Summary Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. Dataform is a platform that helps you apply engineering principles to your data transformations and table definitions, including unit testing SQL scripts, defining repeatable pipelines, and adding metadata to your warehouse to improve your team’s communication. In this episode CTO and co-founder of Dataform Lewis Hemens joins the show to explain his motivation for creating the platform and company, how it works under the covers, and how you can start using it today to get your data warehouse under control.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral. They provide an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure. Datacoral’s customers report that their data engineers are able to spend 80% of their work time invested in data transformations, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from mere terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit Datacoral.com today to find out more. Are you working on data, analytics, or AI using platforms such as Presto, Spark, or Tensorflow? Check out the Data Orchestration Summit on November 7 at the Computer History Museum in Mountain View. This one day conference is focused on the key data engineering challenges and solutions around building analytics and AI platforms. Attendees will hear from companies including Walmart, Netflix, Google, and DBS Bank on how they leveraged technologies such as Alluxio, Presto, Spark, Tensorflow, and you will also hear from creators of open source projects including Alluxio, Presto, Airflow, Iceberg, and more! Use discount code PODCAST for 25% off of your ticket, and the first five people to register get free tickets! Register now as early bird tickets are ending this week! Attendees will takeaway learnings, swag, a free voucher to visit the museum, and a chance to win the latest ipad Pro! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Lewis Hemens about DataForm, a platform that helps analy

Summary Building a data platform that works equally well for data engineering and data science is a task that requires familiarity with the needs of both roles. Data engineering platforms have a strong focus on stateful execution and tasks that are strictly ordered based on dependency graphs. Data science platforms provide an environment that is conducive to rapid experimentation and iteration, with data flowing directly between stages. Jeremiah Lowin has gained experience in both styles of working, leading him to be frustrated with all of the available tools. In this episode he explains his motivation for creating a new workflow engine that marries the needs of data engineers and data scientists, how it helps to smooth the handoffs between teams working on data projects, and how the design lets you focus on what you care about while it handles the failure cases for you. It is exciting to see a new generation of workflow engine that is learning from the benefits and failures of previous tools for processing your data pipelines.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Jeremiah Lowin about Prefect, a workflow platform for data engineering

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Prefect is and your motivation for creating it? What are the axes along which a workflow engine can differentiate itself, and which of those have you focused on for Prefect? In some of your blog posts and your PyData presentation you discuss the concept of negative vs. positive engineering. Can you briefly outline what you mean by that and the ways that Prefect handles the negative cases for you? How is Prefect itself implemented and what tools or systems have you relied on most heavily for inspiration? How do you manage passing data between stages in a pipeline when they are running across distributed nodes? What was your decision making process when deciding to use Dask as your supported execution engine?

For tasks that require specific resources or dependencies how do you approach the idea of task affinity?

Does Prefect support managing tasks that bridge network boundaries? What are some of the features or capabilities of Prefect that are misunderstood or overlooked by users which you think should be exercised more often? What are the limitations of the open source core as compared to the cloud offering that you are building? What were your assumptions going into this project and how have they been challenged or updated as you dug deeper into the problem domain and received feedback from users? What are some of the most interesting/innovative/unexpected ways that you have seen Prefect used? When is Prefect the wrong choice? In your experience working on Airflow and Prefect, what are some of the common challenges and anti-patterns that arise in data engineering projects?

What are some best practices and industry trends that you are most excited by?

What do you have planned for the future of the Prefect project and company?

Contact Info

LinkedIn @jlowin on Twitter

Parting Question

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

Links

Prefect Airflow Dask

Podcast Episode

Prefect Blog PyData Presentation Tensorflow Workflow Engine

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

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Summary Machine learning is a class of technologies that promise to revolutionize business. Unfortunately, it can be difficult to identify and execute on ways that it can be used in large companies. Kevin Dewalt founded Prolego to help Fortune 500 companies build, launch, and maintain their first machine learning projects so that they can remain competitive in our landscape of constant change. In this episode he discusses why machine learning projects require a new set of capabilities, how to build a team from internal and external candidates, and how an example project progressed through each phase of maturity. This was a great conversation for anyone who wants to understand the benefits and tradeoffs of machine learning for their own projects and how to put it into practice.

Introduction

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 Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Kevin Dewalt about his experiences at Prolego, building machine learning projects for Fortune 500 companies

Interview

Introduction How did you get involved in the area of data management? For the benefit of software engineers and team leaders who are new to machine learning, can you briefly describe what machine learning is and why is it relevant to them? What is your primary mission at Prolego and how did you identify, execute on, and establish a presence in your particular market?

How much of your sales process is spent on educating your clients about what AI or ML are and the benefits that these technologies can provide?

What have you found to be the technical skills and capacity necessary for being successful in building and deploying a machine learning project?

When engaging with a client, what have you found to be the most common areas of technical capacity or knowledge that are needed?

Everyone talks about a talent shortage in machine learning. Can you suggest a recruiting or skills development process for companies which need to build out their data engineering practice? What challenges will teams typically encounter when creating an efficient working relationship between data scientists and data engineers? Can you briefly describe a successful project of developing a first ML model and putting it into production?

What is the breakdown of how much time was spent on different activities such as data wrangling, model development, and data engineering pipeline development? When releasing to production, can you share the types of metrics that you track to ensure the health and proper functioning of the models? What does a deployable artifact for a machine learning/deep learning application look like?

