Building robust stream processing applications is challenging. The typical associated with stream processing include the following:
- Complex Data: Diverse data formats and the of data create significant challenges streaming applications. Typically, the data is available in various formats, such as JSON, CSV, AVRO, and binary. Additionally, dirty data, or late arriving, and out-of-order data, can make the design of such applications extremely complex.
- Complex workloads: Streaming applications to support a diverse set of application requirements, including interactive queries, machine learning pipelines, and so on.
- Complex systems: With diverse systems, including Kafka, S3, Kinesis, and so on, system failures can lead to significant reprocessing or bad results.
Steam processing using Spark SQL can be fast, scalable, and fault-tolerant. It provides an extensive set of high-level APIs to deal with complex data and workloads...
