Amazon Neptune announces support for versions 2.3 and 2.5 of Amazon OpenSearch for a Neptune versions and support for OpenSearch Serverless with Neptune engine version 1.2.2.0 and higher. This is in addition to previously supported Elasticsearch 7.1 or higher versions, to allow customers to perform full text search within their graphs. Customers can now use search indexing capabilities with OpenSearch 2.3 and 2.5 , and Serverless with their graph data stored in Neptune.

Today, Amazon Neptune announces the support for Neptune’s data APIs in the AWS SDKs. This launch provides developers access to Neptune’s 40+ data plane APIs using the AWS SDK. You can now utilize Neptune to perform tasks such as data loading or query execution with SDKs in Gremlin and openCypher query languages. 

Today, we are announcing the general availability of Amazon Neptune I/O-Optimized, a new configuration for Amazon Neptune Database that provides improved price performance and predictable pricing for customers with I/O-intensive applications. With Neptune I/O-Optimized, there are zero charges for read and write I/O operations—you only pay for your database instances and storage usage, making it easy to predict your database spend up front. Neptune I/O-Optimized offers up to 40% cost savings for I/O-intensive applications where I/O charges exceed 25% of the total Neptune database spend.

Parameter Store, a capability of AWS Systems Manager that provides secure storage for configuration data, now allows you to share advanced-tier parameters with other AWS accounts, enabling you to centrally manage your configuration data. Parameters are key-value pairs that you can reference in code and through several AWS integrations such as AWS CloudFormation and Amazon EC2. Today, many customers have workloads in multiple AWS accounts that depend on shared configuration data. Now, you can maintain a single source of truth for configuration data by sharing parameters with other accounts that need access rather than manually duplicating and synchronizing data across accounts.

AWS Supply Chain now allows demand planners to input factors (demand drivers) that could influence forecasted demand. Demand Driver Based Forecasting is an adaptive approach that enables machine learning models to capture influence factors that improve forecast accuracy.

Amazon DynamoDB zero-ETL integration with Amazon Redshift is now available in limited preview in the US East (Virginia) region. This zero-ETL integration enables customers to run high performance analytics on their DynamoDB data and has no impact on production workloads running on DynamoDB. As data is written into a DynamoDB table, it is seamlessly made available in Amazon Redshift, eliminating the need for customers to build and maintain complex data pipelines for performing extract, transform, and load (ETL) operations.

Amazon Relational Database Service (RDS) for PostgreSQL now supports the latest minor versions PostgreSQL 16.2, 15.6, 14.11, 13.14, and 12.18. This release of RDS for PostgreSQL also includes support for pgvector 0.6.0, which adds performance improvements for building Hierarchical Navigable Small Worlds (HNSW) indexes including parallelism and in-memory builds. We recommend that you upgrade to the latest minor versions to fix the known security vulnerabilities in prior versions of PostgreSQL, and to benefit from the bug fixes, performance improvements, and new functionality added by the PostgreSQL community.

We are pleased to announce general availability of Amazon EC2 G5 instances in Asia Pacific (Jakarta), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Israel (Tel Aviv), Middle East (UAE), and US East (Ohio) on SageMaker Studio notebooks. We are also pleased to announce general availability of G5 instances in Asia Pacific (Jakarta), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (London), Europe (Stockholm), Israel (Tel Aviv), Middle East (UAE), South America (São Paulo), and US East (Ohio) on SageMaker notebook instances.

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