Apache Beam is an open-source unified model for processing batch and streaming data in a parallel manner. Built to support Google’s Cloud Dataflow backend, Beam pipelines can now be executed on any supported distributed processing backends.
Apache Beam SDKs can represent and process both finite and infinite datasets using the same programming model. All data processing tasks are defined using a Beam pipeline and are represented as directed acyclic graphs. These pipelines can then be executed on multiple execution backends such as Google Cloud Dataflow, Apache Flink, and Apache Spark. In this course, Exploring the Apache Beam SDK for Modeling Streaming Data for Processing, we will explore Beam APIs for defining pipelines, executing transforms, and performing windowing and join operations. First, you will understand and work with the basic components of a Beam pipeline, PCollections, and PTransforms. You will work with PCollections holding different kinds of elements and see how you can specify the schema for PCollection elements. You will then configure these pipelines using custom options and execute them on backends such as Apache Flink and Apache Spark. Next, you will explore the different kinds of core transforms that you can apply to streaming data for processing. This includes the ParDo and DoFns, GroupByKey, CoGroupByKey for join operations and the Flatten and Partition transforms. You will then see how you can perform windowing operations on input streams and apply fixed windows, sliding windows, session windows, and global windows to your streaming data. You will use the join extension library to perform inner and outer joins on datasets. Finally, you will configure metrics that you want tracked during pipeline execution including counter metrics, distribution metrics, and gauge metrics, and then round this course off by executing SQL queries on input data. When you are finished with this course you will have the skills and knowledge to perform a wide range of data processing tasks using core Beam transforms and will be able to track metrics and run SQL queries on input streams.
Apache Beam SDKs can represent and process both finite and infinite datasets using the same programming model. All data processing tasks are defined using a Beam pipeline and are represented as directed acyclic graphs. These pipelines can then be executed on multiple execution backends such as Google Cloud Dataflow, Apache Flink, and Apache Spark. In this course, Exploring the Apache Beam SDK for Modeling Streaming Data for Processing, we will explore Beam APIs for defining pipelines, executing transforms, and performing windowing and join operations. First, you will understand and work with the basic components of a Beam pipeline, PCollections, and PTransforms. You will work with PCollections holding different kinds of elements and see how you can specify the schema for PCollection elements. You will then configure these pipelines using custom options and execute them on backends such as Apache Flink and Apache Spark. Next, you will explore the different kinds of core transforms that you can apply to streaming data for processing. This includes the ParDo and DoFns, GroupByKey, CoGroupByKey for join operations and the Flatten and Partition transforms. You will then see how you can perform windowing operations on input streams and apply fixed windows, sliding windows, session windows, and global windows to your streaming data. You will use the join extension library to perform inner and outer joins on datasets. Finally, you will configure metrics that you want tracked during pipeline execution including counter metrics, distribution metrics, and gauge metrics, and then round this course off by executing SQL queries on input data. When you are finished with this course you will have the skills and knowledge to perform a wide range of data processing tasks using core Beam transforms and will be able to track metrics and run SQL queries on input streams.
