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

Buzzfeed needs to be able to understand how its users are interacting with the myriad articles, videos, etc. that they are posting. This lets them produce new content that will continue to be well-received. To surface the insights that they need to grow their business they need a robust data infrastructure to reliably capture all of those interactions. Walter Menendez is a data engineer on their infrastructure team and in this episode he describes how they manage data ingestion from a wide array of sources and create an interface for their data scientists to produce valuable conclusions.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 Walter Menendez about the data engineering platform at Buzzfeed

Interview

Introduction How did you get involved in the area of data management? How is the data engineering team at Buzzfeed structured and what kinds of projects are you responsible for? What are some of the types of data inputs and outputs that you work with at Buzzfeed? Is the core of your system using a real-time streaming approach or is it primarily batch-oriented and what are the business needs that drive that decision? What does the architecture of your data platform look like and what are some of the most significant areas of technical debt? Which platforms and languages are most widely leveraged in your team and what are some of the outliers? What are some of the most significant challenges that you face, both technically and organizationally? What are some of the dead ends that you have run into or failed projects that you have tried? What has been the most successful project that you have completed and how do you measure that success?

Contact Info

@hackwalter on Twitter walterm on GitHub

Links

Data Literacy MIT Media Lab Tumblr Data Capital Data Infrastructure Google Analytics Datadog Python Numpy SciPy NLTK Go Language NSQ Tornado PySpark AWS EMR Redshift Tracking Pixel Google Cloud Don’t try to be google Stop Hiring DevOps Engineers and Start Growing Them

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

Summary

There is a vast constellation of tools and platforms for processing and analyzing your data. In this episode Matthew Rocklin talks about how Dask fills the gap between a task oriented workflow tool and an in memory processing framework, and how it brings the power of Python to bear on the problem of big data.

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 Matthew Rocklin about Dask and the Blaze ecosystem.

Interview with Matthew Rocklin

Introduction How did you get involved in the area of data engineering? Dask began its life as part of the Blaze project. Can you start by describing what Dask is and how it originated? There are a vast number of tools in the field of data analytics. What are some of the specific use cases that Dask was built for that weren’t able to be solved by the existing options? One of the compelling features of Dask is the fact that it is a Python library that allows for distributed computation at a scale that has largely been the exclusive domain of tools in the Hadoop ecosystem. Why do you think that the JVM has been the reigning platform in the data analytics space for so long? Do you consider Dask, along with the larger Blaze ecosystem, to be a competitor to the Hadoop ecosystem, either now or in the future? Are you seeing many Hadoop or Spark solutions being migrated to Dask? If so, what are the common reasons? There is a strong focus for using Dask as a tool for interactive exploration of data. How does it compare to something like Apache Drill? For anyone looking to integrate Dask into an existing code base that is already using NumPy or Pandas, what does that process look like? How do the task graph capabilities compare to something like Airflow or Luigi? Looking through the documentation for the graph specification in Dask, it appears that there is the potential to introduce cycles or other bugs into a large or complex task chain. Is there any built-in tooling to check for that before submitting the graph for execution? What are some of the most interesting or unexpected projects that you have seen Dask used for? What do you perceive as being the most relevant aspects of Dask for data engineering/data infrastructure practitioners, as compared to the end users of the systems that they support? What are some of the most significant problems that you have been faced with, and which still need to be overcome in the Dask project? I know that the work on Dask is largely performed under the umbrella of PyData and sponsored by Continuum Analytics. What are your thoughts on the financial landscape for open source data analytics and distributed computation frameworks as compared to the broader world of open source projects?

Keep in touch

@mrocklin on Twitter mrocklin on GitHub

Links

http://matthewrocklin.com/blog/work/2016/09/22/cluster-deployments?utm_source=rss&utm_medium=rss https://opendatascience.com/blog/dask-for-institutions/?utm_source=rss&utm_medium=rss Continuum Analytics 2sigma X-Array Tornado

Website Podcast Interview

Airflow Luigi Mesos Kubernetes Spark Dryad Yarn Read The Docs XData

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