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Optimization Strategies for Iceberg Tables

Cloudera

It offers several benefits such as schema evolution, hidden partitioning, time travel, and more that improve the productivity of data engineers and data analysts. 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.

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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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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 is designed to support these features on cost-effective petabyte-scale data lakes on Amazon S3. The snapshot points to the manifest list.

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From Hive Tables to Iceberg Tables: Hassle-Free

Cloudera

However, as there are already 25 million terabytes of data stored in the Hive table format, migrating existing tables in the Hive table format into the Iceberg table format is necessary for performance and cost. In this blog, I will describe a few strategies one could undertake for various use cases.

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

IBM Big Data Hub

With the average cost of bad data reaching $15M, 2 ignoring the problem is a significant pitfall. . starts at the data source, collecting data pipeline metadata across key solutions in the modern data stack like Airflow, dbt, Databricks and many more. Businesses of all sizes, in all industries are facing a data quality problem.

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Enforce fine-grained access control on Open Table Formats via Amazon EMR integrated with AWS Lake Formation

AWS Big Data

By preserving historical versions, data lake time travel provides benefits such as auditing and compliance, data recovery and rollback, reproducible analysis, and data exploration at different points in time. Besides demonstrating with Hudi here, we will follow up with other OTF tables with other blogs.

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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.