Exploring Processing Patterns For Streaming Data Integration In Your Data Lake Data Engineering Podcast

Summary
One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. With the improvements in streaming engines it is now possible to perform all of your data integration in near real time, but it can be challenging to understand the proper processing patterns to make that performant. In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are when coming from a batch oriented mindset.

Announcements

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Your host is Tobias Macey and today I’m interviewing Ori Rafael about strategies for building stream and batch processing patterns for data lake analytics

Interview

Introduction
How did you get involved in the area of data management?
Can you start by giving an overview of the state of the market for data lakes today?

What are the prevailing architectural and technological patterns that are being used to manage these systems?


Batch and streaming systems have been used in various combinations since the early days of Hadoop. The Lambda architecture has largely been abandoned, so what is the answer for today’s data lakes?
What are the challenges presented by streaming approaches to data transformations?

The batch model for processing is intuitive despite its latency problems. What are the benefits that it provides?


The core concept for data orchestration is the DAG. How does that manifest in a streaming context?
In batch processing idempotent/immutable datasets are created by re-running the entire pipeline when logic changes need to be made. Given that there is no definitive start or end of a stream, what are the options for amending logical errors in transformations?
What are some of the