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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. For more information, refer SQL models. Snapshots – These implements type-2 slowly changing dimensions (SCDs) over mutable source tables.

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

AWS Big Data

For more information on streaming applications on AWS, refer to Real-time Data Streaming and Analytics. To learn more about the available optimize data executors and catalog properties, refer to the README file in the GitHub repo. For our testing, we generated about 58,176 small objects with total size of 2 GB.

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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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Use AWS Glue ETL to perform merge, partition evolution, and schema evolution on Apache Iceberg

AWS Big Data

Iceberg creates snapshots for the table contents. Each snapshot is a complete set of data files in the table at a point in time. Data files in snapshots are stored in one or more manifest files that contain a row for each data file in the table, its partition data, and its metrics.

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One of the Best Things You Can Do as a CIO

CIO Business Intelligence

On the secondary storage front, you need to figure out what to do from a replication/snapshot perspective for disaster recovery and business continuity. Data needs to be air-gapped, including logical air gapping and immutable snapshot technologies. Data security must go hand-in-hand with cyber resilience.

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End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

The following are common asks from our customers: Is it possible to develop and test AWS Glue data integration jobs on my local laptop? The software development lifecycle on AWS defines the following six phases: Plan, Design, Implement, Test, Deploy, and Maintain. Test In the testing phase, you check the implementation for bugs.

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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.

Data Lake 114