Designing ETL Pipelines with Structured Streaming and Delta Lake— How to Architect Things Right

Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark's built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner. In this talk, I am going examine a number common streaming design patterns in the context of the following questions. WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements? WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements? HOW are going to architect the solution? And how much are you willing to pay for it Clarity in understanding the 'what and why' of any problem can automatically much clarity on the 'how' to architect it using Structured Streaming and, in many cases, Delta Lake.

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