What basic technology stack is necessary for putting the first ML models into production?

How does the build vs. buy debate break down in this space and what products do you typically recommend to your clients?

What are the major risks associated with deploying ML models and how can a team mitigate them? Suppose a software engineer wants to break into ML. What data engineering skills would you suggest they learn? How should they position themselves for the right opportunity?

Contact Info

Email: Kevin Dewalt [email protected] and Russ Rands [email protected] Connect on LinkedIn: Kevin Dewalt and Russ Rands Twitter: @kevindewalt

Parting Question

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

Links

Prolego Download our book: Become an AI Company in 90 Days Google Rules Of ML AI Winter Machine Learning Supervised Learning O’Reilly Strata Conference GE Rebranding Commercials Jez Humble: Stop Hiring Devops Experts (And Start Growing Them) SQL ORM Django RoR Tensorflow PyTorch Keras Data Engineering Podcast Episode About Data Teams DevOps For Data Teams – DevOps Days Boston Presentation by Tobias Jupyter Notebook Data Engineering Podcast: Notebooks at Netflix Pandas

Podcast Interview

Joel Grus

JupyterCon Presentation Data Science From Scratch

Expensify Airflow

James Meickle Interview

Git Jenkins Continuous Integration Practical Deep Learning For Coders Course by Jeremy Howard Data Carpentry

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

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Summary

The majority of the conversation around machine learning and big data pertains to well-structured and cleaned data sets. Unfortunately, that is just a small percentage of the information that is available, so the rest of the sources of knowledge in a company are housed in so-called “Dark Data” sets. In this episode Alex Ratner explains how the work that he and his fellow researchers are doing on Snorkel can be used to extract value by leveraging labeling functions written by domain experts to generate training sets for machine learning models. He also explains how this approach can be used to democratize machine learning by making it feasible for organizations with smaller data sets than those required by most tooling.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Alex Ratner about Snorkel and Dark Data

Interview

Introduction How did you get involved in the area of data management? Can you start by sharing your definition of dark data and how Snorkel helps to extract value from it? What are some of the most challenging aspects of building labelling functions and what tools or techniques are available to verify their validity and effectiveness in producing accurate outcomes? Can you provide some examples of how Snorkel can be used to build useful models in production contexts for companies or problem domains where data collection is difficult to do at large scale? For someone who wants to use Snorkel, what are the steps involved in processing the source data and what tooling or systems are necessary to analyse the outputs for generating usable insights? How is Snorkel architected and how has the design evolved over its lifetime? What are some situations where Snorkel would be poorly suited for use? What are some of the most interesting applications of Snorkel that you are aware of? What are some of the other projects that you and your group are working on that interact with Snorkel? What are some of the features or improvements that you have planned for future releases of Snorkel?

Contact Info

Website ajratner on Github @ajratner on Twitter

Parting Question

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

Links

Stanford DAWN HazyResearch Snorkel Christopher Ré Dark Data DARPA Memex Training Data FDA ImageNet National Library of Medicine Empirical Studies of Conflict Data Augmentation PyTorch Tensorflow Generative Model Discriminative Model Weak Supervision

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

Summary

As we scale our systems to handle larger volumes of data, geographically distributed users, and varied data sources the requirement to distribute the computational resources for managing that information becomes more pronounced. In order to ensure that all of the distributed nodes in our systems agree with each other we need to build mechanisms to properly handle replication of data and conflict resolution. In this episode Christopher Meiklejohn discusses the research he is doing with Conflict-Free Replicated Data Types (CRDTs) and how they fit in with existing methods for sharing and sharding data. He also shares resources for systems that leverage CRDTs, how you can incorporate them into your systems, and when they might not be the right solution. It is a fascinating and informative treatment of a topic that is becoming increasingly relevant in a data driven world.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Christopher Meiklejohn about establishing consensus in distributed systems

Interview

Introduction How did you get involved in the area of data management? You have dealt with CRDTs with your work in industry, as well as in your research. Can you start by explaining what a CRDT is, how you first began working with them, and some of their current manifestations? Other than CRDTs, what are some of the methods for establishing consensus across nodes in a system and how does increased scale affect their relative effectiveness? One of the projects that you have been involved in which relies on CRDTs is LASP. Can you describe what LASP is and what your role in the project has been? Can you provide examples of some production systems or available tools that are leveraging CRDTs? If someone wants to take advantage of CRDTs in their applications or data processing, what are the available off-the-shelf options, and what would be involved in implementing custom data types? What areas of research are you most excited about right now? Given that you are currently working on your PhD, do you have any thoughts on the projects or industries that you would like to be involved in once your degree is completed?

Contact Info

Website cmeiklejohn on GitHub Google Scholar Citations

Parting Question

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

Links

Basho Riak Syncfree LASP CRDT Mesosphere CAP Theorem Cassandra DynamoDB Bayou System (Xerox PARC) Multivalue Register Paxos RAFT Byzantine Fault Tolerance Two Phase Commit Spanner ReactiveX Tensorflow Erlang Docker Kubernetes Erleans Orleans Atom Editor Automerge Martin Klepman Akka Delta CRDTs Antidote DB Kops Eventual Consistency Causal Consistency ACID Transactions Joe Hellerstein

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