Real-world data streaming practices for building data-intensive applications
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In the field of data analytics, there is a clear trend from batch-oriented systems towards streaming analytics pipelines. Such streaming pipelines can also serve as a more general building block for data-intensive applications.
These trends have, of course, impacted the development of streaming systems. This, in turn, has implications for best practices around building streaming analytics pipelines.
Building a scalable, fault-tolerant, low-latency streaming analytics pipeline remains challenging. Which is the best combination of the many different tools? How should you configure and connect different pieces? How can you scale the pipeline with growing or shrinking data? How are different failure scenarios handled?
In this webcast you will learn about:
- The latest development in streaming analytics.
- What different architecture options you have for your streaming analytics pipeline.
- How to choose the best tool for your use case.
- How to connect these different tools to a scalable and fault-tolerant system.
- How are different failure scenarios impacting the overall architecture and how can they be solved quickly.
- How to scale the pipeline while using the underlying infrastructure efficiently.
About your instructor
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Jörg Schad, is a Technical Lead for Community Projects at Mesosphere in San Francisco. His speaking experience includes various meetups, international conferences, and lecture halls.