Summary

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

Announcements

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

Interview

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

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

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

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

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

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

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

Contact Info

LinkedIn @akarve on Twitter

Parting Question

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

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

Quilt Data

Podcast Episode

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

Podcast Episode

Pachyderm

Podcast Episode

Unstruk

Podcast Episode

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

Podcast Episode

Delta Lake

Podcast Episode

Apache Iceberg

Podcast Episode

Datasette Frictionless DVC

Podcast.init Episode

The in

Summary Data Engineering is a broad and constantly evolving topic, which makes it difficult to teach in a concise and effective manner. Despite that, Daniel Molnar and Peter Fabian started the Pipeline Academy to do exactly that. In this episode they reflect on the lessons that they learned while teaching the first cohort of their bootcamp how to be effective data engineers. By focusing on the fundamentals, and making everyone write code, they were able to build confidence and impart the importance of context for their students.

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 managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & 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 Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Daniel Molnar and Peter Fabian about the lessons that they learned from their first cohort at the Pipeline data engineering academy

Interview

Introduction How did you get involved in the area of data management? Can you start by sharing the curriculum and learning goals for the students? How did you set a common baseline for all of the students to build from throughout the program?

What was your process for determining the structure of the tasks and the tooling used?

What were some of the topics/tools that the students had the most difficulty with?

What topics/tools were the easiest to grasp?

What are some difficulties that you encountered while trying to teach different concepts? How did you deal with the tension of teaching the fundamentals while tying them to toolchains that hiring managers are looking for? What are the successes that you had with this cohort and what changes are you making to your approach/curriculum to build on them? What are some of the failures that you encountered and what lessons have you taken from them? How did the pandemic impact your overall plan and execution of the initial cohort? What were the skills that you focused on for interview preparation? What level of ongoing support/engagement do you have with students once they complete the curriculum? What are the most interesting, innovative, or unexpected solutions that you saw from your students? What are the most interesting, unexpected, or challenging lessons that you have learned while working with your first cohort? When is a bootcamp the wrong approach for skill development? What do you have planned for the future of the Pipeline Academy?

Contact Info

Daniel

LinkedIn Website @soobrosa on Twitter

Peter

LinkedIn

Parting Question

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

Links

Pipeline Academy

Blog

Scikit Pandas Urchin Kafka Three "C"s – Context, Confidence, and Code Prefect

Podcast Episode

Great Expectations

Podcast Episode Podcast.init Episode

Docker Kubernetes Become a Data Engineer On A Shoestring James Mickens

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

Support Data Engineering Podcast

Summary Data integration is a critical piece of every data pipeline, yet it is still far from being a solved problem. There are a number of managed platforms available, but the list of options for an open source system that supports a large variety of sources and destinations is still embarrasingly short. The team at Airbyte is adding a new entry to that list with the goal of making robust and easy to use data integration more accessible to teams who want or need to maintain full control of their data. In this episode co-founders John Lafleur and Michel Tricot share the story of how and why they created Airbyte, discuss the project’s design and architecture, and explain their vision of what an open soure data integration platform should offer. If you are struggling to maintain your extract and load pipelines or spending time on integrating with a new system when you would prefer to be working on other projects then this is definitely a conversation worth listening to.

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 managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. 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. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Your host is Tobias Macey and today I’m interviewing Michel Tricot and John Lafleur about Airbyte, an open source framework for building data integration pipelines.

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Airbyte is and the story behind it? Businesses and data engineers have a variety of options for how to manage their data integration. How would you characterize the overall landscape and how does Airbyte distinguish itself in that space? How would you characterize your target users?

How have those personas instructed the priorities and design of Airbyte? What do you see as the benefits and tradeoffs of a UI oriented data integration platform as compared to a code first approach?

what are the complex/challenging elements of data integration that makes it such a slippery problem? motivation for creating open source ELT as a business Can you describe how the Airbyte platform is implemented?

