Flipboard

JUNE 26, 2023

These techniques utilize various machine learning (ML) based approaches. In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience.

AWS 123

AWS ML ML ETL 123

Alation

MAY 16, 2023

Data Integration A data pipeline can be used to gather data from various disparate sources in one data store. This makes it easier to compare and contrast information and provides organizations with a unified view of their data.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

Alation

MAY 16, 2023

Data Integration A data pipeline can be used to gather data from various disparate sources in one data store. This makes it easier to compare and contrast information and provides organizations with a unified view of their data.

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

SQL 141

SQL Data Analyst Data Warehouse AWS 141

AWS Machine Learning Blog

MARCH 27, 2025

In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. The same ETL workflows were running fine before the upgrade. This started occurring after upgrading to version 4.2.1.

AWS 112

AWS ETL ML ML 112