AWS Big Data Blog

Category: Intermediate (200)

architecture

The AWS Glue Data Catalog now supports storage optimization of Apache Iceberg tables

The AWS Glue Data Catalog now enhances managed table optimization of Apache Iceberg tables by automatically removing data files that are no longer needed. Along with the Glue Data Catalog’s automated compaction feature, these storage optimizations can help you reduce metadata overhead, control storage costs, and improve query performance. Iceberg creates a new version called […]

Differentiate generative AI applications with your data using AWS analytics and managed databases

While the potential of generative artificial intelligence (AI) is increasingly under evaluation, organizations are at different stages in defining their generative AI vision. In many organizations, the focus is on large language models (LLMs), and foundation models (FMs) more broadly. This is just the tip of the iceberg, because what enables you to obtain differential […]

Developer guidance on how to do local testing with Amazon MSK Serverless

In this post, I present you with guidance on how developers can connect to Amazon MSK Serverless from local environments. The connection is done using an Amazon MSK endpoint through an SSH tunnel and a bastion host. This enables developers to experiment and test locally, without needing to setup a separate Kafka cluster.

Solution Architecture

Publish and enrich real-time financial data feeds using Amazon MSK and Amazon Managed Service for Apache Flink

In this post, we demonstrate how you can publish an enriched real-time data feed on AWS using Amazon Managed Streaming for Kafka (Amazon MSK) and Amazon Managed Service for Apache Flink. You can apply this architecture pattern to various use cases within the capital markets industry; we discuss some of those use cases in this post.

A box indicating Amazon Redshift in the center of the image with boxes from right to left for Amazon RDS MySQL and PostgreSQL, Amazon Aurora MySQL and PostreSQL, Amazon EMR, Amazon Glue, Amazon S3 bucket, Amazon Managed Streaming for Apache Kafka and Amazon Kinesis. Each box has an arrow pointing to Amazon Redshift. Each arrow has the following labels: Amazon RDS & Amazon Aurora: zero-ETL and federated queries; AWS Glue and Amazon EMR: spark connector; Amazon S3 bucket: COPY command; Amazon Managed Streaming for Apache Kafka and Amazon Kinesis: redshift streaming. Amazon Data Firehose has an arrow pointing to Amazon S3 bucket indicating the data flow direction.

Amazon Redshift data ingestion options

Amazon Redshift, a warehousing service, offers a variety of options for ingesting data from diverse sources into its high-performance, scalable environment. Whether your data resides in operational databases, data lakes, on-premises systems, Amazon Elastic Compute Cloud (Amazon EC2), or other AWS services, Amazon Redshift provides multiple ingestion methods to meet your specific needs. The currently […]

Solution Overview

Use the AWS CDK with the Data Solutions Framework to provision and manage Amazon Redshift Serverless

In this post, we demonstrate how to use the AWS CDK and DSF to create a multi-data warehouse platform based on Amazon Redshift Serverless. DSF simplifies the provisioning of Redshift Serverless, initialization and cataloging of data, and data sharing between different data warehouse deployments.

Integrate Tableau and Microsoft Entra ID with Amazon Redshift using AWS IAM Identity Center

This blog post provides a step-by-step guide to integrating IAM Identity Center with Microsoft Entra ID as the IdP and configuring Amazon Redshift as an AWS managed application. Additionally, you’ll learn how to set up the Amazon Redshift driver in Tableau, enabling SSO directly within Tableau Desktop.

Attribute Amazon EMR on EC2 costs to your end-users

In this post, we share a chargeback model that you can use to track and allocate the costs of Spark workloads running on Amazon EMR on EC2 clusters. We describe an approach that assigns Amazon EMR costs to different jobs, teams, or lines of business. You can use this feature to distribute costs across various business units. This can assist you in monitoring the return on investment for your Spark-based workloads.

High-level architecture overview

Copy and mask PII between Amazon RDS databases using visual ETL jobs in AWS Glue Studio

In this post, I’ll walk you through how to copy data from one Amazon Relational Database Service (Amazon RDS) for PostgreSQL database to another, while scrubbing PII along the way using AWS Glue. You will learn how to prepare a multi-account environment to access the databases from AWS Glue, and how to model an ETL data flow that automatically masks PII as part of the transfer process, so that no sensitive information will be copied to the target database in its original form.

Use AWS Glue to streamline SFTP data processing

In this blog post, we explore how to use the SFTP Connector for AWS Glue from the AWS Marketplace to efficiently process data from Secure File Transfer Protocol (SFTP) servers into Amazon Simple Storage Service (Amazon S3), further empowering your data analytics and insights.