What was your motivation for choosing Java as the primary language?

incidental complexity of forcing all connectors to be packaged as containers shortcomings of the Singer specification/motivation for creating a backwards incompatible interface perceived potential for community adoption of Airbyte specification tradeoffs of using JSON as interchange format vs. e.g. protobuf/gRPC/Avro/etc.

information lost when converting records to JSON types/how to preserve that information (e.g. field constraints, valid enums, etc.)

interfaces/extension points for integrating with other tools, e.g. Dagster abstraction layers for simplifying implementation of new connectors tradeoffs of storing all connectors in a monorepo with the Airbyte core

impact of community adoption/contributions

What is involved in setting up an Airbyte installation? What are the available axes for scaling an Airbyte deployment? challenges of setting up and maintaining CI environment for Airbyte How are you managing governance and long term sustainability of the project? What are some of the most interesting, unexpected, or innovative ways that you have seen Airbyte used? What are the most interesting, unexpected, or challenging lessons that you have learned while building Airbyte? When is Airbyte the wrong choice? What do you have planned for the future of the project?

Contact Info

Michel

LinkedIn @MichelTricot on Twitter michel-tricot on GitHub

John

LinkedIn @JeanLafleur on Twitter johnlafleur on GitHub

Parting Question

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

Links

Airbyte Liveramp Fivetran

Podcast Episode

Stitch Data Matillion DataCoral

Podcast Episode

Singer Meltano

Podcast Episode

Airflow

Podcast.init Episode

Kotlin Docker Monorepo Airbyte Specification Great Expectations

Podcast Episode

Dagster

Data Engineering Podcast Episode Podcast.init Episode

Prefect

Podcast Episode

DBT

Podcast Episode

Kubernetes Snowflake

Podcast Episode

Redshift Presto Spark Parquet

Podcast Episode

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

Support Data Engineering Podcast

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools. At WeWork they needed a system that would provide visibility into their Airflow pipelines and the outputs produced. In this episode Julien Le Dem and Willy Lulciuc explain how they built Marquez to serve that need, how it is architected, and how it compares to other options that you might be considering. Even if you already have a metadata repository this is worth a listen to learn more about the value that visibility of your data can bring to your organization.

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 work hard to make sure that your data is clean, reliable, and reproducible throughout the ingestion pipeline, but what happens when it gets to the data warehouse? Dataform picks up where your ETL jobs leave off, turning raw data into reliable analytics. Their web based transformation tool with built in collaboration features lets your analysts own the full lifecycle of data in your warehouse. Featuring built in version control integration, real-time error checking for their SQL code, data quality tests, scheduling, and a data catalog with annotation capabilities it’s everything you need to keep your data warehouse in order. Sign up for a free trial today at dataengineeringpodcast.com/dataform and email [email protected] with the subject "Data Engineering Podcast" to get a hands-on demo from one of their data experts. 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, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference, the Strata Data conference, and PyCon US. 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 Willy Lulciuc and Julien Le Dem about Marquez, an open source platform to collect, aggregate, and visualize a data ecosystem’s metadata

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Marquez is?

What was missing in existing metadata management platforms that necessitated the creation of Marquez?

How do the capabilities of Marquez compare with tools and services that bill themselves as data catalogs?

How does it compare to the Amundsen platform that Lyft recently released?

What are some of the tools or platforms that are currently integrated with Marquez and what additional integrations would you like to see? What are some of the capabilities that are unique to Marquez and how are you using them at WeWork? What are the primary resource types that you support in Marquez?

What are some of the lowest common denominator attributes that are necessary and useful to track in a metadata repository?

Can you explain how Marquez is architected and how the design has evolved since you first began working on it?

Many metadata management systems are simply a service layer on top of a separate data storage engine. What are the benefits of using PostgreSQL as the system of record for Marquez?

What are some of the complexities that arise from relying on a relational engine as opposed to a document store or graph database?

How is the metadata itself stored and managed in Marquez?

How much up-front data modeling is necessary and what types of schema representations are supported?

Can you talk through the overall workflow of someone using Marquez in their environment?

