Self Service Real Time Data Integration Without The Headaches With Meroxa

Summary

Analytical workloads require a well engineered and well maintained data integration process to ensure that your information is reliable and up to date. Building a real-time pipeline for your data lakes and data warehouses is a non-trivial effort, requiring a substantial investment of time and energy. Meroxa is a new platform that aims to automate the heavy lifting of change data capture, monitoring, and data loading. In this episode founders DeVaris Brown and Ali Hamidi explain how their tenure at Heroku informed their approach to making data integration self service, how the platform is architected, and how they have designed their system to adapt to the continued evolution of the data ecosystem.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Your host is Tobias Macey and today I’m interviewing DeVaris Brown and Ali Hamidi about Meroxa, a new platform as a service for data integration

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you start by describing what you are building at Meroxa and what motivated you to turn it into a business?
• What are the lessons that you learned from your time at Heroku which you are applying to your work on Meroxa?
• Who are your target users and what are your guiding principles for designing the platform interface?
• What are the common difficulties that engineers face in building and maintaining data infrastructure?
• There are a variety of platforms that offer solutions for managing data integration, or powering end-to-end analytics, or building machine learning pipelines. What are the shortcomings of those existing options that might lead someone to choose Meroxa?
• How is the Meroxa platform architected?
  • What are some of the initial assumptions that you had which have been challenged as you proceed with implementation?
• What new capabilities does Meroxa bring to someone who uses it for integrating their application data?
• What are the growth options for organizations that get started with Meroxa?
• What are the core principles that you are focused on to allow for evolving your platform over the long run as the surrounding ecosystem continues to mature?
• When is Meroxa the wrong choice?
• What do you have planned for the future?

Contact Info

• DeVaris Brown
• Ali Hamidi

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.
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Links

• Meroxa
• Heroku
• Heroku Kafka
• Ascend
• StreamSets
• Nexus
• Kafka Connect
• Airflow
  • Podcast.__init__ Episode
• Spark
  • Data Engineering Episode
• Change Data Capture
• Segment
  • Podcast Episode
• Rudderstack
• MParticle
• Debezium
  • Podcast Episode
• DBT
  • Podcast Episode
• Materialize
  • Podcast Episode
• Stitch Data
• Fivetran
  • Podcast Episode
• Elasticsearch
  • Podcast Episode
• gRPC
• GraphQL
• REST == REpresentational State Transfer
• Dagster/Elementl
  • Data Engineering Podcast Episode
  • Podcast.__init__ Episode
• Prefect
  • Podcast Episode

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