Summary There is a lot of attention on the database market and cloud data warehouses. While they provide a measure of convenience, they also require you to sacrifice a certain amount of control over your data. If you want to build a warehouse that gives you both control and flexibility then you might consider building on top of the venerable PostgreSQL project. In this episode Thomas Richter and Joshua Drake share their advice on how to build a production ready data warehouse with Postgres.

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! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. 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 Thomas Richter and Joshua Drake about using Postgres as your data warehouse

Interview

Introduction How did you get involved in the area of data management? Can you start by establishing a working definition of what constitutes a data warehouse for the purpose of this discussion?

What are the limitations for out-of-the-box Postgres when trying to use it for these workloads?

There are a large and growing number of options for data warehouse style workloads. How would you categorize the different systems and what is PostgreSQL’s position in that ecosystem?

What do you see as the motivating factors for a team or organization to select from among those categories?

Why would someone want to use Postgres as their data warehouse platform rather than using a purpose-built engine? What is the cost/performance equation for Postgres as compared to other data warehouse solutions? For someone who wants to turn Postgres into a data warehouse engine, what are their options?

What are the relative tradeoffs of the different open source and commercial offerings? (e.g. Citus, cstore_fdw, zedstore, Swarm64, Greenplum, etc.)

One of the biggest areas of growth right now is in the "cloud data warehouse" market where storage and compute are decoupled. What are the options for making that possible with Postgres? (e.g. using foreign data wrappers for interacting with data lake storage (S3, HDFS, Alluxio, etc.)) What areas of work are happening in the Postgres community for upcoming releases to make it more easily suited to data warehouse/analytical workloads? What are some of the most interesting, innovative, or unexpected ways that you have seen Postgres used in analytical contexts? What are the most interesting, unexpected, or challenging lessons that you have learned from your own experiences of building analytical systems with Postgres? When is Postgres the wrong choice fo

Summary The scale and complexity of the systems that we build to satisfy business requirements is increasing as the available tools become more sophisticated. In order to bridge the gap between legacy infrastructure and evolving use cases it is necessary to create a unifying set of components. In this episode Dipti Borkar explains how the emerging category of data orchestration tools fills this need, some of the existing projects that fit in this space, and some of the ways that they can work together to simplify projects such as cloud migration and hybrid cloud environments. It is always useful to get a broad view of new trends in the industry and this was a helpful perspective on the need to provide mechanisms to decouple physical storage from computing capacity.

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, 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, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral today to find out more. 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 Dipti Borkark about data orchestration and how it helps in migrating data workloads to the cloud

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you mean by the term "Data Orchestration"?

How does it compare to the concept of "Data Virtualization"? What are some of the tools and platforms that fit under that umbrella?

What are some of the motivations for organizations to use the cloud for their data oriented workloads?

What are they giving up by using cloud resources in place of on-premises compute?

For businesses that have invested heavily in their own datacenters, what are some ways that they can begin to replicate some of the benefits of cloud environments? What are some of the common patterns for cloud migration projects and what challenges do they present?

Do you have advice on useful metrics to track for determining project completion or success criteria?

How do businesses approach employee education for designing and implementing effective systems for achieving their migration goals? Can you talk through some of the ways that different data orchestration tools can be composed together for a cloud migration effort?

What are some of the common pain points that organizations encounter when working on hybrid implementations?

What are some of the missing pieces in the data orchestration landscape?

Are there any efforts that you are aware of that are aiming to fill those gaps?

Where is the data orchestration market heading, and what are some industry trends that are driving it?

What projects are you most interested in or excited by?

For someone who wants to learn more about data orchestration and the benefits the technologies can provide, what are some resources that you would recommend?