What is involved in registering and updating datasets? How do you define and track the health of a given dataset? What are some of the interesting questions that can be answered from the information stored in Marquez?

What were your assumptions going into this project and how have they been challenged or updated as you began using it for production use cases? For someone who is interested in using Marquez what is involved in deploying and maintaining an installation of it? What have you found to be the most challenging or unanticipated aspects of building and maintaining a metadata repository and data discovery platform? When is Marquez the wrong choice for a metadata repository? What do you have planned for the future of Marquez?

Contact Info

Julien Le Dem

@J_ on Twitter Email julienledem on GitHub

Willy

LinkedIn @wslulciuc on Twitter wslulciuc on GitHub

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 show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. 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 iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Marquez

DataEngConf Presentation

WeWork Canary Yahoo Dremio Hadoop Pig Parquet

Podcast Episode

Airflow Apache Atlas Amundsen

Podcast Episode

Uber DataBook LinkedIn DataHub Iceberg Table Format

Podcast Episode

Delta Lake

Podcast Episode

Great Expectations data pipeline unit testing framework

Podcast.init Episode

Redshift SnowflakeDB

Podcast Episode

Apache Kafka Schema Registry

Podcast Episode

Open Tracing Jaeger Zipkin DropWizard Java framework Marquez UI Cayley Graph Database Kubernetes Marquez Helm Chart Marquez Docker Container Dagster

Podcast Episode

Luigi DBT

Podcast Episode

Thrift Protocol Buffers

The intro and outro music is from a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug?utm_source=rss&utm_medium=rss"…

Summary

With the attention being paid to the systems that power large volumes of high velocity data it is easy to forget about the value of data collection at human scales. Ona is a company that is building technologies to support mobile data collection, analysis of the aggregated information, and user-friendly presentations. In this episode CTO Peter Lubell-Doughtie describes the architecture of the platform, the types of environments and use cases where it is being employed, and the value of small data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Peter Lubell-Doughtie about using Ona for collecting data and processing it with Canopy

Interview

Introduction How did you get involved in the area of data management? What is Ona and how did the company get started?

What are some examples of the types of customers that you work with?

What types of data do you support in your collection platform? What are some of the mechanisms that you use to ensure the accuracy of the data that is being collected by users? Does your mobile collection platform allow for anyone to submit data without having to be associated with a given account or organization? What are some of the integration challenges that are unique to the types of data that get collected by mobile field workers? Can you describe the flow of the data from collection through to analysis? To help improve the utility of the data being collected you have started building Canopy. What was the tipping point where it became worth the time and effort to start that project?

What are the architectural considerations that you factored in when designing it? What have you found to be the most challenging or unexpected aspects of building an enterprise data warehouse for general users?

What are your plans for the future of Ona and Canopy?

Contact Info

Email pld on Github Website

Parting Question

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

Links

OpenSRP Ona Canopy Open Data Kit Earth Institute at Columbia University Sustainable Engineering Lab WHO Bill and Melinda Gates Foundation XLSForms PostGIS Kafka Druid Superset Postgres Ansible Docker Terraform

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

Summary

Data integration and routing is a constantly evolving problem and one that is fraught with edge cases and complicated requirements. The Apache NiFi project models this problem as a collection of data flows that are created through a self-service graphical interface. This framework provides a flexible platform for building a wide variety of integrations that can be managed and scaled easily to fit your particular needs. In this episode project members Kevin Doran and Andy LoPresto discuss the ways that NiFi can be used, how to start using it in your environment, and plans for future development. They also explained how it fits in the broad landscape of data tools, the interesting and challenging aspects of the project, and how to build new extensions.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kevin Doran and Andy LoPresto about Apache NiFi

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what NiFi is? What is the motivation for building a GUI as the primary interface for the tool when the current trend is to represent everything as code? How did you get involved with the project?

Where does it sit in the broader landscape of data tools?

Does the data that is processed by NiFi flow through the servers that it is running on (á la Spark/Flink/Kafka), or does it orchestrate actions on other systems (á la Airflow/Oozie)?

