Summary

As more companies and organizations are working to gain a real-time view of their business, they are increasingly turning to stream processing technologies to fullfill that need. However, the storage requirements for continuous, unbounded streams of data are markedly different than that of batch oriented workloads. To address this shortcoming the team at Dell EMC has created the open source Pravega project. In this episode Tom Kaitchuk explains how Pravega simplifies storage and processing of data streams, how it integrates with processing engines such as Flink, and the unique capabilities that it provides in the area of exactly once processing and transactions. And if you listen at approximately the half-way mark, you can hear as the hosts mind is blown by the possibilities of treating everything, including schema information, as a stream.

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 Tom Kaitchuck about Pravega, an open source data storage platform optimized for persistent streams

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Pravega is and the story behind it? What are the use cases for Pravega and how does it fit into the data ecosystem?

How does it compare with systems such as Kafka and Pulsar for ingesting and persisting unbounded data?

How do you represent a stream on-disk?

What are the benefits of using this format for persisted streams?

One of the compelling aspects of Pravega is the automatic sharding and resource allocation for variations in data patterns. Can you describe how that operates and the benefits that it provides? I am also intrigued by the automatic tiering of the persisted storage. How does that work and what options exist for managing the lifecycle of the data in the cluster? For someone who wants to build an application on top of Pravega, what interfaces does it provide and what architectural patterns does it lend itself toward? What are some of the unique system design patterns that are made possible by Pravega? How is Pravega architected internally? What is involved in integrating engines such as Spark, Flink, or Storm with Pravega? A common challenge for streaming systems is exactly once semantics. How does Pravega approach that problem?

Does it have any special capabilities for simplifying processing of out-of-order events?

For someone planning a deployment of Pravega, what is involved in building and scaling a cluster?

What are some of the operational edge cases that users should be aware of?

What are some of the most interesting, useful, or challenging experiences that you have had while building Pravega? What are some cases where you would recommend against using Pravega? What is in store for the future of Pravega?

Contact Info

tkaitchuk on GitHub LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooli

Send us a text Happy holidays from the Making Data Simple team! Enjoy a rebroadcast of a conversation with Seth Dobrin, Vice President and Chief Data Officer for IBM Analytics, as he and Al explore the strategies and people your company needs to disrupt and succeed in the year ahead. Do you or your team members need new credentials to work in data? Seth also discusses what you need in your toolkit to be a data scientist at IBM.

Show Notes 00.30 Connect with Al Martin on Twitter and LinkedIn. 01.00 Connect with Seth Dobrin on Twitter and LinkedIn. 01.40 Read "What IBM looks for in a Data Scientist" by Seth Dobrin and Jean-Francois Puget. 06.00 Learn more about GDPR.  13.00 Learn more about master data management. 13.05 Learn more about unified governance and integration.  13.25 Learn more about machine learning.  14.00 Connect and learn more about Ginni Rometty.  14.40 Learn more about cognitive computing. 19.35 Connect with Rob Thomas on Twitter and LinkedIn. 21.00 Connect with Jean-Francois Puget on Twitter and LinkedIn. Follow @IBMAnalytics Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary

Processing high velocity time-series data in real-time is a complex challenge. The team at PipelineDB has built a continuous query engine that simplifies the task of computing aggregates across incoming streams of events. In this episode Derek Nelson and Usman Masood explain how it is architected, strategies for designing your data flows, how to scale it up and out, and edge cases to be aware of.

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. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Usman Masood and Derek Nelson about PipelineDB, an open source continuous query engine for PostgreSQL

Interview

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

What are the major use cases that it enables? What are some example applications that are uniquely well suited to the capabilities of PipelineDB?

What are the major concepts and components that users of PipelineDB should be familiar with? Given the fact that it is a plugin for PostgreSQL, what level of compatibility exists between PipelineDB and other plugins such as Timescale and Citus? What are some of the common patterns for populating data streams? What are the options for scaling PipelineDB systems, both vertically and horizontally?

How much elasticity does the system support in terms of changing volumes of inbound data? What are some of the limitations or edge cases that users should be aware of?

