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

AWS Big Data

Systems of this nature generate a huge number of small objects and need attention to compact them to a more optimal size for faster reading, such as 128 MB, 256 MB, or 512 MB. For more information on streaming applications on AWS, refer to Real-time Data Streaming and Analytics. with Spark 3.3.2, and JupyterEnterpriseGateway 2.6.0.

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Optimize checkpointing in your Amazon Managed Service for Apache Flink applications with buffer debloating and unaligned checkpoints – Part 2

AWS Big Data

We’ve already discussed how checkpoints, when triggered by the job manager, signal all source operators to snapshot their state, which is then broadcasted as a special record called a checkpoint barrier. When barriers from all upstream partitions have arrived, the sub-task takes a snapshot of its state.

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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

It also applies general software engineering principles like integrating with git repositories, setting up DRYer code, adding functional test cases, and including external libraries. In this post, we look into an optimal and cost-effective way of incorporating dbt within Amazon Redshift. For more information, refer SQL models.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Building a starter version of anything can often be straightforward, but building something with enterprise-grade scale, security, resiliency, and performance typically requires knowledge and adherence to battle-tested best practices, and using the right tools and features in the right scenario. String-optimized compression The Data Vault 2.0

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Whenever there is an update to the Iceberg table, a new snapshot of the table is created, and the metadata pointer points to the current table metadata file. At the top of the hierarchy is the metadata file, which stores information about the table’s schema, partition information, and snapshots.

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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. As of this writing, the “__BACKUP__” suffix is hardcoded.

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Simplifying data processing at Capitec with Amazon Redshift integration for Apache Spark

AWS Big Data

Your applications can seamlessly read from and write to your Amazon Redshift data warehouse while maintaining optimal performance and transactional consistency. Additionally, you’ll benefit from performance improvements through pushdown optimizations, further enhancing the efficiency of your operations.