Category: Intermediate (200)

In this post, we explore the new features in PostgreSQL 16 and discuss how they improve performance and query speed. This includes new replication features, including logical decoding on standbys and parallel application of logical replication, SQL/JSON functionality, new monitoring tools, such as the pg_stat_io system view, and security features.

In this post, we provide different options for data migration from an SAP ASE on-premises database to SAP ASE on HAQM Elastic Compute Cloud (HAQM EC2) based on the size of data, application downtime, and data compliance. The migration methods include using AWS Database Migration Service (AWS DMS) and SAP ASE native features.

In this post, we show you how the Gen team replayed an Oracle database workload for a mission-critical application on HAQM Aurora PostgreSQL-Compatible Edition.

We’re excited to announce Valkey General Language Independent Driver for the Enterprise (GLIDE), an open source permissively licensed (Apache 2.0 license) Valkey client library. In this post, we discuss the benefits of Valkey GLIDE.

In this post, we explore the key considerations and best practices for implementing effective DR strategies for your HAQM Neptune database deployments.

In a previous post, we described how to connect HAQM Neptune to metaphactory, securely, and then how to explore and search the Neptune graph data using metaphactory. In this post, we show how you can use metaphactory to build an end user application using its dynamic model driven components, driven by SPARQL queries.

In this post, we show you can implement T-SQL global temporary table behavior in Babelfish for Aurora PostgreSQL using permanent table.

In this post, we walk you through how to configure Secure Sockets Layer (SSL) encryption between the source endpoints in AWS DMS and an on-premises SAP ASE source for secure data transfer. We also show you the steps for enabling SSL on an on-premises SAP ASE database. Configuring SSL encryption on source endpoints enables encrypting data in transit during the database migration process for enhanced security.

In this post, we discuss how HAQM DynamoDB helps media and entertainment customers overcome these challenges for streaming and media supply chain workloads. We also share customer examples, such as Disney, Warner Bros. Discovery, ViacomCBS, and other media applications that are built with DynamoDB.

In this post, we discuss how to implement Aurora ML performance optimizations to perform real-time inference against a SageMaker endpoint at a large scale. More specifically, we simulate an OLTP workload against the database, where multiple clients are making simultaneous calls against the database and are putting the SageMaker endpoint under stress to respond to thousands of requests in a short time window. Moreover, we show how to use SQL triggers to create an automatic orchestration pipeline for your predictive workload without using additional services.