REDSHIFT UNLOAD PARQUET UPDATE
Pause – Reads the state information and processes incremental data since the last checkpoint, but does not update it.Disable – Makes sure that job bookmarks are not used that can result in the job always processing the entire dataset.Subsequent job run on the same data source only process newly added data since the last checkpoint. Enable – This option causes the job to update the bookmark state after each successful run to keep track of processed data.When using the AWS Glue console or the AWS Glue API to start a job, a job bookmark option is passed as a parameter. AWS Glue tracks the partitions that the job has processed successfully to prevent duplicate processing and writing the same data to the target data store multiple times. For example, your AWS Glue job might read new partitions in an S3-backed table. A job bookmark is composed of the states of various job elements, such as sources, transformations, and targets. Job bookmarks are used by AWS Glue jobs to process incremental data since the last job run. The snapshot above shows a view of the Glue Console with multiple job runs at different time instances of the same ETL job. The persisted state information is called job bookmark. This mechanism is used to track data processed by a particular run of an ETL job. AWS Glue job bookmarksĪWS Glue’s Spark runtime has a mechanism to store state.
The AWS Glue Parquet writer also allows schema evolution in datasets with the addition or deletion of columns.
REDSHIFT UNLOAD PARQUET HOW TO
The post also reviews best practices using job bookmarks with complex AWS Glue ETL scripts and workloads.įinally, the post shows how to use the custom AWS Glue Parquet writer optimized for performance by avoiding extra passes of the data and computing the schema at runtime. It also shows how to scale AWS Glue ETL jobs by reading only newly added data using job bookmarks, and processing late-arriving data by resetting the job bookmark to the end of a prior job run. This post shows how to incrementally load data from data sources in an Amazon S3 data lake and databases using JDBC. The first post of the series, Best practices to scale Apache Spark jobs and partition data with AWS Glue, discusses best practices to help developers of Apache Spark applications and Glue ETL jobs, big data architects, data engineers, and business analysts scale their data processing jobs running on AWS Glue automatically.
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October 2022: This post was reviewed for accuracy.ĪWS Glue provides a serverless environment to prepare (extract and transform) and load large amounts of datasets from a variety of sources for analytics and data processing with Apache Spark ETL jobs.
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