How do you manage versioning and backup of data flows, as well as promoting them between environments?

One of the advertised features is tracking provenance for data flows that are managed by NiFi. How is that data collected and managed?

What types of reporting are available across this information?

What are some of the use cases or requirements that lend themselves well to being solved by NiFi?

When is NiFi the wrong choice?

What is involved in deploying and scaling a NiFi installation?

What are some of the system/network parameters that should be considered? What are the scaling limitations?

What have you found to be some of the most interesting, unexpected, and/or challenging aspects of building and maintaining the NiFi project and community? What do you have planned for the future of NiFi?

Contact Info

Kevin Doran

@kevdoran on Twitter Email

Andy LoPresto

@yolopey on Twitter Email

Parting Question

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

Links

NiFi HortonWorks DataFlow HortonWorks Apache Software Foundation Apple CSV XML JSON Perl Python Internet Scale Asset Management Documentum DataFlow NSA (National Security Agency) 24 (TV Show) Technology Transfer Program Agile Software Development Waterfall Spark Flink Kafka Oozie Luigi Airflow FluentD ETL (Extract, Transform, and Load) ESB (Enterprise Service Bus) MiNiFi Java C++ Provenance Kubernetes Apache Atlas Data Governance Kibana K-Nearest Neighbors DevOps DSL (Domain Specific Language) NiFi Registry Artifact Repository Nexus NiFi CLI Maven Archetype IoT Docker Backpressure NiFi Wiki TLS (Transport Layer Security) Mozilla TLS Observatory NiFi Flow Design System Data Lineage GDPR (General Data Protection Regulation)

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

Summary

Collaboration, distribution, and installation of software projects is largely a solved problem, but the same cannot be said of data. Every data team has a bespoke means of sharing data sets, versioning them, tracking related metadata and changes, and publishing them for use in the software systems that rely on them. The CEO and founder of Quilt Data, Kevin Moore, was sufficiently frustrated by this problem to create a platform that attempts to be the means by which data can be as collaborative and easy to work with as GitHub and your favorite programming language. In this episode he explains how the project came to be, how it works, and the many ways that you can start using it today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kevin Moore about Quilt Data, a platform and tooling for packaging, distributing, and versioning data

Interview

Introduction How did you get involved in the area of data management? What is the intended use case for Quilt and how did the project get started? Can you step through a typical workflow of someone using Quilt?

How does that change as you go from a single user to a team of data engineers and data scientists?

Can you describe the elements of what a data package consists of?

What was your criteria for the file formats that you chose?

How is Quilt architected and what have been the most significant changes or evolutions since you first started? How is the data registry implemented?

What are the limitations or edge cases that you have run into? What optimizations have you made to accelerate synchronization of the data to and from the repository?

What are the limitations in terms of data volume, format, or usage? What is your goal with the business that you have built around the project? What are your plans for the future of Quilt?

Contact Info

Email LinkedIn

Parting Question

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

Links

Quilt Data GitHub Jobs Reproducible Data Dependencies in Jupyter Reproducible Machine Learning with Jupyter and Quilt Allen Institute: Programmatic Data Access with Quilt Quilt Example: MissingNo Oracle Pandas Jupyter Ycombinator Data.World

Podcast Episode with CTO Bryon Jacob

Kaggle Parquet HDF5 Arrow PySpark Excel Scala Binder Merkle Tree Allen Institute for Cell Science Flask PostGreSQL Docker Airflow Quilt Teams Hive Hive Metastore PrestoDB

Podcast Episode

Netflix Iceberg Kubernetes Helm

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

Summary

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

Preamble

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

Interview

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

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

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

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

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

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

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

Contact Info

Peter

LinkedIn petermattis on GitHub @petermattis on Twitter

Cockroach Labs

@CockroackDB on Twitter Website cockroachdb on GitHub

Parting Question

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

Links

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

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

Summary

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

Preamble

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

Interview

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

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

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

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

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

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

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

Contact Info

LinkedIn @yairwein on Twitter

Parting Question

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

Links

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

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Summary

As software lifecycles move faster, the database needs to be able to keep up. Practices such as version controlled migration scripts and iterative schema evolution provide the necessary mechanisms to ensure that your data layer is as agile as your application. Pramod Sadalage saw the need for these capabilities during the early days of the introduction of modern development practices and co-authored a book to codify a large number of patterns to aid practitioners, and in this episode he reflects on the current state of affairs and how things have changed over the past 12 years.