Given that inbound data is not persisted to disk, how do you guard against data loss?

Is it possible to archive the data in a stream, unaltered, to a separate destination table or other storage location? Can a separate table be used as an input stream?

Since the data being processed by the continuous queries is potentially unbounded, how do you approach checkpointing or windowing the data in the continuous views? What are some of the features that you have found to be the most useful which users might initially overlook? What would be involved in generating an alert or notification on an aggregate output that was in some way anomalous? What are some of the most challenging aspects of building continuous aggregates on unbounded data? What have you found to be some of the most interesting, complex, or challenging aspects of building and maintaining PipelineDB? What are some of the most interesting or unexpected ways that you have seen PipelineDB used? When is PipelineDB the wrong choice? What do you have planned for the future of PipelineDB now that you have hit the 1.0 milestone?

Contact Info

Derek

derekjn on GitHub LinkedIn

Usman

@usmanm on Twitter Website

Parting Question

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

Links

PipelineDB Stride PostgreSQL

Podcast Episode

AdRoll Probabilistic Data Structures TimescaleDB

[Podcast Episode](

Hive Redshift Kafka Kinesis ZeroMQ Nanomsg HyperLogLog Bloom Filter

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

In this episode, Daniel Graham dissects the capabilities of data lakes and compares it to data warehouses. He talks about the primary use cases of data lakes and how they are vital for big data ecosystems. He then goes on to explain the role of data warehouses which are still responsible for timely and accurate data but don't have a central role anymore. In the end, both Wayne Eckerson and Dan Graham settle on a common definition for modern data architectures.

Daniel Graham has more than 30 years in IT, consulting, research, and product marketing, with almost 30 years at leading database management companies. Dan was a Strategy Director in IBM’s Global BI Solutions division and General Manager of Teradata’s high-end server divisions. During his tenure as a product marketer, Dan has been responsible for MPP data management systems, data warehouses, and data lakes, and most recently, the Internet of Things and streaming systems.

Recent technology developments are driving urgency to modernize data management. What do you do about architecture, modeling, quality, and governance to keep up with big data, cloud, self-service, and other trends in data and technology? Examining some best practices can spark ideas of where to begin.

Originally published at https://www.eckerson.com/articles/stepping-up-to-modern-data-management

Summary

Every business needs a pipeline for their critical data, even if it is just pasting into a spreadsheet. As the organization grows and gains more customers, the requirements for that pipeline will change. In this episode Christian Heinzmann, Head of Data Warehousing at Grubhub, discusses the various requirements for data pipelines and how the overall system architecture evolves as more data is being processed. He also covers the changes in how the output of the pipelines are used, how that impacts the expectations for accuracy and availability, and some useful advice on build vs. buy for the components of a data platform.

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. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Christian Heinzmann about how data pipelines evolve as your business grows

Interview

Introduction How did you get involved in the area of data management? Can you start by sharing your definition of a data pipeline?

At what point in the life of a project or organization should you start thinking about building a pipeline?

In the early stages when the scale of the data and business are still small, what are some of the design characteristics that you should be targeting for your pipeline?

What metrics/use cases should you be optimizing for at this point?

What are some of the indicators that you look for to signal that you are reaching the next order of magnitude in terms of scale?

How do the design requirements for a data pipeline change as you reach this stage? What are some of the challenges and complexities that begin to present themselves as you build and run your pipeline at medium scale?

What are some of the changes that are necessary as you move to a large scale data pipeline? At each level of scale it is important to minimize the impact of the ETL process on the source systems. What are some strategies that you have employed to avoid degrading the performance of the application systems? In recent years there has been a shift to using data lakes as a staging ground before performing transformations. What are your thoughts on that approach? When performing transformations there is a potential for discarding information or losing fidelity. How have you worked to reduce the impact of this effect? Transformations of the source data can be brittle when the format or volume changes. How do you design the pipeline to be resilient to these types of changes? What are your selection criteria when determining what workflow or ETL engines to use in your pipeline?

How has your preference of build vs buy changed at different scales of operation and as new/different projects become available?

