Remove Cost-Benefit Remove Data Warehouse Remove Reference Remove Snapshot
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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

In this post, we look into an optimal and cost-effective way of incorporating dbt within Amazon Redshift. For more information, refer SQL models. Snapshots – These implements type-2 slowly changing dimensions (SCDs) over mutable source tables. For more information, refer to Redshift set up.

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

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

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Optimization Strategies for Iceberg Tables

Cloudera

Introduction Apache Iceberg has recently grown in popularity because it adds data warehouse-like capabilities to your data lake making it easier to analyze all your data — structured and unstructured. Problem with too many snapshots Everytime a write operation occurs on an Iceberg table, a new snapshot is created.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Snowflake integrates with AWS Glue Data Catalog to access the Iceberg table catalog and the files on Amazon S3 for analytical queries. This greatly improves performance and compute cost in comparison to external tables on Snowflake , because the additional metadata improves pruning in query plans.

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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. They also provide a “ snapshot” procedure that creates an Iceberg table with a different name with the same underlying data.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor. The result is made available to the application by querying the latest snapshot. For more information, refer to Notions of Time: Event Time and Processing Time. For more information, refer to Dynamic Tables.

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

AWS Big Data

There is an increased need for data lakes to support database like features such as ACID transactions, record-level updates and deletes, time travel, and rollback. 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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