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 Pramod Sadalage about refactoring databases and integrating database design into an iterative development workflow

Interview

Introduction How did you get involved in the area of data management? You first co-authored Refactoring Databases in 2006. What was the state of software and database system development at the time and why did you find it necessary to write a book on this subject? What are the characteristics of a database that make them more difficult to manage in an iterative context? How does the practice of refactoring in the context of a database compare to that of software? How has the prevalence of data abstractions such as ORMs or ODMs impacted the practice of schema design and evolution? Is there a difference in strategy when refactoring the data layer of a system when using a non-relational storage system? How has the DevOps movement and the increased focus on automation affected the state of the art in database versioning and evolution? What have you found to be the most problematic aspects of databases when trying to evolve the functionality of a system? Looking back over the past 12 years, what has changed in the areas of database design and evolution?

How has the landscape of tooling for managing and applying database versioning changed since you first wrote Refactoring Databases? What do you see as the biggest challenges facing us over the next few years?

Contact Info

Website pramodsadalage on GitHub @pramodsadalage on Twitter

Parting Question

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

Links

Database Refactoring

Website Book

Thoughtworks Martin Fowler Agile Software Development XP (Extreme Programming) Continuous Integration

The Book Wikipedia

Test First Development DDL (Data Definition Language) DML (Data Modification Language) DevOps Flyway Liquibase DBMaintain Hibernate SQLAlchemy ORM (Object Relational Mapper) ODM (Object Document Mapper) NoSQL Document Database MongoDB OrientDB CouchBase CassandraDB Neo4j ArangoDB Unit Testing Integration Testing OLAP (On-Line Analytical Processing) OLTP (On-Line Transaction Processing) Data Warehouse Docker QA==Quality Assurance HIPAA (Health Insurance Portability and Accountability Act) PCI DSS (Payment Card Industry Data Security Standard) Polyglot Persistence Toplink Java ORM Ruby on Rails ActiveRecord Gem

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

Summary

As communications between machines become more commonplace the need to store the generated data in a time-oriented manner increases. The market for timeseries data stores has many contenders, but they are not all built to solve the same problems or to scale in the same manner. In this episode the founders of TimescaleDB, Ajay Kulkarni and Mike Freedman, discuss how Timescale was started, the problems that it solves, and how it works under the covers. They also explain how you can start using it in your infrastructure and their plans for the future.

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 Ajay Kulkarni and Mike Freedman about Timescale DB, a scalable timeseries database built on top of PostGreSQL

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Timescale is and how the project got started? The landscape of time series databases is extensive and oftentimes difficult to navigate. How do you view your position in that market and what makes Timescale stand out from the other options? In your blog post that explains the design decisions for how Timescale is implemented you call out the fact that the inserted data is largely append only which simplifies the index management. How does Timescale handle out of order timestamps, such as from infrequently connected sensors or mobile devices? How is Timescale implemented and how has the internal architecture evolved since you first started working on it?

What impact has the 10.0 release of PostGreSQL had on the design of the project? Is timescale compatible with systems such as Amazon RDS or Google Cloud SQL?

For someone who wants to start using Timescale what is involved in deploying and maintaining it? What are the axes for scaling Timescale and what are the points where that scalability breaks down?

Are you aware of anyone who has deployed it on top of Citus for scaling horizontally across instances?

