Summary

Real-time capabilities have quickly become an expectation for consumers. The complexity of providing those capabilities is still high, however, making it more difficult for small teams to compete. Meroxa was created to enable teams of all sizes to deliver real-time data applications. In this episode DeVaris Brown discusses the types of applications that are possible when teams don't have to manage the complex infrastructure necessary to support continuous data flows.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing DeVaris Brown about the impact of real-time data on business opportunities and risk profiles

Interview

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

How have the focus and goals of the platform and company evolved over the past 2 years?

Who are the target customers for Meroxa?

What problems are they trying to solve when they come to your platform?

Applications powered by real-time data were the exclusive domain of large and/or sophisticated tech companies for several years due to the inherent complexities involved. What are the shifts that have made them more accessible to a wider variety of teams?

What are some of the remaining blockers for teams who want to start using real-time data?

With the democratization of real-time data, what are the new categories of products and applications that are being unlocked?

How are organizations thinking about the potential value that those types of apps/services can provide?

With data flowing constantly, there are new challenges around oversight and accuracy. How does real-time data change the risk profile for applications that are consuming it?

What are some of the technical controls that are available for organizations that are risk-averse?

What skills do developers need to be able to effectively design, develop, and deploy real-time data applications?

How does this differ when talking about internal vs. consumer/end-user facing applications?

What are the most interesting, innovative, or unexpected ways that you have seen Meroxa used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Meroxa? When is Meroxa the wrong choice? What do you have planned for the future of Meroxa?

Contact Info

LinkedIn @devarispbrown on Twitter

Parting Question

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

Closing Announcements

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

Links

Meroxa

Podcast Episode

Kafka Kafka Connect Conduit - golang Kafka connect replacement Pulsar Redpanda Flink Beam Clickhouse Druid Pinot

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

Summary

Yelp needs to be able to consume and process all of the user interactions that happen in their platform in as close to real-time as possible. To achieve that goal they embarked on a journey to refactor their monolithic architecture to be more modular and modern, and then they open sourced it! In this episode Justin Cunningham joins me to discuss the decisions they made and the lessons they learned in the process, including what worked, what didn’t, and what he would do differently if he was starting over 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 www.dataengineeringpodcast.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Justin Cunningham about Yelp’s data pipeline

Interview with Justin Cunningham

Introduction How did you get involved in the area of data engineering? Can you start by giving an overview of your pipeline and the type of workload that you are optimizing for? What are some of the dead ends that you experienced while designing and implementing your pipeline? As you were picking the components for your pipeline, how did you prioritize the build vs buy decisions and what are the pieces that you ended up building in-house? What are some of the failure modes that you have experienced in the various parts of your pipeline and how have you engineered around them? What are you using to automate deployment and maintenance of your various components and how do you monitor them for availability and accuracy? While you were re-architecting your monolithic application into a service oriented architecture and defining the flows of data, how were you able to make the switch while verifying that you were not introducing unintended mutations into the data being produced? Did you plan to open-source the work that you were doing from the start, or was that decision made after the project was completed? What were some of the challenges associated with making sure that it was properly structured to be amenable to making it public? What advice would you give to anyone who is starting a brand new project and how would that advice differ for someone who is trying to retrofit a data management architecture onto an existing project?

Keep in touch

Yelp Engineering Blog Email

Links

Kafka Redshift ETL Business Intelligence Change Data Capture LinkedIn Data Bus Apache Storm Apache Flink Confluent Apache Avro Game Days Chaos Monkey Simian Army PaaSta Apache Mesos Marathon SignalFX Sensu Thrift Protocol Buffers JSON Schema Debezium Kafka Connect Apache Beam

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