Remove Data Warehouse Remove Events Remove Metadata Remove Snapshot
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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time. Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more.

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

AWS Big Data

AWS-powered data lakes, supported by the unmatched availability of Amazon Simple Storage Service (Amazon S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches. It will never remove files that are still required by a non-expired snapshot.

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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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From Hive Tables to Iceberg Tables: Hassle-Free

Cloudera

While these instructions are carried out for Cloudera Data Platform (CDP), Cloudera Data Engineering, and Cloudera Data Warehouse, one can extrapolate them easily to other services and other use cases as well. In all the use cases we are trying to migrate a table named “events.”

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Build a multi-Region and highly resilient modern data architecture using AWS Glue and AWS Lake Formation

AWS Big Data

This solution only replicates metadata in the Data Catalog, not the actual underlying data. To have a redundant data lake using Lake Formation and AWS Glue in an additional Region, we recommend replicating the Amazon S3-based storage using S3 replication , S3 sync, aws-s3-copy-sync-using-batch or S3 Batch replication process.

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Implement a serverless CDC process with Apache Iceberg using Amazon DynamoDB and Amazon Athena

AWS Big Data

On the Code tab, choose Test , then Configure test event. Configure a test event with the default hello-world template event JSON. Provide an event name without any changes to the template and save the test event. Provide an event name without any changes to the template and save the test event.

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

It aims to provide a framework to create low-latency streaming applications on the AWS Cloud using Amazon Kinesis Data Streams and AWS purpose-built data analytics services. In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices.

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