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

AWS Big Data

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. In early 2022, AWS announced general availability of Athena ACID transactions, powered by Apache Iceberg. and later supports the Apache Iceberg framework for data lakes.

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Implement tag-based access control for your data lake and Amazon Redshift data sharing with AWS Lake Formation

AWS Big Data

This leads to having data across many instances of data warehouses and data lakes using a modern data architecture in separate AWS accounts. We recently announced the integration of Amazon Redshift data sharing with AWS Lake Formation.

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Build a real-time GDPR-aligned Apache Iceberg data lake

AWS Big Data

Data lakes are a popular choice for today’s organizations to store their data around their business activities. As a best practice of a data lake design, data should be immutable once stored. A data lake built on AWS uses Amazon Simple Storage Service (Amazon S3) as its primary storage environment.

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Perform upserts in a data lake using Amazon Athena and Apache Iceberg

AWS Big Data

Amazon Athena supports the MERGE command on Apache Iceberg tables, which allows you to perform inserts, updates, and deletes in your data lake at scale using familiar SQL statements that are compliant with ACID (Atomic, Consistent, Isolated, Durable). Create a table to point to the CDC data. Upload 20220922-184314489.csv

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Open Data Lakehouse powered by Iceberg for all your Data Warehouse needs

Cloudera

Cloudera Contributors: Ayush Saxena, Tamas Mate, Simhadri Govindappa Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), we are excited to see customers testing their analytic workloads on Iceberg. We will publish follow up blogs for other data services.

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

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Has the Data Warehouse Had Its Day?

BI-Survey

Statements from countless interviews with our customers reveal that the data warehouse is seen as a “black box” by many and understood by few business users. Therefore, it is not clear why the costly and apparently flexibility-inhibiting data warehouse is needed at all. The limiting factor is rather the data landscape.