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Optimization Strategies for Iceberg Tables

Cloudera

This blog discusses a few problems that you might encounter with Iceberg tables and offers strategies on how to optimize them in each of those scenarios. Problem with too many snapshots Everytime a write operation occurs on an Iceberg table, a new snapshot is created. See Write properties.

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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.

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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.

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Streaming Ingestion for Apache Iceberg With Cloudera Stream Processing

Cloudera

In this blog post, we are going to share with you how Cloudera Stream Processing ( CSP ) is integrated with Apache Iceberg and how you can use the SQL Stream Builder ( SSB ) interface in CSP to create stateful stream processing jobs using SQL. Iceberg is a high-performance open table format for huge analytic data sets.

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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.

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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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Don’t let your data pipeline slow to a trickle of low-quality data

IBM Big Data Hub

starts at the data source, collecting data pipeline metadata across key solutions in the modern data stack like Airflow, dbt, Databricks and many more. Moreover, mean time to repair (MTTR) is also improved as contextual metadata helps data engineers focus on the source of the problem, rather than debugging where the problem stems from.