By Zubair Nabi
Learn the fitting state of the art talents and information to leverage Spark Streaming to enforce a big selection of real-time, streaming functions. This e-book walks you thru end-to-end real-time software improvement utilizing real-world functions, facts, and code. Taking an application-first strategy, each one bankruptcy introduces use situations from a selected and makes use of publicly to be had datasets from that area to solve the intricacies of production-grade layout and implementation. The domain names lined in Pro Spark Streaming contain social media, the sharing economic climate, finance, web advertising, telecommunication, and IoT.
In the previous few years, Spark has develop into synonymous with significant facts processing. DStreams increase the underlying Spark processing engine to help streaming research with a unique micro-batch processing version. Pro Spark Streaming by Zubair Nabi will assist you to turn into a consultant of latency delicate functions via leveraging the foremost positive factors of DStreams, micro-batch processing, and useful programming. To this finish, the ebook comprises ready-to-deploy examples and real code. Pro Spark Streaming will act because the bible of Spark Streaming.
What you are going to Learn
- Discover Spark Streaming software improvement and most sensible practices
- Work with the low-level information of discretized streams
- Optimize production-grade deployments of Spark Streaming through configuration recipes and instrumentation utilizing Graphite, collectd, and Nagios
- Ingest information from disparate assets together with MQTT, Flume, Kafka, Twitter, and a customized HTTP receiver
- Integrate and couple with HBase, Cassandra, and Redis
- Take benefit of layout styles for side-effects and retaining nation around the Spark Streaming micro-batch model
- Implement real-time and scalable ETL utilizing info frames, SparkSQL, Hive, and SparkR
- Use streaming desktop studying, predictive analytics, and recommendations
- Mesh batch processing with circulation processing through the Lambda architecture
Who This e-book Is For
Data scientists, enormous information specialists, BI analysts, and knowledge architects.
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Extra resources for Pro Spark Streaming: The Zen of Real-Time Analytics Using Apache Spark
Sample text
Another use case is the creation of shared variables, such as broadcast variables and accumulators. The features of StreamingContext can be grouped on the basis of their functionality. That is what we do next. Creating DStreams The unit of application development from the user perspective is a DStream. It is also the data-ingestion point. StreamingContext is used to read data from various real-time sources and convert it to DStreams. Files, sockets, Akka actors, or others—StreamingContext covers them all.
45. 46. 47. 48. 49. 50. 51. 52. toMap } } The SparkConf object (line 24) remains unchanged. Your friend SparkContext, on the other hand, has been replaced with StreamingContext, which, as the name suggests, enables streaming applications. Along with SparkConf, it takes a batch size, which dictates the time interval at which the application is invoked for each micro-batch of the input data. In this example, the batch size is 1 second (line 27). 31 CHAPTER 3 ■ DSTREAMS: REAL-TIME RDDS StreamingContext is created by the driver program and maintains the connection with the Spark subsystem.
39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. toMap } } The SparkConf object (line 24) remains unchanged. Your friend SparkContext, on the other hand, has been replaced with StreamingContext, which, as the name suggests, enables streaming applications. Along with SparkConf, it takes a batch size, which dictates the time interval at which the application is invoked for each micro-batch of the input data. In this example, the batch size is 1 second (line 27). 31 CHAPTER 3 ■ DSTREAMS: REAL-TIME RDDS StreamingContext is created by the driver program and maintains the connection with the Spark subsystem.