Datascape podcast episode 30 – learn about streaming

In this episode of the Datascape Podcast, we welcome back Danil Zburivsky from Pythian to talk about the new streaming technologies that he and his team are working on. The step up from batch processing to streaming seems to be on everyone’s minds, and Danil is here to explain exactly what we can expect from this new service. In our conversation, we go over precisely what Danil’s role at Pythian entails before moving onto some general definitions and differences between batch and streaming data technologies. Danil gives us some examples of how this new streaming platform works and can be implemented and then talks education and his approach to sharing skills and knowledge around his specialty. We then go through some of the more prominent companies and products that are currently available, before Danil goes a bit deeper into the actual nuts and bolts of dealing with data, troubleshooting and more.

Be sure to tune in to hear this fascinating discussion!

Key points from this episode:

• Danil talks about his latest project.
• A brief definition of streaming and batch services from Danil’s perspective.
• Some use case examples from Danil’s work that implements streaming.
• Differentiating between the applications of batch and streaming.
• Danil’s approach to business education around these fields.
• Looking at some of the key players in this arena.
• Handling and aggregating the constant stream of data.
• The use of time in organizing the amount of data that is processed.
• Practical troubleshooting for some of the obvious pitfalls of these models.

And much more!

Links mentioned in this episode:

Danil Zburivsky on LinkedIn

Danil Zburivsky on Pythian

Episode 3 of Datascape Podcast

Pythian

Microsoft SQL Server

MySQL

PostgreSQL

Oracle

Spark

Beam

Kafka

Kinesis

Cloudscape Podcast