Exploring Processing Patterns For Streaming Data Integration In Your Data Lake
Data Engineering Podcast
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Streaming - Is There a Dependency Grab?
Swimming is an orchestration problem, and we touch some of that during the corso. All of these operations need to happen in a certain sequence so you maintain consistency. And when you go to streaming, now it's the useless responsibility to be the pipe line octomiser. The idea of the clevative pipe lines grows very y with treaming because it's just too hard to do all the dot of cossation yourself. You're not just orchestrating the stream. You're orchestrating the work on the data itself, in the lake, the data ni table, the metho data like all of that is part of the management that the piper needs to do
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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
- 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 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 data processing/integration patterns that are impossible in a batch system?
- What are some useful strategies for migrating from a purely batch, or hybrid batch and streaming architecture, to a purely streaming system?
- What are some of the changes in technological or organizational patterns that are often overlooked or misunderstood in this shift?
- What are some of the most surprising things that you have learned about streaming systems in your time at Upsolver?
- What are the most interesting, innovative, or unexpected ways that you have seen streaming architectures used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on streaming data integration?
- When are streaming architectures the wrong approach?
- What do you have planned for the future of Upsolver to make streaming data easier to work with?
Contact Info
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.
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Links
- Upsolver
- Hive Metastore
- Hudi
- Iceberg
- Hadoop
- Lambda Architecture
- Kappa Architecture
- Apache Beam
- Event Sourcing
- Flink
- Spark Structured Streaming
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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