Category: Learning Levels

Amazon Redshift accelerates your time to insights with fast, easy, and secure cloud data warehousing at scale. Tens of thousands of customers rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries. You can use your preferred SQL clients to analyze your data in an Amazon Redshift data warehouse. Connect seamlessly by […]

In this post, we outline planning a POC to measure media effectiveness in a paid advertising campaign. The collaborators are a media owner (“CTV.Co,” a connected TV provider) and brand advertiser (“Coffee.Co,” a quick service restaurant company), that are analyzing their collective data to understand the impact on sales as a result of an advertising campaign.

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt focuses on the transform layer of extract, load, transform (ELT) or extract, transform, load (ETL) processes across data warehouses and databases through specific engine adapters to achieve extract and load functionality. It […]

Amazon Redshift, a widely used cloud data warehouse, has evolved significantly to meet the performance requirements of the most demanding workloads. This post covers one such new feature—the multidimensional data layout sort key. Amazon Redshift now improves your query performance by supporting multidimensional data layout sort keys, which is a new type of sort key […]

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Today, we’re excited to bring the benefits of Graviton3 to Kafka workloads, with Amazon MSK now offering M7g instances for new MSK provisioned clusters. AWS Graviton […]

Today, we’re making available a new capability of AWS Glue Data Catalog that allows generating column-level statistics for AWS Glue tables. These statistics are now integrated with the cost-based optimizers (CBO) of Amazon Athena and Amazon Redshift Spectrum, resulting in improved query performance and potential cost savings. Data lakes are designed for storing vast amounts […]

For any modern data-driven company, having smooth data integration pipelines is crucial. These pipelines pull data from various sources, transform it, and load it into destination systems for analytics and reporting. When running properly, it provides timely and trustworthy information. However, without vigilance, the varying data volumes, characteristics, and application behavior can cause data pipelines […]

Today, we are pleased to announce serverless Spark UI built into the AWS Glue console. You can now use Spark UI easily as it’s a built-in component of the AWS Glue console, enabling you to access it with a single click when examining the details of any given job run. There’s no infrastructure setup or teardown required. AWS Glue serverless Spark UI is a fully-managed serverless offering and generally starts up in a matter of seconds. Serverless Spark UI makes it significantly faster and easier to get jobs working in production because you have ready access to low level details for your job runs.

Amazon Athena is a serverless, interactive analytics service built on open source frameworks, supporting open table file formats. Athena provides a simplified, flexible way to analyze petabytes of data where it lives. You can analyze data or build applications from an Amazon Simple Storage Service (Amazon S3) data lake and 30 data sources, including on-premises […]

May 2025: This post was reviewed for accuracy. Amazon DynamoDB is a fully managed, serverless, key-value NoSQL database designed to run high-performance applications at any scale. DynamoDB offers built-in security, continuous backups, automated multi-Region replication, in-memory caching, and data import and export tools. The scalability and flexible data schema of DynamoDB make it well-suited for […]