What are some of the dead ends or edge cases that you have had to deal with in your current role at Grubhub? What are some of the common mistakes or overlooked aspects of building a data pipeline that you have seen? What are your plans for improving your current pipeline at Grubhub? What are some references that you recommend for anyone who is designing a new data platform?

Contact Info

@sirchristian on Twitter Blog sirchristian on GitHub

Par

Summary

Apache Spark is a popular and widely used tool for a variety of data oriented projects. With the large array of capabilities, and the complexity of the underlying system, it can be difficult to understand how to get started using it. Jean George Perrin has been so impressed by the versatility of Spark that he is writing a book for data engineers to hit the ground running. In this episode he helps to make sense of what Spark is, how it works, and the various ways that you can use it. He also discusses what you need to know to get it deployed and keep it running in a production environment and how it fits into the overall data ecosystem.

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. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Jean Georges Perrin, author of the upcoming Manning book Spark In Action 2nd Edition, about the ways that Spark is used and how it fits into the data landscape

Interview

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

What are some of the main use cases for Spark? What are some of the problems that Spark is uniquely suited to address? Who uses Spark?

What are the tools offered to Spark users? How does it compare to some of the other streaming frameworks such as Flink, Kafka, or Storm? For someone building on top of Spark what are the main software design paradigms?

How does the design of an application change as you go from a local development environment to a production cluster?

Once your application is written, what is involved in deploying it to a production environment? What are some of the most useful strategies that you have seen for improving the efficiency and performance of a processing pipeline? What are some of the edge cases and architectural considerations that engineers should be considering as they begin to scale their deployments? What are some of the common ways that Spark is deployed, in terms of the cluster topology and the supporting technologies? What are the limitations of the Spark programming model?

What are the cases where Spark is the wrong choice?

What was your motivation for writing a book about Spark?

Who is the target audience?

What have been some of the most interesting or useful lessons that you have learned in the process of writing a book about Spark? What advice do you have for anyone who is considering or currently using Spark?

Contact Info

@jgperrin on Twitter Blog

Parting Question

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

Book Discount

Use the code poddataeng18 to get 40% off of all of Manning’s products at manning.com

Links

Apache Spark Spark In Action Book code examples in GitHub Informix International Informix Users Group MySQL Microsoft SQL Server ETL (Extract, Transform, Load) Spark SQL and Spark In Action‘s chapter 11 Spark ML and Spark In Action‘s chapter 18 Spark Streaming (structured) and Spark In Action‘s chapter 10 Spark GraphX Hadoop Jupyter

Podcast Interview

Zeppelin Databricks IBM Watson Studio Kafka Flink

P

Data catalogs have become the centerpiece of modern data management. They are the means to connect self-service analysts with the right data, the “go to” technology for data curation, and the new gold standard for metadata management. With many data catalog tools available, choosing the right data catalog is an important decision.

Summary Distributed systems are complex to build and operate, and there are certain primitives that are common to a majority of them. Rather then re-implement the same capabilities every time, many projects build on top of Apache Zookeeper. In this episode Patrick Hunt explains how the Apache Zookeeper project was started, how it functions, and how it is used as a building block for other distributed systems. He also explains the operational considerations for running your own cluster, how it compares to more recent entrants such as Consul and EtcD, and what is in store for the future.

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. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Patrick Hunt about Apache Zookeeper and how it is used as a building block for distributed systems

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Zookeeper is and how the project got started?

What are the main motivations for using a centralized coordination service for distributed systems?

What are the distributed systems primitives that are built into Zookeeper?

What are some of the higher-order capabilities that Zookeeper provides to users who are building distributed systems on top of Zookeeper? What are some of the types of system level features that application developers will need which aren’t provided by Zookeeper?

Can you discuss how Zookeeper is architected and how that design has evolved over time?

What have you found to be some of the most complicated or difficult aspects of building and maintaining Zookeeper?

What are the scaling factors for Zookeeper?

What are the edge cases that users should be aware of? Where does it fall on the axes of the CAP theorem?

What are the main failure modes for Zookeeper?

How much of the recovery logic is left up to the end user of the Zookeeper cluster?

