Author: Rohit Bansal

Amazon Redshift ML allows data analysts, developers, and data scientists to train machine learning (ML) models using SQL. In previous posts, we demonstrated how you can use the automatic model training capability of Redshift ML to train classification and regression models. Redshift ML allows you to create a model using SQL and specify your algorithm, […]

Amazon Redshift supports querying a wide variety of data formats, such as CSV, JSON, Parquet, and ORC, and table formats like Apache Hudi and Delta. Amazon Redshift also supports querying nested data with complex data types such as struct, array, and map. With this capability, Amazon Redshift extends your petabyte-scale data warehouse to an exabyte-scale data lake on Amazon S3 in a cost-effective manner. Apache Iceberg is the latest table format that is supported by Amazon Redshift. In this post, we show you how to query Iceberg tables using Amazon Redshift, and explore Iceberg support and options.

In modern data architectures, it’s common to store data in multiple data sources. However, organizations embracing this approach still need insights from their data and require technologies that help them break down data silos. Amazon Athena is an interactive query service that makes it easy to analyze structured, unstructured, and semi-structured data stored in Amazon […]