Remove Data Lake Remove Reference Remove Snapshot Remove Unstructured Data
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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. and later supports the Apache Iceberg framework for data lakes. The snapshot points to the manifest list. AWS Glue 3.0

Data Lake 121
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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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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

For security, Kinesis Data Streams provide server-side encryption so you can meet strict data management requirements by encrypting your data at rest and Amazon Virtual Private Cloud (VPC) interface endpoints to keep traffic between your Amazon VPC and Kinesis Data Streams private.

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Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg

AWS Big Data

Major market indexes, such as S&P 500, are subject to periodic inclusions and exclusions for reasons beyond the scope of this post (for an example, refer to CoStar Group, Invitation Homes Set to Join S&P 500; Others to Join S&P 100, S&P MidCap 400, and S&P SmallCap 600 ).

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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.

Data Lake 117
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Simplify external object access in Amazon Redshift using automatic mounting of the AWS Glue Data Catalog

AWS Big Data

Amazon Redshift now makes it easier for you to run queries in AWS data lakes by automatically mounting the AWS Glue Data Catalog. You no longer have to create an external schema in Amazon Redshift to use the data lake tables cataloged in the Data Catalog.