Since there are a number of projects that rely on Zookeeper, many of which are likely to be run in the same environment (e.g. Kafka and Flink), what would be involved in sharing a single Zookeeper cluster among those multiple services? In recent years we have seen projects such as EtcD which is used by Kubernetes, and Consul. How does Zookeeper compare with those projects?

What are some of the cases where Zookeeper is the wrong choice?

How have the needs of distributed systems engineers changed since you first began working on Zookeeper? If you were to start the project over today, what would you do differently?

Would you still use Java?

What are some of the most interesting or unexpected ways that you have seen Zookeeper used? What do you have planned for the future of Zookeeper?

Contact Info

@phunt on Twitter

Parting Question

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

Links

Zookeeper Cloudera Google Chubby Sourceforge HBase High Availability Fallacies of distributed computing Falsehoods programmers believe about networking Consul EtcD Apache Curator Raft Consensus Algorithm Zookeeper Atomic Broadcast SSD Write Cliff Apache Kafka Apache Flink

Podcast

This book, Hands-On Big Data Modeling, provides you with practical guidance on data modeling techniques, focusing particularly on the challenges of big data. You will learn the concepts behind various data models, explore tools and platforms for efficient data management, and gain hands-on experience with structured and unstructured data. What this Book will help me do Master the fundamental concepts of big data and its challenges. Explore advanced data modeling techniques using SQL, Python, and R. Design effective models for structured, semi-structured, and unstructured data types. Apply data modeling to real-world datasets like social media and sensor data. Optimize data models for performance and scalability in various big data platforms. Author(s) The authors of this book are experienced data architects and engineers with a strong background in developing scalable data solutions. They bring their collective expertise to simplify complex concepts in big data modeling, ensuring readers can effectively apply these techniques in their projects. Who is it for? This book is intended for data architects, business intelligence professionals, and any programmer interested in understanding and applying big data modeling concepts. If you are already familiar with basic data management principles and want to enhance your skills, this book is perfect for you. You will learn to tackle real-world datasets and create scalable models. Additionally, it is suitable for professionals transitioning to working with big data frameworks.

PostgreSQL 11 Server Side Programming Quick Start Guide introduces you to the world of database programming directly at the database level. This book delves into the concepts of server-side programming, providing you with the necessary tools to author stored procedures, triggers, and extensions for your PostgreSQL instance. What this Book will help me do Learn how to create stored procedures and functions for efficient database logic. Understand how to use triggers and rules to maintain data integrity. Gain expertise in developing extensions to extend PostgreSQL functionality. Master techniques for handling inter-process communication and background workers. Explore custom data types and integration with programming languages like Java and Perl. Author(s) None Ferrari, a seasoned database administrator and developer, specializes in delivering insightful PostgreSQL training. With extensive experience in both database management and software development, None brings practical knowledge and real-world examples to guide readers through mastering PostgreSQL server-side programming. Who is it for? This book is tailored for database administrators, developers, and engineers who have a basic understanding of PostgreSQL and are looking to expand their knowledge into server-side programming. If you're aiming to implement advanced database functionality or streamline data management tasks in PostgreSQL, this book is for you. It is ideal for those who wish to apply database programming techniques to enterprise-grade challenges. Beginner-friendly but designed to empower professionals with actionable insights.

Summary

When your data lives in multiple locations, belonging to at least as many applications, it is exceedingly difficult to ask complex questions of it. The default way to manage this situation is by crafting pipelines that will extract the data from source systems and load it into a data lake or data warehouse. In order to make this situation more manageable and allow everyone in the business to gain value from the data the folks at Dremio built a self service data platform. In this episode Tomer Shiran, CEO and co-founder of Dremio, explains how it fits into the modern data landscape, how it works under the hood, and how you can start using it today to make your life easier.

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. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Tomer Shiran about Dremio, the open source data as a service platform

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Dremio is and how the project and business got started?

What was the motivation for keeping your primary product open source? What is the governance model for the project?

How does Dremio fit in the current landscape of data tools?

What are some use cases that Dremio is uniquely equipped to support? Do you think that Dremio obviates the need for a data warehouse or large scale data lake?

How is Dremio architected internally?