Contact Info

LinkedIn @dborkar 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 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

Alluxio

Podcast Episode

UC San Diego Couchbase Presto

Podcast Episode

Spark SQL Data Orchestration Data Virtualization PyTorch

Podcast.init Episode

Rook storage orchestration PySpark MinIO

Podcast Episode

Kubernetes Openstack Hadoop HDFS Parquet Files

Podcast Episode

ORC Files Hive Metastore Iceberg Table Format

Podcast Episode

Data Orchestration Summit Star Schema Snowflake Schema Data Warehouse Data Lake Teradata

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

Support Data Engineering Podcast

Summary Object storage is quickly becoming the unifying layer for data intensive applications and analytics. Modern, cloud oriented data warehouses and data lakes both rely on the durability and ease of use that it provides. S3 from Amazon has quickly become the de-facto API for interacting with this service, so the team at MinIO have built a production grade, easy to manage storage engine that replicates that interface. In this episode Anand Babu Periasamy shares the origin story for the MinIO platform, the myriad use cases that it supports, and the challenges that they have faced in replicating the functionality of S3. He also explains the technical implementation, innovative design, and broad vision for the project.

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, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. 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 Anand Babu Periasamy about MinIO, the neutral, open source, enterprise grade object storage system.

Interview

Introduction How did you get involved in the area of data management? Can you explain what MinIO is and its origin story? What are some of the main use cases that MinIO enables? How does MinIO compare to other object storage options and what benefits does it provide over other open source platforms?

Your marketing focuses on the utility of MinIO for ML and AI workloads. What benefits does object storage provide as compared to distributed file systems? (e.g. HDFS, GlusterFS, Ceph)

What are some of the challenges that you face in terms of maintaining compatibility with the S3 interface?

What are the constraints and opportunities that are provided by adhering to that API?

Can you describe how MinIO is implemented and the overall system design?

How has that design evolved since you first began working on it?

What assumptions did you have at the outset and how have they been challenged or updated?

What are the axes for scaling that MinIO provides and how does it handle clustering?

Where does it fall on the axes of availability and consistency in the CAP theorem?

One of the useful features that you provide is efficient erasure coding, as well as protection against data corruption. How much overhead do those capabilties incur, in terms of computational efficiency and, in a clustered scenario, storage volume? For someone who is interested in running MinIO, what is involved in deploying and maintain

Summary

The Hadoop platform is purpose built for processing large, slow moving data in long-running batch jobs. As the ecosystem around it has grown, so has the need for fast data analytics on fast moving data. To fill this need the Kudu project was created with a column oriented table format that was tuned for high volumes of writes and rapid query execution across those tables. For a perfect pairing, they made it easy to connect to the Impala SQL engine. In this episode Brock Noland and Jordan Birdsell from PhData explain how Kudu is architected, how it compares to other storage systems in the Hadoop orbit, and how to start integrating it into you analytics pipeline.

Preamble

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 Brock Noland and Jordan Birdsell about Apache Kudu and how it is able to provide fast analytics on fast data in the Hadoop ecosystem

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Kudu is and the motivation for building it?

How does it fit into the Hadoop ecosystem? How does it compare to the work being done on the Iceberg table format?

What are some of the common application and system design patterns that Kudu supports? How is Kudu architected and how has it evolved over the life of the project? There are many projects in and around the Hadoop ecosystem that rely on Zookeeper as a building block for consensus. What was the reasoning for using Raft in Kudu? How does the storage layer in Kudu differ from what would be found in systems like Hive or HBase?

What are the implementation details in the Kudu storage interface that have had the greatest impact on its overall speed and performance?

A number of the projects built for large scale data processing were not initially built with a focus on operational simplicity. What are the features of Kudu that simplify deployment and management of production infrastructure? What was the motivation for using C++ as the language target for Kudu?

If you were to start the project over today what would you do differently?

What are some situations where you would advise against using Kudu? What have you found to be the most interesting/unexpected/challenging lessons learned in the process of building and maintaining Kudu? What are you most excited about for the future of Kudu?

