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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.

Data Lake 100
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From data lakes to insights: dbt adapter for Amazon Athena now supported in dbt Cloud

AWS Big Data

The need for streamlined data transformations As organizations increasingly adopt cloud-based data lakes and warehouses, the demand for efficient data transformation tools has grown. This enables you to extract insights from your data without the complexity of managing infrastructure.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

This post was co-written with Dipankar Mazumdar, Staff Data Engineering Advocate with AWS Partner OneHouse. Data architecture has evolved significantly to handle growing data volumes and diverse workloads. In practice, OTFs are used in a broad range of analytical workloads, from business intelligence to machine learning.

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Unleash deeper insights with Amazon Redshift data sharing for data lake tables

AWS Big Data

Over the years, this customer-centric approach has led to the introduction of groundbreaking features such as zero-ETL , data sharing , streaming ingestion , data lake integration , Amazon Redshift ML , Amazon Q generative SQL , and transactional data lake capabilities.

Data Lake 101
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How Volkswagen streamlined access to data across multiple data lakes using Amazon DataZone – Part 1

AWS Big Data

Over the years, organizations have invested in creating purpose-built, cloud-based data lakes that are siloed from one another. A major challenge is enabling cross-organization discovery and access to data across these multiple data lakes, each built on different technology stacks.

Data Lake 122
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How Cloudinary transformed their petabyte scale streaming data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Enterprises and organizations across the globe want to harness the power of data to make better decisions by putting data at the center of every decision-making process. However, throughout history, data services have held dominion over their customers’ data.

Data Lake 121
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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

While traditional extract, transform, and load (ETL) processes have long been a staple of data integration due to its flexibility, for common use cases such as replication and ingestion, they often prove time-consuming, complex, and less adaptable to the fast-changing demands of modern data architectures.