Everyone expects data to be transmitted, processed, and updated instantly as more and more products integrate streaming data. The technology to make that possible has been around for a number of years, but the barriers to adoption have still been high due to the level of technical understanding and operational capacity that have been required to run at scale. Datastax has recently introduced a new managed offering for Pulsar workloads in the form of Astra Streaming that lowers those barriers and make stremaing workloads accessible to a wider audience. In this episode Prabhat Jha and Jonathan Ellis share the work that they have been doing to integrate streaming data into their managed Cassandra service. They explain how Pulsar is being used by their customers, the work that they have done to scale the administrative workload for multi-tenant environments, and the challenges of operating such a data intensive service at large scale. This is a fascinating conversation with a lot of useful lessons for anyone who wants to understand the operational aspects of Pulsar and the benefits that it can provide to data workloads.
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- 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 Prabhat Jha and Jonathan Ellis about Astra Streaming, a cloud-native streaming platform built on Apache Pulsar
How did you get involved in the area of data management?
Can you describe what the Astra platform is and the story behind it?
How does streaming fit into your overall product vision and the needs of your customers?
What was your selection process/criteria for adopting a streaming engine to complement your existing technology investment?
What are the core use cases that you are aiming to support with Astra Streaming?
Can you describe the architecture and automation of your hosted platform for Pulsar?
- What are the integration points that you have built to make it work well with Cassandra?
What are some of the additional tools that you have added to your distribution of Pulsar to simplify operation and use?
What are some of the sharp edges that you have had to sand down as you have scaled up your usage of Pulsar?
What is the process for someone to adopt and integrate with your Astra Streaming service?
- How do you handle migrating existing projects, particularly if they are using Kafka currently?
One of the capabilities that you highlight on the product page for Astra Streaming is the ability to execute machine learning workflows on data in flight. What are some of the supporting systems that are necessary to power that workflow?
- What are the capabilities that are built into Pulsar that simplify the operational aspects of streaming ML?
What are the ways that you are engaging with and supporting the Pulsar community?
- What are the near to medium term elements of the Pulsar roadmap that you are working toward and excited to incorporate into Astra?
What are the most interesting, innovative, or unexpected ways that you have seen Astra used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Astra?
When is Astra the wrong choice?
What do you have planned for the future of Astra?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Datastax Astra Streaming
- Datastax Astra DB
- Luna Streaming Distribution
- Kesque (formerly Kafkaesque)
- Pulsar Heartbeat
- Pulsar Summit
- Pulsar Summit Presentation on Kafka Connectors
- Chaos Engineering
- Fallout chaos engineering tools
- Jack VanLightly
- Change Data Capture