What has been the most challenging aspect of building and marketing Timescale? When is Timescale the wrong tool to use for time series data? One of the use cases that you call out on your website is for systems metrics and monitoring. How does Timescale fit into that ecosystem and can it be used along with tools such as Graphite or Prometheus? What are some of the most interesting uses of Timescale that you have seen? Which came first, Timescale the business or Timescale the database, and what is your strategy for ensuring that the open source project and the company around it both maintain their health? What features or improvements do you have planned for future releases of Timescale?

Contact Info

Ajay

LinkedIn @acoustik on Twitter Timescale Blog

Mike

Website LinkedIn @michaelfreedman on Twitter Timescale Blog

Timescale

Website @timescaledb on Twitter GitHub

Parting Question

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

Links

Timescale PostGreSQL Citus Timescale Design Blog Post MIT NYU Stanford SDN Princeton Machine Data Timeseries Data List of Timeseries Databases NoSQL Online Transaction Processing (OLTP) Object Relational Mapper (ORM) Grafana Tableau Kafka When Boring Is Awesome PostGreSQL RDS Google Cloud SQL Azure DB Docker Continuous Aggregates Streaming Replication PGPool II Kubernetes Docker Swarm Citus Data

Website Data Engineering Podcast Interview

Database Indexing B-Tree Index GIN Index GIST Index STE Energy Redis Graphite Prometheus pg_prometheus OpenMetrics Standard Proposal Timescale Parallel Copy Hadoop PostGIS KDB+ DevOps Internet of Things MongoDB Elastic DataBricks Apache Spark Confluent New Enterprise Associates MapD Benchmark Ventures Hortonworks 2σ Ventures CockroachDB Cloudflare EMC Timescale Blog: Why SQL is beating NoSQL, and what this means for the future of data

The intro and outro music is from a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug?utm_source=rss&utm_medium=rss" target="_blank"…

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

Summary

Data oriented applications that need to operate on large, fast-moving sterams of information can be difficult to build and scale due to the need to manage their state. In this episode Sean T. Allen, VP of engineering for Wallaroo Labs, explains how Wallaroo was designed and built to reduce the cognitive overhead of building this style of project. He explains the motivation for building Wallaroo, how it is implemented, and how you can start using it today.

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. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. 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 Sean T. Allen about Wallaroo, a framework for building and operating stateful data applications at scale

Interview

Introduction How did you get involved in the area of data engineering? What is Wallaroo and how did the project get started? What is the Pony language, and what features does it have that make it well suited for the problem area that you are focusing on? Why did you choose to focus first on Python as the language for interacting with Wallaroo and how is that integration implemented? How is Wallaroo architected internally to allow for distributed state management?

Is the state persistent, or is it only maintained long enough to complete the desired computation? If so, what format do you use for long term storage of the data?

What have been the most challenging aspects of building the Wallaroo platform? Which axes of the CAP theorem have you optimized for? For someone who wants to build an application on top of Wallaroo, what is involved in getting started? Once you have a working application, what resources are necessary for deploying to production and what are the scaling factors?

What are the failure modes that users of Wallaroo need to account for in their application or infrastructure?

What are some situations or problem types for which Wallaroo would be the wrong choice? What are some of the most interesting or unexpected uses of Wallaroo that you have seen? What do you have planned for the future of Wallaroo?

Contact Info

IRC Mailing List Wallaroo Labs Twitter Email Personal Twitter

Parting Question

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

Links

Wallaroo Labs Storm Applied Apache Storm Risk Analysis Pony Language Erlang Akka Tail Latency High Performance Computing Python Apache Software Foundation Beyond Distributed Transactions: An Apostate’s View Consistent Hashing Jepsen Lineage Driven Fault Injection Chaos Engineering QCon 2016 Talk Codemesh in London: How did I get here? CAP Theorem CRDT Sync Free Project Basho Wallaroo on GitHub Docker Puppet Chef Ansible SaltStack Kafka TCP Dask Data Engineering Episode About Dask Beowulf Cluster Redis Flink Haskell

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

Summary

Building a data pipeline that is reliable and flexible is a difficult task, especially when you have a small team. Astronomer is a platform that lets you skip straight to processing your valuable business data. Ry Walker, the CEO of Astronomer, explains how the company got started, how the platform works, and their commitment to open source.

