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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.

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

AWS Big Data

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. You can use either the AWS Glue Data Catalog (recommended) or a Hive catalog for Iceberg tables.

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Snowflake and Domino: Better Together

Domino Data Lab

Snowflake is a solution for data warehousing, data lakes, and data application development and specializes in securely sharing and consuming data. About Domino Data Lab. Domino Data Lab is the system-of-record for enterprise data science teams. Integration Features.

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Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg

AWS Big Data

Terminology Let’s first discuss some of the terminology used in this post: Research data lake on Amazon S3 – A data lake is a large, centralized repository that allows you to manage all your structured and unstructured data at any scale. This is where the tagging feature in Apache Iceberg comes in handy.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

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How Gupshup built their multi-tenant messaging analytics platform on Amazon Redshift

AWS Big Data

It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Moreover, no separate effort is required to process historical data versus live streaming data. Apart from incremental analytics, Redshift simplifies a lot of operational aspects.

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

AWS Big Data

The utility for cloning and experimentation is available in the open-sourced GitHub repository. This solution only replicates metadata in the Data Catalog, not the actual underlying data. This ensures that the data lake will still be functional in another Region if Lake Formation has an availability issue.