Summary

If you like the features of Cassandra DB but wish it ran faster with fewer resources then ScyllaDB is the answer you have been looking for. In this episode Eyal Gutkind explains how Scylla was created and how it differentiates itself in the crowded database market.

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 Eyal Gutkind about ScyllaDB

Interview

Introduction How did you get involved in the area of data management? What is ScyllaDB and why would someone choose to use it? How do you ensure sufficient reliability and accuracy of the database engine? The large draw of Scylla is that it is a drop in replacement of Cassandra with faster performance and no requirement to manage th JVM. What are some of the technical and architectural design choices that have enabled you to do that? Deployment and tuning What challenges are inroduced as a result of needing to maintain API compatibility with a diferent product? Do you have visibility or advance knowledge of what new interfaces are being added to the Apache Cassandra project, or are you forced to play a game of keep up? Are there any issues with compatibility of plugins for CassandraDB running on Scylla? For someone who wants to deploy and tune Scylla, what are the steps involved? Is it possible to join a Scylla cluster to an existing Cassandra cluster for live data migration and zero downtime swap? What prompted the decision to form a company around the database? What are some other uses of Seastar?

Keep in touch

Eyal

LinkedIn

ScyllaDB

Website @ScyllaDB on Twitter GitHub Mailing List Slack

Links

Seastar Project DataStax XFS TitanDB OpenTSDB KairosDB CQL Pedis

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

Summary

What exactly is data engineering? How has it evolved in recent years and where is it going? How do you get started in the field? In this episode, Maxime Beauchemin joins me to discuss these questions and more.

Transcript provided by CastSource

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 Maxime Beauchemin

Questions

Introduction How did you get involved in the field of data engineering? How do you define data engineering and how has that changed in recent years? Do you think that the DevOps movement over the past few years has had any impact on the discipline of data engineering? If so, what kinds of cross-over have you seen? For someone who wants to get started in the field of data engineering what are some of the necessary skills? What do you see as the biggest challenges facing data engineers currently? At what scale does it become necessary to differentiate between someone who does data engineering vs data infrastructure and what are the differences in terms of skill set and problem domain? How much analytical knowledge is necessary for a typical data engineer? What are some of the most important considerations when establishing new data sources to ensure that the resulting information is of sufficient quality? You have commented on the fact that data engineering borrows a number of elements from software engineering. Where does the concept of unit testing fit in data management and what are some of the most effective patterns for implementing that practice? How has the work done by data engineers and managers of data infrastructure bled back into mainstream software and systems engineering in terms of tools and best practices? How do you see the role of data engineers evolving in the next few years?

Keep In Touch

@mistercrunch on Twitter mistercrunch on GitHub Medium

Links

Datadog Airflow The Rise of the Data Engineer Druid.io Luigi Apache Beam Samza Hive Data Modeling

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