![]() By default, the original table is retained with the name table_BACKUP_. Supported formats are Avro, Parquet, and ORC. The table’s schema, partitioning, properties, and location are copied from the source table. Using migrate: This procedure replaces a table with an Apache Iceberg table loaded with the source’s data files.Upon completion, the Iceberg table treats these files as if they are part of the set of files owned by Apache Iceberg. This procedure doesn’t analyze the schema of the files to determine if they match the schema of the Iceberg table. Unlike migrate or snapshot, add_files can import files from a specific partition or partitions and doesn’t create a new Iceberg table. Using add_files: This procedure adds existing data files to an existing Iceberg table with a new snapshot that includes the files.An in-place migration can be performed in either of two ways: The existing data file format must be Apache Parquet, Apache ORC, or Apache Avro. ![]() This can be a much less expensive operation compared to rewriting all the data files. In this method, the metadata are recreated in an isolated environment and colocated with the existing data files. This means the data files in the data lake aren’t modified during the migration and all Apache Iceberg metadata files (manifests, manifest files, and table metadata files) are generated outside the purview of the data. In an in-place data migration strategy, existing datasets are upgraded to Apache Iceberg format without first reprocessing or restating existing data. There are two broad methods to migrate the existing data in a data lake in Apache Parquet format to Apache Iceberg format to convert the data lake to a transactional table format. In this post, we show you how you can convert existing data in an Amazon S3 data lake in Apache Parquet format to Apache Iceberg format to support transactions on the data using Jupyter Notebook based interactive sessions over AWS Glue 4.0. Apache Iceberg enables transactions on data lakes and can simplify data storage, management, ingestion, and processing. Open table formats, such as Apache Iceberg, provide a solution to this issue. With changing use cases, customers are looking for ways to not only move new or incremental data to data lakes as transactions, but also to convert existing data based on Apache Parquet to a transactional format. But traditionally, data lakes built on Amazon S3 are immutable and don’t provide the transactional capabilities needed to support changing use cases. Oftentimes, you want to continuously ingest data from various sources into a data lake and query the data concurrently through multiple analytics tools with transactional capabilities. Amazon S3 allows you to access diverse data sets, build business intelligence dashboards, and accelerate the consumption of data by adopting a modern data architecture or data mesh pattern on Amazon Web Services (AWS).Īnalytics use cases on data lakes are always evolving. Over the years, data lakes on Amazon Simple Storage Service (Amazon S3) have become the default repository for enterprise data and are a common choice for a large set of users who query data for a variety of analytics and machine leaning use cases. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. For more information, see Making a cross-account API call.A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. Note: The Amazon EMR or AWS Glue job must have sufficient AWS Identity and Access Management (IAM) permissions to access the cross-account AWS Glue Data Catalog. ![]() "spark.jars": "/usr/share/aws/iceberg/lib/iceberg-spark3-runtime.jar", "": ".extensions.IcebergSparkSessionExtensions", If you're using Amazon EMR version 6.5 or later, then use the following spark-defaults configuration: ] } ![]() Note: For cross-account scenarios, you must always use the glue.id property to specify the corresponding AWS Glue Data Catalog ID (AWS account ID). ![]() For more information, see Use an Iceberg cluster with Spark. Or, use the Spark default configuration (/etc/spark/conf/nf). Value: =.extensions.IcebergSparkSessionExtensions -conf =.SparkCatalog -conf .glue.id= -conf .warehouse=s3:/// -conf .catalog-impl=.glue.GlueCatalog -conf .io-impl=.s3.S3FileIOįor an Amazon EMR cluster that runs version 6.5 or later, set the parameters when you submit the job. You can set these parameters in a number of ways, depending on whether you use an AWS Glue job or an Amazon EMR cluster.įor AWS Glue jobs, use job parameters. conf _catalog.catalog-impl=.glue.GlueCatalog \ conf =.extensions.IcebergSparkSessionExtensions \ Set the following parameters to use Spark to interact with Apache Iceberg tables from the AWS Glue Data Catalog: ![]()
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