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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

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 this method, the metadata are recreated in an isolated environment and colocated with the existing data files.

Data Lake 108
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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Apache Iceberg addresses customer needs by capturing rich metadata information about the dataset at the time the individual data files are created.

Data Lake 122
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From Hive Tables to Iceberg Tables: Hassle-Free

Cloudera

They also provide a “ snapshot” procedure that creates an Iceberg table with a different name with the same underlying data. You could first create a snapshot table, run sanity checks on the snapshot table, and ensure that everything is in order. Hive creates Iceberg’s metadata files for the same exact table.

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Materialized Views in Hive for Iceberg Table Format

Cloudera

The snapshotId of the source tables involved in the materialized view are also maintained in the metadata. Subsequently, these snapshot IDs are used to determine the delta changes that should be applied to the materialized view rows. Furthermore, it is partitioned on the d_year column.

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Simplify operational data processing in data lakes using AWS Glue and Apache Hudi

AWS Big Data

Finally, by testing the framework, we summarize how it meets the aforementioned requirements. The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker. It also updates technical metadata in the AWS Glue Data Catalog.

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12 Times Faster Query Planning With Iceberg Manifest Caching in Impala

Cloudera

A range of Iceberg table analysis such as listing table’s data file, selecting table snapshot, partition filtering, and predicate filtering can be delegated through Iceberg Java API instead, obviating the need for each query engine to implement it themself. The data files and metadata files in Iceberg format are immutable.

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AWS Glue streaming application to process Amazon MSK data using AWS Glue Schema Registry

AWS Big Data

After the processed data is stored in Amazon S3, we create an AWS Glue crawler to create a Data Catalog table that acts as a metadata layer for the data. test-schema-registry MSKSchemaName Name of the schema. test The stack creation process can take around 15–20 minutes to complete. Refer to the first stack’s output.