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 137

SQL Data Analyst Data Warehouse AWS 137

Hacker News

JUNE 29, 2025

This is exactly the kind of thing I've had in mind as one of the offshoots for PRQL for processing data beyond just generating SQL. A dynamic runtime on top of the eBPF virtual machine / SQL workbench that lets you create real time visualizations of system performance data. This seems way more friendly.

AI 84

AI AI Database Python 84

Data Science Blog

MAY 20, 2024

It also supports a wide range of data warehouses, analytical databases, data lakes, frontends, and pipelines/ETL. This includes the creation of SQL Code, DACPAC files, SSIS packages, Data Factory ARM templates, and XMLA files. Pipelines/ETL : It supports SQL Server Integration Packages (SSIS), Azure Data Factory 2.0

Data Pipeline 262

Data Pipeline Data Warehouse Azure Data Lakes 262

AWS Machine Learning Blog

APRIL 16, 2024

They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference. Previously, data scientists often found themselves juggling multiple tools to support SQL in their workflow, which hindered productivity.

SQL 128

SQL AWS Database Data Scientist 128