October 2023
Beginner to intermediate
264 pages
6h 45m
English
Content preview from Delta Lake: Up and Running
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Chapter 8. Operations on Streaming Data
Spark Structured Streaming was first introduced in Apache Spark 2.0. The main goal of Structured Streaming was to build near-real-time streaming applications on Spark. Structured Streaming replaced an older, lower-level API called DStreams (Discretized Streams), which was based upon the old Spark RDD model. Since then, Structured Streaming has added many optimizations and connectors, including integration with Delta Lake.
Delta Lake is integrated with Spark Structured Streaming through its two major operators: readStream and writeStream. Delta tables can be used as both streaming sources and streaming sinks. Delta Lake overcomes many limitations typically associated with streaming systems, including:
Coalescing small files produced by low-latency ingestion
Maintaining “exactly-once” processing with more than one stream (or concurrent batch jobs)
Leveraging the Delta transaction log for efficient discovery of which files are new when using files for a source stream
We will start this chapter with a quick review of Spark Structured Streaming, followed by an initial overview of Delta Lake streaming and its unique capabilities. Next, we will walk through a small “Hello Streaming World!” Delta Lake streaming example. While limited in scope, this example will provide an opportunity to understand the details of the Delta Lake streaming programming model in a very simple context.
Incremental processing of data has become a popular ETL model. The ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.