Category: Analytics

In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. We discuss various design patterns and architectures for extracting and cataloging valuable insights from unstructured data using AWS. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data.

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud, providing up to five times better price-performance than any other cloud data warehouse, with performance innovation out of the box at no additional cost to you. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to […]

With the release of Amazon OpenSearch Service 2.5, you can create maps with multiple layers to visualize your geographical data. You can build each layer from a different index pattern to separate data sources. Organizing the map in layers makes it more straightforward to visualize, view, and analyze geographical data. The layering also helps fetch […]

At AWS re:Invent 2022, Amazon Athena launched support for Apache Spark. With this launch, Amazon Athena supports two open-source query engines: Apache Spark and Trino. Athena Spark allows you to build Apache Spark applications using a simplified notebook experience on the Athena console or through Athena APIs. Athena Spark notebooks support PySpark and notebook magics […]

Amazon MSK Connect is a feature of Amazon Managed Streaming for Apache Kafka (Amazon MSK) that offers a fully managed Apache Kafka Connect environment on AWS. With MSK Connect, you can deploy fully managed connectors built for Kafka Connect that move data into or pull data from popular data stores like Amazon S3 and Amazon […]

In this post, we use Azure Blob Storage as an example and demonstrate how the new connector works, introduce the connector’s functions, and provide you with key steps to set it up. We provide you with prerequisites, share how to subscribe to this connector in AWS Marketplace, and describe how to create and run AWS Glue for Apache Spark jobs with it. Regarding the Azure Data Lake Storage Gen2 Connector, we highlight any major differences in this post.

SmugMug operates two very large online photo platforms, SmugMug and Flickr, enabling more than 100 million customers to safely store, search, share, and sell tens of billions of photos. Customers uploading and searching through decades of photos helped turn search into critical infrastructure, growing steadily since SmugMug first used Amazon CloudSearch in 2012, followed by […]

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure. An open table format such as Apache Hudi, Delta Lake, or Apache Iceberg is widely used to build data lakes […]

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. Today, tens of thousands of AWS customers—from Fortune 500 companies, startups, and everything in between—use Amazon Redshift to run mission-critical business intelligence (BI) dashboards, […]

In this post, we show how to migrate a data warehouse from Microsoft Azure Synapse to Redshift Serverless using AWS Schema Conversion Tool (AWS SCT) and AWS SCT data extraction agents. AWS SCT makes heterogeneous database migrations predictable by automatically converting the source database code and storage objects to a format compatible with the target database.