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Book description Implement, run, operate, and test data processing pipelines using Apache BeamKey FeaturesUnderstand how to improve usability and productivity when implementing Beam pipelinesLearn how to use stateful processing to implement complex use cases using Apache BeamImplement, test, and run Apache Beam pipelines with the help of expert tips and techniquesBook DescriptionApache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing.This book will help you to confidently build data processing pipelines with Apache Beam. You'll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You'll also learn how to test and run the pipelines efficiently. As you progress, you'll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you'll understand advanced Apache Beam concepts, such as implementing your own I/O connectors.By the end of this book, you'll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems.What you will learnUnderstand the core concepts and architecture of Apache BeamImplement stateless and stateful data processing pipelinesUse state and timers for processing real-time event processingStructure your code for reusabilityUse streaming SQL to process real-time data for increasing productivity and data accessibilityRun a pipeline using a portable runner and implement data processing using the Apache Beam Python

Widgets docs onembed_minimal_html.Kubeflow PipelinesKubeflow Pipelinesincludes integrations that embed the TFMA notebook extension (code).This integration relies on network access at runtime to load a variant of theJavaScript build published on unpkg.com (see configand loader code).Notable DependenciesTensorFlow is required.Apache Beam is required; it's the way that efficientdistributed computation is supported. By default, Apache Beam runs in localmode but can also run in distributed mode usingGoogle Cloud Dataflow and other ApacheBeamrunners.Apache Arrow is also required. TFMA uses Arrow torepresent data internally in order to make use of vectorized numpy functions.Getting StartedFor instructions on using TFMA, see the get startedguide.Compatible VersionsThe following table is the TFMA package versions that are compatible with eachother. This is determined by our testing framework, but other untestedcombinations may also work.tensorflow-model-analysisapache-beam[gcp]pyarrowtensorflowtensorflow-metadatatfx-bslGitHub master2.60.010.0.1nightly (2.x)1.16.11.16.10.47.12.60.010.0.12.161.16.11.16.10.47.02.60.010.0.12.161.16.11.16.10.46.02.47.010.0.02.151.15.01.15.10.45.02.47.010.0.02.131.14.01.14.00.44.02.40.06.0.02.121.13.11.13.00.43.02.40.06.0.02.111.12.01.12.00.42.02.40.06.0.01.15.5 / 2.101.11.01.11.10.41.02.40.06.0.01.15.5 / 2.91.10.01.10.10.40.02.38.05.0.01.15.5 / 2.91.9.01.9.00.39.02.38.05.0.01.15.5 / 2.81.8.01.8.00.38.02.36.05.0.01.15.5 / 2.81.7.01.7.00.37.02.35.05.0.01.15.5 / 2.71.6.01.6.00.36.02.34.05.0.01.15.5 / 2.71.5.01.5.00.35.02.33.05.0.01.15 / 2.61.4.01.4.00.34.12.32.02.0.01.15 / 2.61.2.01.3.00.34.02.31.02.0.01.15 / 2.61.2.01.3.10.33.02.31.02.0.01.15 / 2.51.2.01.2.00.32.12.29.02.0.01.15 / 2.51.1.01.1.10.32.02.29.02.0.01.15 / 2.51.1.01.1.00.31.02.29.02.0.01.15 / 2.51.0.01.0.00.30.02.28.02.0.01.15 / 2.40.30.00.30.00.29.02.28.02.0.01.15 / 2.40.29.00.29.00.28.02.28.02.0.01.15 / 2.40.28.00.28.00.27.02.27.02.0.01.15 / 2.40.27.00.27.00.26.12.28.00.17.01.15 / 2.30.26.00.26.00.26.02.25.00.17.01.15 / 2.30.26.00.26.00.25.02.25.00.17.01.15 / 2.30.25.00.25.00.24.32.24.00.17.01.15 / 2.30.24.00.24.10.24.22.23.00.17.01.15 / 2.30.24.00.24.00.24.12.23.00.17.01.15 / 2.30.24.00.24.00.24.02.23.00.17.01.15 / 2.30.24.00.24.00.23.02.23.00.17.01.15 / 2.30.23.00.23.00.22.22.20.00.16.01.15 / 2.20.22.20.22.00.22.12.20.00.16.01.15 / 2.20.22.20.22.00.22.02.20.00.16.01.15 / 2.20.22.00.22.00.21.62.19.00.15.01.15 / 2.10.21.00.21.30.21.52.19.00.15.01.15 / 2.10.21.00.21.30.21.42.19.00.15.01.15 / 2.10.21.00.21.30.21.32.17.00.15.01.15 / 2.10.21.00.21.00.21.22.17.00.15.01.15 / 2.10.21.00.21.00.21.12.17.00.15.01.15 / 2.10.21.00.21.00.21.02.17.00.15.01.15 / 2.10.21.00.21.00.15.42.16.00.15.01.15 / 2.0n/a0.15.10.15.32.16.00.15.01.15 / 2.0n/a0.15.10.15.22.16.00.15.01.15 / 2.0n/a0.15.10.15.12.16.00.15.01.15 / 2.0n/a0.15.00.15.02.16.00.15.01.15n/an/a0.14.02.14.0n/a1.14n/an/a0.13.12.11.0n/a1.13n/an/a0.13.02.11.0n/a1.13n/an/a0.12.12.10.0n/a1.12n/an/a0.12.02.10.0n/a1.12n/an/a0.11.02.8.0n/a1.11n/an/a0.9.22.6.0n/a1.9n/an/a0.9.12.6.0n/a1.10n/an/a0.9.02.5.0n/a1.9n/an/a0.6.02.4.0n/a1.6n/an/aQuestionsPlease direct any questions about working with TFMA toStack Overflow using thetensorflow-model-analysistag.

This page describes how to use the Dataflow connector forSpanner to import, export, and modify data in SpannerGoogleSQL-dialect databases and PostgreSQL-dialect databases.Dataflow is a managed service for transforming and enrichingdata. The Dataflow connector for Spanner lets you readdata from and write data to Spanner in a Dataflowpipeline, optionally transforming or modifying the data. You can also createpipelines that transfer data between Spanner and otherGoogle Cloud products.The Dataflow connector is the recommended method for efficientlymoving data into and out of Spanner in bulk. It's also therecommended method for performing large transformations to a database which arenot supported by Partitioned DML, such as table moves and bulk deletesthat require a JOIN. When working with individual databases, there are othermethods you can use to import and export data:Use the Google Cloud console to export an individual database fromSpanner to Cloud Storage in Avroformat.Use the Google Cloud console to import a database back intoSpanner from files you exported to Cloud Storage.Use the REST API or Google Cloud CLI to run export or importjobs from Spanner to Cloud Storage and back also usingAvro format.The Dataflow connector for Spanner is part of theApache Beam Java SDK, and it provides an API for performing the previousactions. For more information about some of the concepts discussed in this page,such as PCollection objects and transforms, see the Apache Beam programmingguide.Add the connector to your Maven projectTo add the Google Cloud Dataflow connector to a Mavenproject, add the beam-sdks-java-io-google-cloud-platform Maven artifact toyour pom.xml file as a dependency.For example, assuming that your pom.xml file sets beam.version to theappropriate version number, you would add the following dependency: org.apache.beam beam-sdks-java-io-google-cloud-platform ${beam.version}Read data from SpannerTo read from Spanner, apply the SpannerIO.readtransform. Configure the read using the methods in theSpannerIO.Read class. Applying the transform returns aPCollection, where each element in the collectionrepresents an individual row