How has that architecture evolved from when it was first built?

There are a large array of components (e.g. governance, lineage, catalog) built into Dremio that are often found in dedicated products. What are some of the strategies that you have as a business and development team to manage and integrate the complexity of the product?

What are the benefits of integrating all of those capabilities into a single system? What are the drawbacks?

One of the useful features of Dremio is the granular access controls. Can you discuss how those are implemented and controlled? For someone who is interested in deploying Dremio to their environment what is involved in getting it installed?

What are the scaling factors?

What are some of the most exciting features that have been added in recent releases? When is Dremio the wrong choice? What have been some of the most challenging aspects of building, maintaining, and growing the technical and business platform of Dremio? What do you have planned for the future of Dremio?

Contact Info

Tomer

@tshiran on Twitter LinkedIn

Dremio

Website @dremio on Twitter dremio on GitHub

Parting Question

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

Links

Dremio MapR Presto Business Intelligence Arrow Tableau Power BI Jupyter OLAP Cube Apache Foundation Hadoop Nikon DSLR Spark ETL (Extract, Transform, Load) Parquet Avro K8s Helm Yarn Gandiva Initiative for Apache Arrow LLVM TLS

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

Summary

Modern applications and data platforms aspire to process events and data in real time at scale and with low latency. Apache Flink is a true stream processing engine with an impressive set of capabilities for stateful computation at scale. In this episode Fabian Hueske, one of the original authors, explains how Flink is architected, how it is being used to power some of the world’s largest businesses, where it sits in the lanscape of stream processing tools, and how 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, 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. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Fabian Hueske, co-author of the upcoming O’Reilly book Stream Processing With Apache Flink, about his work on Apache Flink, the stateful streaming engine

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Flink is and how the project got started? What are some of the primary ways that Flink is used? How does Flink compare to other streaming engines such as Spark, Kafka, Pulsar, and Storm?

What are some use cases that Flink is uniquely qualified to handle?

Where does Flink fit into the current data landscape? How is Flink architected?

How has that architecture evolved? Are there any aspects of the current design that you would do differently if you started over today?

How does scaling work in a Flink deployment?

What are the scaling limits? What are some of the failure modes that users should be aware of?

How is the statefulness of a cluster managed?

What are the mechanisms for managing conflicts? What are the limiting factors for the volume of state that can be practically handled in a cluster and for a given purpose? Can state be shared across processes or tasks within a Flink cluster?

What are the comparative challenges of working with bounded vs unbounded streams of data? How do you handle out of order events in Flink, especially as the delay for a given event increases? For someone who is using Flink in their environment, what are the primary means of interacting with and developing on top of it? What are some of the most challenging or complicated aspects of building and maintaining Flink? What are some of the most interesting or unexpected ways that you have seen Flink used? What are some of the improvements or new features that are planned for the future of Flink? What are some features or use cases that you are explicitly not planning to support? For people who participate in the training sessions that you offer through Data Artisans, what are some of the concepts that they are challenged by?

What do they find most interesting or exciting?

Contact Info

LinkedIn @fhueske on Twitter fhueske on GitHub

Parting Question

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

Links

Flink Data Artisans IBM DB2 Technische Universität Berlin Hadoop Relational Database Google Cloud Dataflow Spark Cascading Java RocksDB Flink Checkpoints Flink Savepoints Kafka Pulsar Storm Scala LINQ (Language INtegrated Query) SQL Backpressure

Summary

A data lake can be a highly valuable resource, as long as it is well built and well managed. Unfortunately, that can be a complex and time-consuming effort, requiring specialized knowledge and diverting resources from your primary business. In this episode Yoni Iny, CTO of Upsolver, discusses the various components that are necessary for a successful data lake project, how the Upsolver platform is architected, and how modern data lakes can benefit your organization.

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 Yoni Iny about Upsolver, a data lake platform that lets developers integrate and analyze streaming data with ease

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Upsolver is and how it got started?

What are your goals for the platform?

There are a lot of opinions on both sides of the data lake argument. When is it the right choice for a data platform?

What are the shortcomings of a data lake architecture?

How is Upsolver architected?