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 www.dataengineeringpodcast.com/linode?utm_source=rss&utm_medium=rss 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 This is your host Tobias Macey and today I’m interviewing Ry Walker, CEO of Astronomer, the platform for data engineering.

Interview

Introduction How did you first get involved in the area of data management? What is Astronomer and how did it get started? Regulatory challenges of processing other people’s data What does your data pipelining architecture look like? What are the most challenging aspects of building a general purpose data management environment? What are some of the most significant sources of technical debt in your platform? Can you share some of the failures that you have encountered while architecting or building your platform and company and how you overcame them? There are certain areas of the overall data engineering workflow that are well defined and have numerous tools to choose from. What are some of the unsolved problems in data management? What are some of the most interesting or unexpected uses of your platform that you are aware of?

Contact Information

Email @rywalker on Twitter

Links

Astronomer Kiss Metrics Segment Marketing tools chart Clickstream HIPAA FERPA PCI Mesos Mesos DC/OS Airflow SSIS Marathon Prometheus Grafana Terraform Kafka Spark ELK Stack React GraphQL PostGreSQL MongoDB Ceph Druid Aries Vault Adapter Pattern Docker Kinesis API Gateway Kong AWS Lambda Flink Redshift NOAA Informatica SnapLogic Meteor

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

Summary

Do you wish that you could track the changes in your data the same way that you track the changes in your code? Pachyderm is a platform for building a data lake with a versioned file system. It also lets you use whatever languages you want to run your analysis with its container based task graph. This week Daniel Whitenack shares the story of how the project got started, how it works under the covers, and how you can get started using it today!

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure 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 Daniel Whitenack about Pachyderm, a modern container based system for building and analyzing a versioned data lake.

Interview with Daniel Whitenack

Introduction How did you get started in the data engineering space? What is pachyderm and what problem were you trying to solve when the project was started? Where does the name come from? What are some of the competing projects in the space and what features does Pachyderm offer that would convince someone to choose it over the other options? Because of the fact that the analysis code and the data that it acts on are all versioned together it allows for tracking the provenance of the end result. Why is this such an important capability in the context of data engineering and analytics? What does Pachyderm use for the distribution and scaling mechanism of the file system? Given that you can version your data and track all of the modifications made to it in a manner that allows for traversal of those changesets, how much additional storage is necessary over and above the original capacity needed for the raw data? For a typical use of Pachyderm would someone keep all of the revisions in perpetuity or are the changesets primarily just useful in the context of an analysis workflow? Given that the state of the data is calculated by applying the diffs in sequence what impact does that have on processing speed and what are some of the ways of mitigating that? Another compelling feature of Pachyderm is the fact that it natively supports the use of any language for interacting with your data. Why is this such an important capability and why is it more difficult with alternative solutions?

How did you implement this feature so that it would be maintainable and easy to implement for end users?

Given that the intent of using containers is for encapsulating the analysis code from experimentation through to production, it seems that there is the potential for the implementations to run into problems as they scale. What are some things that users should be aware of to help mitigate this? The data pipeline and dependency graph tooling is a useful addition to the combination of file system and processing interface. Does that preclude any requirement for external tools such as Luigi or Airflow? I see that the docs mention using the map reduce pattern for analyzing the data in Pachyderm. Does it support other approaches such as streaming or tools like Apache Drill? What are some of the most interesting deployments and uses of Pachyderm that you have seen? What are some of the areas that you are looking for help from the community and are there any particular issues that the listeners can check out to get started with the project?

Keep in touch

Daniel

Twitter – @dwhitena

Pachyderm

Website

Free Weekend Project

GopherNotes

Links

AirBnB RethinkDB Flocker Infinite Project Git LFS Luigi Airflow Kafka Kubernetes Rkt SciKit Learn Docker Minikube General Fusion

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