev expands more on the concept of writing a “for Dummies” book   (00:53) Sanjeev shares his definition of a data product, including both a technical and a business definition (01:59) Why Sanjeev believes organizational changes and accountability are the keys to preventing the acceleration of shipping data products with little to no tangible value (05:45) How Sanjeev recommends getting buy-in for data product ownership from other departments in an organization (11:05) Sanjeev and I explore adoption challenges and the topic of user experience (13:23) Sanjeev explains what role is responsible for user experience and design (19:03) Who should be responsible for defining the metrics that determine business value (28:58) Sanjeev shares some case studies of companies who have adopted this approach to data products and their outcomes (30:29) Where companies are finding data product managers currently (34:19) Sanjeev expands on his perspective regarding the importance of prioritizing business value and use cases (40:52) Where listeners can get Data Products for Dummies, and learn more about Sanjeev’s work (44:33)

Quotes from Today’s Episode “You may slap a label of data product on existing artifact; it does not make it a data product because there’s no sense of accountability. In a data product, because they are following product management best practices, there must be a data product owner or a data product manager. There’s a single person [responsible for the result]. — Sanjeev Mohan (09:31)

“I haven’t even mentioned the word data mesh because data mesh and data products, they don’t always have to go hand-in-hand. I can build data products, but I don’t need to go into the—do all of data mesh principles.” – Sanjeev Mohan (26:45)

“We need to have the right organization, we need to have a set of processes, and then we need a simplified technology which is standardized across different teams. So, this way, we have the benefit of reusing the same technology. Maybe it is Snowflake for storage, DBT for modeling, and so on. And the idea is that different teams should have the ability to bring their own analytical engine.” – Sanjeev Mohan (27:58)

“Generative AI, right now as we are recording, is still in a prototyping phase. Maybe in 2024, it’ll go heavy-duty production. We are not in prototyping phase for data products for a lot of companies. They’ve already been experimenting for a year or two, and now they’re actually using them in production. So, we’ve crossed that tipping point for data products.” – Sanjeev Mohan (33:15)

“Low adoption is a problem that’s not just limited to data products. How long have we had data catalogs, but they have low adoption. So, it’s a common problem.” – Sanjeev Mohan (39:10)

“That emphasis on technology first is a wrong approach. I tell people that I’m sorry to burst your bubble, but there are no technology projects, there are only business projects. Technology is an enabler. You don’t do technology for the sake of technology; you have to serve a business cause, so let’s start with that and keep that front and center.” – Sanjeev Mohan (43:03)

Links Data Products for Dummies: https://www.dataops.live/dataproductsfordummies “What Exactly is A Data Product” article: https://medium.com/data-mesh-learning/what-exactly-is-a-data-product-7f6935a17912 It Depends: https://www.youtube.com/@SanjeevMohan Chief Data Analytics and Product Officer of Equifax: https://www.youtube.com/watch?v=kFY7WGc-jFM SanjMo Consulting: https://www.sanjmo.com/ dataops.live: https://dataops.live dataops.live/dataproductsfordummies: https://dataops.live/dataproductsfordummies LinkedIn: https://www.linkedin.com/in/sanjmo/ Medium articles: https://sanjmo.medium.com

The unbundling of the data ecosystem is causing organizations to “duct tape” products and frameworks together to build their solutions and data delivery processes. Organizations fail to build and deploy end-to-end, automated, repeatable data-driven systems, ignoring data engineering & dataops principles as well as best practices. Published at: https://www.eckerson.com/articles/dataops-in-data-engineering

Summary

Data transformation is a key activity for all of the organizational roles that interact with data. Because of its importance and outsized impact on what is possible for downstream data consumers it is critical that everyone is able to collaborate seamlessly. SQLMesh was designed as a unifying tool that is simple to work with but powerful enough for large-scale transformations and complex projects. In this episode Toby Mao explains how it works, the importance of automatic column-level lineage tracking, and how you can start using it today.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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/rudderstack- Your host is Tobias Macey and today I'm interviewing Toby Mao about SQLMesh, an open source DataOps framework designed to scale data transformations with ease of collaboration and validation built in

Interview

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

DataOps is a term that has been co-opted and overloaded. What are the concepts that you are trying to convey with that term in the context of SQLMesh?

What are the rough edges in existing toolchains/workflows that you are trying to address with SQLMesh?

How do those rough edges impact the productivity and effectiveness of teams using those

Can you describe how SQLMesh is implemented?

How have the design and goals evolved since you first started working on it?

What are the lessons that you have learned from dbt which have informed the design and functionality of SQLMesh? For teams who have already invested in dbt, what is the migration path from or integration with dbt? You have some built-in integration with/awareness of orchestrators (currently Airflow). What are the benefits of making the transformation tool aware of the orchestrator? What do you see as the potential benefits of integration with e.g. data-diff? What are the second-order benefits of using a tool such as SQLMesh that addresses the more mechanical aspects of managing transformation workfows and the associated dependency chains? What are the most interesting, innovative, or unexpected ways that you have seen SQLMesh used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on SQLMesh? When is SQLMesh the wrong choice? What do you have planned for the future of SQLMesh?

