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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 116
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Apache Iceberg optimization: Solving the small files problem in Amazon EMR

AWS Big Data

Iceberg tables store metadata in manifest files. As the number of data files increase, the amount of metadata stored in these manifest files also increases, leading to longer query planning time. The query runtime also increases because it’s proportional to the number of data or metadata file read operations. with Spark 3.3.2,

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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 102
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From Hive Tables to Iceberg Tables: Hassle-Free

Cloudera

In this blog, I will describe a few strategies one could undertake for various use cases. 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.

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Apache Ozone Metadata Explained

Cloudera

As an important part of achieving better scalability, Ozone separates the metadata management among different services: . Ozone Manager (OM) service manages the metadata of the namespace such as volume, bucket and keys. Datanode service manages the metadata of blocks, containers and pipelines running on the datanode. .

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Now Available: Cloudera Data Science Workbench Release 1.4

Cloudera

With Experiments, data scientists can run a batch job that will: create a snapshot of model code, dependencies, and configuration parameters necessary to train the model. save the built model container, along with metadata like who built or deployed it. let the user document, test, and share the model.

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Materialized Views in Hive for Iceberg Table Format

Cloudera

Overview This blog post describes support for materialized views for the Iceberg table format. Create Iceberg materialized view For the examples in this blog, we will use three tables from the TPC-DS dataset as our base tables: store_sales, customer and date_dim. Both full and incremental rebuild of the materialized view are supported.