Category: Learning Levels

Amazon Redshift now enables the secure sharing of data lake tables—also known as external tables or Amazon Redshift Spectrum tables—that are managed in the AWS Glue Data Catalog, as well as Redshift views referencing those data lake tables. By using granular access controls, data sharing in Amazon Redshift helps data owners maintain tight governance over who can access the shared information. In this post, we explore powerful use cases that demonstrate how you can enhance cross-team and cross-organizational collaboration, reduce overhead, and unlock new insights by using this innovative data sharing functionality.

This post demonstrates a method to selectively install Python dependencies based on the Amazon MWAA component type (web server scheduler, or worker) from a Git repository only accessible from your virtual private cloud (VPC).

We recently announced a new capacity level of 30TB for time series data per account per AWS Region. The OpenSearch Serverless compute capacity for data ingestion and search/query is measured in OpenSearch Compute Units (OCUs), which are shared among various collections with the same AWS Key Management Service (AWS KMS) key. This post discusses how you can analyze 30TB time series datasets with OpenSearch Serverless.

This post demonstrates how to implement a dynamic rules engine using Amazon Managed Service for Apache Flink. Our implementation provides the ability to create dynamic rules that can be created and updated without the need to change or redeploy the underlying code or implementation of the rules engine itself. We discuss the architecture, the key services of the implementation, some implementation details that you can use to build your own rules engine, and an AWS Cloud Development Kit (AWS CDK) project to deploy this in your own account.

In this post, we explore a solution using AWS Lake Formation and AWS IAM Identity Center to address the complex challenges of managing and governing legacy data during digital transformation. We demonstrate how enterprises can effectively preserve historical data while enforcing compliance and maintaining user entitlements. This solution enables your organization to maintain robust audit trails, enforce governance controls, and provide secure, role-based access to data.

In this post, we outline two solutions that provide cross-Region resiliency without needing to reestablish relationships during a failback, using an active-active replication model with Amazon OpenSearch Ingestion (OSI) and Amazon Simple Storage Service (Amazon S3). These solutions apply to both OpenSearch Service managed clusters and OpenSearch Serverless collections. We use OpenSearch Serverless as an example for the configurations in this post.

Amazon OpenSearch Service Pricing is based on three dimensions: instances, storage, and data transfer. Storage pricing depends on the chosen storage type and also the storage tier. Visibility into domain-level charges enables accurate budgeting, efficient resource allocation, fair cost attribution across projects, and overall cost transparency. In this post, we show you how to view the OpenSearch Service domain-level cost using AWS Cost Explorer.

Salesforce and Amazon have collaborated to help customers unlock value from unified data and accelerate time to insights with bidirectional Zero Copy data sharing between Salesforce Data Cloud and Amazon Redshift. In the Part 1 of this series, we discussed how to configure data sharing between Salesforce Data Cloud and customers’ AWS accounts in the same AWS Region. In this post, we discuss the architecture and implementation details of cross-Region data sharing between Salesforce Data Cloud and customers’ AWS accounts.

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 […]

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 […]