Author: Amit Maindola

In this post, we demonstrate how to use Amazon Bedrock Flows in SageMaker Unified Studio to build a sophisticated generative AI application for financial analysis and investment decision-making.

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.

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.

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. Using Spark SQL to run Hive workloads provides not only the simplicity of SQL-like queries but also taps into the exceptional speed and performance provided by Spark. Spark SQL is an Apache Spark module for structured data processing. One […]

Many organizations build data lakes on Amazon Simple Storage Service (Amazon S3) using a modern architecture for a scalable and cost-effective solution. Open-source storage formats like Parquet and Avro are commonly used, and data is stored in these formats as immutable files. As the data lake is expanded to additional use cases, there are still […]