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Apache Spark has been gaining steam, with rapidity, both in the headlines and in real-world adoption. Spark was developed in 2009, and open sourced in 2010. Since then, it has grown to become one of the largest open source communities in big data with over 200 contributors from more than 50 organizations. This open source analytics engine stands out for its ability to process large volumes of data significantly faster than contemporaries such as MapReduce, primarily owing to in-memory storage of data on its own processing framework. That being said, one of the top real-world industry use cases for Apache Spark is its ability to process ‘streaming data‘.
With so much data being processed on a daily basis, it has become essential for companies to be able to stream and analyze it all in real time, and Spark Streaming has the capability to handle this extra workload. Some experts even theorize that Spark could become the go-to platform for stream-computing applications, no matter the type. The reason for this claim is that Spark Streaming unifies disparate data processing capabilities, allowing developers to use a single framework to accommodate all their processing needs. Among the general ways that Spark Streaming is being used by businesses today are Streaming ETL, Data Enrichment, Trigger Event Detection and Complex Session Analysis. In this webinar, we will cover an introduction, internals and industry use cases of ‘Structured Streaming in Spark’.
- Understanding of Data Processing Architecture
- Why and When to use Spark’s Structured Streaming
- Spark’s Structured Streaming Programming Paradigm
- Internals of Spark’s Structured Streaming
- Spark Structured Streaming in the Real World – examples of how customers of Qubole use it