Contact Info

Brock

LinkedIn @brocknoland on Twitter

Jordan

LinkedIn @jordanbirdsell jbirdsell on GitHub

PhData

Website phdata on GitHub @phdatainc on Twitter

Parting Question

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

Links

Kudu PhData Getting Started with Apache Kudu Thomson Reuters Hadoop Oracle Exadata Slowly Changing Dimensions HDFS S3 Azure Blob Storage State Farm Stanly Black & Decker ETL (Extract, Transform, Load) Parquet

Podcast Episode

ORC HBase Spark

Podcast Episode

Summary

With the growth of the Hadoop ecosystem came a proliferation of implementations for the Hive table format. Unfortunately, with no formal specification, each project works slightly different which increases the difficulty of integration across systems. The Hive format is also built with the assumptions of a local filesystem which results in painful edge cases when leveraging cloud object storage for a data lake. In this episode Ryan Blue explains how his work on the Iceberg table format specification and reference implementation has allowed Netflix to improve the performance and simplify operations for their S3 data lake. This is a highly detailed and technical exploration of how a well-engineered metadata layer can improve the speed, accuracy, and utility of large scale, multi-tenant, cloud-native data platforms.

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. 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 Ryan Blue about Iceberg, a Netflix project to implement a high performance table format for batch workloads

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Iceberg is and the motivation for creating it?

Was the project built with open-source in mind or was it necessary to refactor it from an internal project for public use?

How has the use of Iceberg simplified your work at Netflix? How is the reference implementation architected and how has it evolved since you first began work on it?

What is involved in deploying it to a user’s environment?

For someone who is interested in using Iceberg within their own environments, what is involved in integrating it with their existing query engine?

Is there a migration path for pre-existing tables into the Iceberg format?

How is schema evolution managed at the file level?

How do you handle files on disk that don’t contain all of the fields specified in a table definition?

One of the complicated problems in data modeling is managing table partitions. How does Iceberg help in that regard? What are the unique challenges posed by using S3 as the basis for a data lake?

What are the benefits that outweigh the difficulties?

What have been some of the most challenging or contentious details of the specification to define?

What are some things that you have explicitly left out of the specification?

What are your long-term goals for the Iceberg specification?

Do you anticipate the reference implementation continuing to be used and maintained?

Contact Info

rdblue on GitHub LinkedIn

Parting Question

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

Links

Iceberg Reference Implementation Iceberg Table Specification Netflix Hadoop Cloudera Avro Parquet Spark S3 HDFS Hive ORC S3mper Git Metacat Presto Pig DDL (Data Definition Language) Cost-Based Optimization

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

Summary

When working with large volumes of data that you need to access in parallel across multiple instances you need a distributed filesystem that will scale with your workload. Even better is when that same system provides multiple paradigms for interacting with the underlying storage. Ceph is a highly available, highly scalable, and performant system that has support for object storage, block storage, and native filesystem access. In this episode Sage Weil, the creator and lead maintainer of the project, discusses how it got started, how it works, and how you can start using it on your infrastructure today. He also explains where it fits in the current landscape of distributed storage and the plans for future improvements.

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 Sage Weil about Ceph, an open source distributed file system that supports block storage, object storage, and a file system interface.

Interview

Introduction How did you get involved in the area of data management? Can you start with an overview of what Ceph is?

What was the motivation for starting the project? What are some of the most common use cases for Ceph?

There are a large variety of distributed file systems. How would you characterize Ceph as it compares to other options (e.g. HDFS, GlusterFS, LionFS, SeaweedFS, etc.)? Given that there is no single point of failure, what mechanisms do you use to mitigate the impact of network partitions?

What mechanisms are available to ensure data integrity across the cluster?

How is Ceph implemented and how has the design evolved over time? What is required to deploy and manage a Ceph cluster?

What are the scaling factors for a cluster? What are the limitations?

How does Ceph handle mixed write workloads with either a high volume of small files or a smaller volume of larger files? In services such as S3 the data is segregated from block storage options like EBS or EFS. Since Ceph provides all of those interfaces in one project is it possible to use each of those interfaces to the same data objects in a Ceph cluster? In what situations would you advise someone against using Ceph? What are some of the most interested, unexpected, or challenging aspects of working with Ceph and the community? What are some of the plans that you have for the future of Ceph?

Contact Info

Email @liewegas on Twitter liewegas on GitHub

Parting Question

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

Links

Ceph Red Hat DreamHo