Category: Technical How-to

In this post, we show how to use AWS service integrations to minimize custom code while providing a robust platform for industrial data ingestion, processing, and analytics. By using Amazon S3 Tables and its built-in optimizations, you can maximize query performance and minimize costs without additional infrastructure setup.

In this post, we demonstrate the powerful interoperability between Amazon EMR and Databricks Unity Catalog by walking through how to enable external access to Unity Catalog, configure EMR Spark to connect seamlessly with Unity Catalog, and perform DML and DDL operations on Unity Catalog tables using EMR Serverless.

Customers new to Amazon OpenSearch Service often ask how many shards their indexes need. An index is a collection of shards, and an index’s shard count can affect both indexing and search request efficiency. OpenSearch Service can take in large amounts of data, split it into smaller units called shards, and distribute those shards across a dynamically changing set of instances. In this post, we provide some practical guidance for determining the ideal shard count for your use case.

We now have a public preview of two integrations between Amazon Simple Storage Service (Amazon S3) Vectors and Amazon OpenSearch Service that give you more flexibility in how you store and search vector embeddings. In this post, we walk through this seamless integration, providing you with flexible options for vector search implementation.

This post demonstrates how the new jobs experience works in SageMaker Unified Studio.

Today, we are excited to launch a new visual workflows builder in SageMaker Unified Studio. With the new visual workflow experience, you don’t need to code the Python DAGs manually. Instead, you can visually define the orchestration workflow in SageMaker Unified Studio, and the visual definition is automatically converted to a Python DAG definition that is supported in Airflow.This post demonstrates the new visual workflow experience in SageMaker Unified Studio.

In this post, we review how to set up Redshift Serverless to use geospatial data contained within a data lake to enhance maps in ArcGIS Pro. This technique helps builders and GIS analysts use available datasets in data lakes and transform it in Amazon Redshift to further enrich the data before presenting it on a map.

In this post, we demonstrate how to develop and monitor a Spark application using existing data in Amazon S3 using SageMaker Unified Studio. The solution addresses key challenges organizations face in managing big data analytics workloads through an integrated development environment where data teams can develop, test, and refine Spark applications while leveraging EMR Serverless for dynamic resource allocation and built-in monitoring tools.

Amazon SageMaker Unified Studio enables per-project cost allocation through resource tagging, allowing organizations to track and manage costs across different projects and domains effectively. This post demonstrates how to implement cost tracking using AWS Billing and Cost Management tools, including Cost Explorer and Data Exports, to help finance and business analysts follow FinOps best practices for controlling cloud infrastructure costs.

In this post, we show you how Stifel implemented a modern data platform using AWS services and open data standards, building an event-driven architecture for domain data products while centralizing the metadata to facilitate discovery and sharing of data products.