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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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Incremental refresh for Amazon Redshift materialized views on data lake tables

AWS Big Data

Amazon Redshift is a fast, fully managed cloud data warehouse that makes it cost-effective to analyze your data using standard SQL and business intelligence tools. Customers use data lake tables to achieve cost effective storage and interoperability with other tools.

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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

This week on the keynote stages at AWS re:Invent 2024, you heard from Matt Garman, CEO, AWS, and Swami Sivasubramanian, VP of AI and Data, AWS, speak about the next generation of Amazon SageMaker , the center for all of your data, analytics, and AI. The relationship between analytics and AI is rapidly evolving.

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Reduce time to access your transactional data for analytical processing using the power of Amazon SageMaker Lakehouse and zero-ETL

AWS Big Data

However, the reality of scattered data across various systems—from data lakes to data warehouses and applications—makes it difficult to access and use data efficiently. As data volumes grow, so do the costs associated with ETL, leading to delayed insights and increased operational overhead. Choose Confirm.

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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.

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Accelerate SQL code migration from Google BigQuery to Amazon Redshift using BladeBridge

AWS Big Data

This post explores how you can use BladeBridge , a leading data environment modernization solution, to simplify and accelerate the migration of SQL code from BigQuery to Amazon Redshift. Tens of thousands of customers use Amazon Redshift every day to run analytics, processing exabytes of data for business insights.

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Ingest data from Google Analytics 4 and Google Sheets to Amazon Redshift using Amazon AppFlow

AWS Big Data

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. He specializes in migrating enterprise data warehouses to AWS Modern Data Architecture.