How has that architecture changed over time? How do you manage schema validation for incoming data? What would you do differently if you were to start over today?

What are the biggest challenges at each of the major stages of the data lake? What is the workflow for a user of Upsolver and how does it compare to a self-managed data lake? When is Upsolver the wrong choice for an organization considering implementation of a data platform? Is there a particular scale or level of data maturity for an organization at which they would be better served by moving management of their data lake in house? What features or improvements do you have planned for the future of Upsolver?

Contact Info

Yoni

yoniiny on GitHub LinkedIn

Upsolver

Website @upsolver on Twitter LinkedIn Facebook

Parting Question

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

Links

Upsolver Data Lake Israeli Army Data Warehouse Data Engineering Podcast Episode About Data Curation Three Vs Kafka Spark Presto Drill Spot Instances Object Storage Cassandra Redis Latency Avro Parquet ORC Data Engineering Podcast Episode About Data Serialization Formats SSTables Run Length Encoding CSV (Comma Separated Values) Protocol Buffers Kinesis ETL DevOps Prometheus Cloudwatch DataDog InfluxDB SQL Pandas Confluent KSQL

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

Summary

Business intelligence is a necessity for any organization that wants to be able to make informed decisions based on the data that they collect. Unfortunately, it is common for different portions of the business to build their reports with different assumptions, leading to conflicting views and poor choices. Looker is a modern tool for building and sharing reports that makes it easy to get everyone on the same page. In this episode Daniel Mintz explains how the product is architected, the features that make it easy for any business user to access and explore their reports, and how you can use it for your organization 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. 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 Daniel Mintz about Looker, a a modern data platform that can serve the data needs of an entire company

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Looker is and the problem that it is aiming to solve?

How do you define business intelligence?

How is Looker unique from other approaches to business intelligence in the enterprise?

How does it compare to open source platforms for BI?

Can you describe the technical infrastructure that supports Looker? Given that you are connecting to the customer’s data store, how do you ensure sufficient security? For someone who is using Looker, what does their workflow look like?

How does that change for different user roles (e.g. data engineer vs sales management)

What are the scaling factors for Looker, both in terms of volume of data for reporting from, and for user concurrency? What are the most challenging aspects of building a business intelligence tool and company in the modern data ecosystem?

What are the portions of the Looker architecture that you would do differently if you were to start over today?

What are some of the most interesting or unusual uses of Looker that you have seen? What is in store for the future of Looker?

Contact Info

LinkedIn

Parting Question

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

Links

Looker Upworthy MoveOn.org LookML SQL Business Intelligence Data Warehouse Linux Hadoop BigQuery Snowflake Redshift DB2 PostGres ETL (Extract, Transform, Load) ELT (Extract, Load, Transform) Airflow Luigi NiFi Data Curation Episode Presto Hive Athena DRY (Don’t Repeat Yourself) Looker Action Hub Salesforce Marketo Twilio Netscape Navigator Dynamic Pricing Survival Analysis DevOps BigQuery ML Snowflake Data Sharehouse

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

In this episode, Wayne Eckerson and Shakeeb Ahkter dive into DataOps. They discuss what DataOps is, the goals and principles of DataOps, and reasons to adopt a DataOps strategy. Shakeeb also reveals the benefits gained from DataOps and what tools he uses. He is the Director of Enterprise Data Warehouse at Northwestern Medicine and is responsible for direction and oversight of data management, data engineering, and analytics.

Summary

Jupyter notebooks have gained popularity among data scientists as an easy way to do exploratory analysis and build interactive reports. However, this can cause difficulties when trying to move the work of the data scientist into a more standard production environment, due to the translation efforts that are necessary. At Netflix they had the crazy idea that perhaps that last step isn’t necessary, and the production workflows can just run the notebooks directly. Matthew Seal is one of the primary engineers who has been tasked with building the tools and practices that allow the various data oriented roles to unify their work around notebooks. In this episode he explains the rationale for the effort, the challenges that it has posed, the development that has been done to make it work, and the benefits that it provides to the Netflix data platform teams.

