Remove 2023 Remove Data Warehouse Remove Snapshot
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Load data incrementally from transactional data lakes to data warehouses

AWS Big Data

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure. Delta Lake doesn’t have a specific concept for incremental queries.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

This post was co-written with Dipankar Mazumdar, Staff Data Engineering Advocate with AWS Partner OneHouse. Data architecture has evolved significantly to handle growing data volumes and diverse workloads. Querying all snapshots, we can see that we created three snapshots with overwrites after the initial one.

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Use Amazon Athena with Spark SQL for your open-source transactional table formats

AWS Big Data

These formats enable ACID (atomicity, consistency, isolation, durability) transactions, upserts, and deletes, and advanced features such as time travel and snapshots that were previously only available in data warehouses. It will never remove files that are still required by a non-expired snapshot.

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Improve operational efficiencies of Apache Iceberg tables built on Amazon S3 data lakes

AWS Big Data

RIO is really great",date("2023-04-06"),2023)""") You can check the new snapshot is created after this append operation by querying the Iceberg snapshot: spark.sql("""SELECT * FROM dev.db.amazon_reviews_iceberg.snapshots""").show() We expire the old snapshots from the table and keep only the last two.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

They enable transactions on top of data lakes and can simplify data storage, management, ingestion, and processing. These transactional data lakes combine features from both the data lake and the data warehouse. The Data Catalog provides a central location to govern and keep track of the schema and metadata.

Data Lake 122
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Achieve near real time operational analytics using Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift

AWS Big Data

and zero-ETL support) as the source, and a Redshift data warehouse as the target. The integration replicates data from the source database into the target data warehouse. Additionally, you can choose the capacity, to limit the compute resources of the data warehouse. For this post, set this to 8 RPUs.

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Find the best Amazon Redshift configuration for your workload using Redshift Test Drive

AWS Big Data

Amazon Redshift is a widely used, fully managed, petabyte-scale cloud data warehouse. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Take a snapshot of the source Redshift data warehouse.

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