AWS Database Blog

MUFG Bank standardized overseas back-office procedures across roughly 30 countries by grounding generative AI in a structured domain model on Amazon Neptune and Amazon Bedrock. Its AI Flowchart application is expected to cut the system-development effort behind each branch-procedure standardization from 11 person-days to 1, a reduction of up to 90%.

Part 2 of migrating SSIS packages to Amazon Aurora PostgreSQL tackles the complex packages. Learn how to replace SSIS control flow, Foreach Loop Containers, and Script Tasks with AWS Step Functions state machines, Map and Parallel states, and Lambda, and schedule them with Amazon EventBridge in place of SQL Server Agent.

Migrating SQL Server Integration Services (SSIS) packages is one of the more complex parts of moving from SQL Server to Amazon Aurora PostgreSQL. In Part 1 of this two-part series, learn how to filter, categorize, and simplify your SSIS portfolio using native PostgreSQL features, pg_cron, and the aws_s3 and aws_lambda extensions.

Configure AWS DMS change data capture (CDC) from a self-managed SQL Server source without granting sysadmin to the endpoint login. This least-privilege pattern uses certificate-signed wrapper procedures, granular permissions, and AWS Secrets Manager to meet separation-of-duties requirements while preserving full CDC functionality.

When an Amazon Neptune workload needs resilience beyond a single cluster, this post presents a decision framework that maps availability targets to Neptune architectural patterns, from Multi-AZ and Global Database to write-ahead logs and caching, based on your RTO, RPO, and budget.

Migrating SQL Server Full-Text Search to Babelfish for Amazon Aurora PostgreSQL is one of the trickier parts of a migration. This post presents a six-checkpoint decision tree to assess whether your Full-Text Search workload can move to Babelfish, what migrates directly, what needs a custom function, and what requires an alternative approach.

Amagi built a Global Metadata Store on Amazon Neptune to model billions of interconnected media metadata relationships. Learn how their RDF knowledge graph, paired with Amazon OpenSearch Service and a custom blank-node synchronization layer, delivers deep semantic queries and sub-500ms transactional reads across 2,500+ channels and 150+ countries.

Filtered export for Amazon DynamoDB helps you export only the items and attributes you need to Amazon S3, without consuming table capacity. This post introduces the feature and shows how to recover a single tenant’s data, share a tenant’s history with selected attributes, and relocate a tenant to another Region.

Amazon Aurora DSQL supports foreign key constraints, letting you enforce referential integrity directly in the database. This post covers defining foreign keys, immediate versus deferred enforcement, adding constraints to existing tables, and how optimistic concurrency control resolves conflicts in distributed workloads.

In this post, we show you how to tune Babelfish performance through monitoring, query optimization, parameter tuning, and ongoing maintenance.