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Modernize your legacy databases with AWS data lakes, Part 2: Build a data lake using AWS DMS data on Apache Iceberg

AWS Big Data

This is part two of a three-part series where we show how to build a data lake on AWS using a modern data architecture. This post shows how to load data from a legacy database (SQL Server) into a transactional data lake ( Apache Iceberg ) using AWS Glue. Delete the bucket.

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. They are the same.

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Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality

AWS Big Data

Apache Iceberg is an open table format that brings atomicity, consistency, isolation, and durability (ACID) transactions to data lakes, streamlining data management. One of its key features is the ability to manage data using branches. We discuss two common strategies to verify the quality of published 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. AWS Glue 3.0 The following diagram illustrates the solution architecture.

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The Unexpected Cost of Data Copies

Fortunately, a next-gen data architecture enabled by the Dremio data lake service removes the need for replicated data, helping organizations to minimize complexity, boost efficiency and dramatically reduce costs. Read this whitepaper to learn: Why organizations frequently end up with unnecessary data copies.

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Accelerate Amazon Redshift Data Lake queries with AWS Glue Data Catalog Column Statistics

AWS Big Data

Amazon Redshift enables you to efficiently query and retrieve structured and semi-structured data from open format files in Amazon S3 data lake without having to load the data into Amazon Redshift tables. Amazon Redshift extends SQL capabilities to your data lake, enabling you to run analytical queries.

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How Cloudinary transformed their petabyte scale streaming data lake with Apache Iceberg and AWS Analytics

AWS Big Data

A modern data strategy redefines and enables sharing data across the enterprise and allows for both reading and writing of a singular instance of the data using an open table format.

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