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 Matthew Seal about the ways that Netflix is using Jupyter notebooks to bridge the gap between data roles

Interview

Introduction How did you get involved in the area of data management? Can you start by outlining the motivation for choosing Jupyter notebooks as the core interface for your data teams?

Where are you using notebooks and where are you not?

What is the technical infrastructure that you have built to suppport that design choice? Which team was driving the effort?

Was it difficult to get buy in across teams?

How much shared code have you been able to consolidate or reuse across teams/roles? Have you investigated the use of any of the other notebook platforms for similar workflows? What are some of the notebook anti-patterns that you have encountered and what conventions or tooling have you established to discourage them? What are some of the limitations of the notebook environment for the work that you are doing? What have been some of the most challenging aspects of building production workflows on top of Jupyter notebooks? What are some of the projects that are ongoing or planned for the future that you are most excited by?

Contact Info

Matthew Seal

Email LinkedIn @codeseal on Twitter MSeal on GitHub

Parting Question

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

Links

Netflix Notebook Blog Posts Nteract Tooling OpenGov Project Jupyter Zeppelin Notebooks Papermill Titus Commuter Scala Python R Emacs NBDime

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

A classic management practice dictates that a newly-appointed leader must accomplish certain things in their first 90 days. While some of this is general knowledge, there are specifics when it comes to Data Management and Analytics If you have been recently named to head any group that has to manage or facilitate the use of data, at any level in the organization, then this audio blog post is for you.

Originally published at https://www.eckerson.com/articles/what-do-you-do-first-after-being-hired-as-a-bi-analytics-data-engineering-director

Summary

As data science becomes more widespread and has a bigger impact on the lives of people, it is important that those projects and products are built with a conscious consideration of ethics. Keeping ethical principles in mind throughout the lifecycle of a data project helps to reduce the overall effort of preventing negative outcomes from the use of the final product. Emily Miller and Peter Bull of Driven Data have created Deon to improve the communication and conversation around ethics among and between data teams. It is a Python project that generates a checklist of common concerns for data oriented projects at the various stages of the lifecycle where they should be considered. In this episode they discuss their motivation for creating the project, the challenges and benefits of maintaining such a checklist, and how 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. 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 This is your host Tobias Macey and this week I am sharing an episode from my other show, Podcast.init, about a project from Driven Data called Deon. It is a simple tool that generates a checklist of ethical considerations for the various stages of the lifecycle for data oriented projects. This is an important topic for all of the teams involved in the management and creation of projects that leverage data. So give it a listen and if you like what you hear, be sure to check out the other episodes at pythonpodcast.com

Interview

Introductions How did you get introduced to Python? Can you start by describing what Deon is and your motivation for creating it? Why a checklist, specifically? What’s the advantage of this over an oath, for example? What is unique to data science in terms of the ethical concerns, as compared to traditional software engineering? What is the typical workflow for a team that is using Deon in their projects? Deon ships with a default checklist but allows for customization. What are some common addendums that you have seen?

Have you received pushback on any of the default items?

How does Deon simplify communication around ethics across team boundaries? What are some of the most often overlooked items? What are some of the most difficult ethical concerns to comply with for a typical data science project? How has Deon helped you at Driven Data? What are the customer facing impacts of embedding a discussion of ethics in the product development process? Some of the items on the default checklist coincide with regulatory requirements. Are there any cases where regulation is in conflict with an ethical concern that you would like to see practiced? What are your hopes for the future of the Deon project?

Keep In Touch

Emily

LinkedIn ejm714 on GitHub

Peter

LinkedIn @pjbull on Twitter pjbull on GitHub

Driven Data

@drivendataorg on Twitter drivendataorg on GitHub Website

Picks

Tobias

Richard Bond Glass Art

Emily

Tandem Coffee in Portland, Maine

Peter

The Model Bakery in Saint Helena and Napa, California

Links

Deon Driven Data International Development Brookings Institution Stata Econometrics Metis Bootcamp Pandas

Podcast Episode

C# .NET Podcast.init Episode On Software Ethics Jupyter Notebook

Podcast Episode

Word2Vec cookiecutter data science Logistic Regression

The intro and outro music is

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