Contact Info

tobymao on GitHub @captaintobs on Twitter 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 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

SQLMesh Tobiko Data SAS AirBnB Minerva SQLGlot Cron AST == Abstract Syntax Tree Pandas Terraform dbt

Podcast Episode

SQLFluff

Podcast.init Episode

The intro and outro music is from The Hug by The Freak Fandango Orc

We talked about:

Santona's background Focusing on data workflows Upsolver vs DBT ML pipelines vs Data pipelines MLOps vs DataOps Tools used for data pipelines and ML pipelines The “modern data stack” and today's data ecosystem Staging the data and the concept of a “lakehouse” Transforming the data after staging What happens after the modeling phase Human-centric vs Machine-centric pipeline Applying skills learned in academia to ML engineering Crafting user personas based on real stories A framework of curiosity Santona's book and resource recommendations

Links:

LinkedIn: https://www.linkedin.com/in/santona-tuli/ Upsolver website: upsolver.com Why we built a SQL-based solution to unify batch and stream workflows: https://www.upsolver.com/blog/why-we-built-a-sql-based-solution-to-unify-batch-and-stream-workflows

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

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

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

Summary

A significant portion of the time spent by data engineering teams is on managing the workflows and operations of their pipelines. DataOps has arisen as a parallel set of practices to that of DevOps teams as a means of reducing wasted effort. Agile Data Engine is a platform designed to handle the infrastructure side of the DataOps equation, as well as providing the insights that you need to manage the human side of the workflow. In this episode Tevje Olin explains how the platform is implemented, the features that it provides to reduce the amount of effort required to keep your pipelines running, and how you can start using it in your own team.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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/rudderstack Your host is Tobias Macey and today I'm interviewing Tevje Olin about Agile Data Engine, a platform that combines data modeling, transformations, continuous delivery and workload orchestration to help you manage your data products and the whole lifecycle of your warehouse

Interview

Introduction How did you get involved in the area of data management? Can you describe what Agile Data Engine is and the story behind it? What are some of the tools and architectures that an organization might be able to replace with Agile Data Engine?

How does the unified experience of Agile Data Engine change the way that teams think about the lifecycle of their data? What are some of the types of experiments that are enabled by reduced operational overhead?

What does CI/CD look like for a data warehouse?

How is it different from CI/CD for software applications?

Can you describe how Agile Data Engine is architected?

How have the design and goals of the system changed since you first started working on it? What are the components that you needed to develop in-house to enable your platform goals?

What are the changes in the broader data ecosystem that have had the most influence on your product goals and customer adoption? Can you describe the workflow for a team that is using Agile Data Engine to power their business analytics?

What are some of the insights that you generate to help your customers understand how to improve their processes or identify new opportunities?

In your "about" page it mentions the unique approaches that you take for warehouse automation. How do your practices differ from the rest of the industry? How have changes in the adoption/implementation of ML and AI impacted the ways that your customers exercise your platform? What are the most interesting, innovative, or unexpected ways that you have seen the Agile Data Engine platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Agile Data Engine? When is Agile Data Engine the wrong choice? What do you have planned for the future of Agile Data Engine?

Guest Contact Info

LinkedIn

Parting Question

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

About Agile Data Engine

Agile Data Engine unlocks the potential of your data to drive business value - in a rapidly changing world. Agile Data Engine is a DataOps Management platform for designing, deploying, operating and managing data products, and managing the whole lifecycle of a data warehouse. It combines data modeling, transformations, continuous delivery and workload orchestration into the same platform.

Links

Agile Data Engine Bill Inmon Ralph Kimball Snowflake Redshift BigQuery Azure Synapse Airflow

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

RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines.

RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team.

RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again.

Visit dataengineeringpodcast.com/rudderstack to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.Support Data Engineering Podcast

We talked about:

Bart's background What is data governance? Data dictionaries and data lineage Data access management How to learn about data governance What skills are needed to do data governance effectively When an organization needs to start thinking about data governance Good data access management processes Data masking and the importance of automating data access DPO and CISO roles How data access management works with a data mesh approach Avoiding the role explosion problem The importance of data governance integration in DataOps Terraform as a stepping stone to data governance How Raito can help an organization with data governance Open-source data governance tools

Links:

LinkedIn: https://www.linkedin.com/in/bartvandekerckhove/ Twitter: https://twitter.com/Bart_H_VDK Github: https://github.com/raito-io Website: https://www.raito.io/ Data Mesh Learning Slack: https://data-mesh-learning.slack.com/join/shared_invite/zt-1qs976pm9-ci7lU8CTmc4QD5y4uKYtAA#/shared-invite/email DataQG Website: https://dataqg.com/ DataQG Slack: https://dataqgcommunitygroup.slack.com/join/shared_invite/zt-12n0333gg-iTZAjbOBeUyAwWr8I~2qfg#/shared-invite/email DMBOK (Data Management Book of Knowledge): https://www.dama.org/cpages/body-of-knowledge DMBOK Wheel describing the data governance activities: https://www.dama.org/cpages/dmbok-2-wheel-images

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

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

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

We talked about:

Boyan's background What is data strategy? Due diligence and establishing a common goal Designing a data strategy Impact assessment, portfolio management, and DataOps Data products DataOps, Lean, and Agile Data Strategist vs Data Science Strategist The skills one needs to be a data strategist How does one become a data strategist? Data strategist as a translator Transitioning from a Data Strategist role to a CTO Using ChatGPT as a writing co-pilot Using ChatGPT as a starting point How ChatGPT can help in data strategy Pitching a data strategy to a stakeholder Setting baselines in a data strategy Boyan's book recommendations

Links:

LinkedIn: https://www.linkedin.com/in/angelovboyan/ Twitter: https://twitter.com/thinking_code Github: https://github.com/boyanangelov Website: https://boyanangelov.com/

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Many data engineers already use large language models to assist data ingestion, transformation, DataOps, and orchestration. This blog commences a series that explores the emergence of ChatGPT, Bard, and LLM tools from data pipeline vendors, and their implications for the discipline of data engineering. Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-examining-the-role-of-chatgpt-and-large-language-models-in-data-engineering