Category: Expert (400)

AWS DMS accommodates a broad range of source and target data repositories, such as relational databases, data warehouses, and NoSQL databases. Proper preparation and design are vital for a successful migration process, especially when it comes to optimizing performance and addressing potential delay issues. In this blog post, we offer guidance about recognizing potential root causes of complete load and CDC delays early in the process and provide suggestions for optimally clustering tables to achieve the best performance for an AWS DMS task.

When your network fails, finding the root cause usually takes hours of investigations, going through correlated alarms that often lead to symptoms rather than the actual problem. Root-cause analysis (RCA) systems are often built on hardcoded rules, static thresholds, and pre-defined patterns that work great until they don’t. Whether you’re troubleshooting network-level outages or service-level degradations, those rigid rule sets can’t adapt to cascading failures and complex interdependencies. In this post, we show you our AWS solution architecture that features a network digital twin using graphs and Agentic AI. We also share four runbook design patterns for Agentic AI-powered graph-based RCA on AWS. Finally, we show how DOCOMO provides real-world validation from their commercial networks of our first runbook design pattern, showing drastic MTTD improvement with 15s for failure isolation in transport and Radio Access Networks.

Organizations operating hybrid environments can now extend their self-managed Active Directory authentication to Amazon RDS for Db2 instances via a forest trust with AWS Managed Microsoft AD. While basic Kerberos authentication functions transparently in this configuration, implementing group-based access control necessitates specific configuration, particularly when collaborating with users from trusted AD domains. In this post, we show how to enable Amazon RDS for Db2 to allow authorizations of groups in a customer managed Microsoft AD through a Directory Service domain.

In this post, we discuss different ways you can use volatility classification with functions in PostgreSQL and provide best practices to help you keep your database optimized and develop efficient and reliable database applications.

Healthcare providers manage an ever-growing volume of patient data and medication information to help ensure safe, effective treatment. Although traditional database systems excel at storing patient records, they require complex queries to access information. By adding generative AI capabilities, healthcare providers can now use natural language to search patient records and verify medication safety, rather than writing complex database queries. In this post, I show you a solution that uses Amazon Bedrock and Amazon DynamoDB to create an AI agent that helps healthcare providers quickly identify potential drug interactions by validating new prescriptions against a patient’s current medication records.

Many organizations rely on Windows Authentication and Kerberos for secure access to their SQL Server databases. When using Amazon RDS for SQL Server with a self-managed Active Directory, organizations can enhance their authentication beyond the default NTLM protocol to support Kerberos authentication. In this post, we show you how to manually configure and maintain Kerberos authentication for Amazon RDS for SQL Server DB instances joined to a self-managed Active Directory. We walk through the process of configuring service principal names (SPNs), adding necessary user principal name (UPN) suffixes, and automating SPN updates to handle failovers and host replacements.

In this post, we look into Kerberos authentication for Amazon Aurora PostgreSQL-Compatible Edition using AWS Directory Service for Microsoft Active Directory, and particularly the new pg_ad_mapping extension and how it can help you manage access control more efficiently.

In this post, we walk you through a solution to aggregate RDS orphan snapshots across accounts and AWS Regions, enabling automation and organization-wide visibility to optimize cloud spend based on data-driven insights. Cross-region copied snapshots, Aurora cluster copied snapshots and shared snapshots are out of scope for this solution. The solution uses AWS Step Functions orchestration together with AWS Lambda functions to generate orphan snapshot metadata across your organization. Generated metadata information is stored in Amazon Simple Storage Service (Amazon S3) and transformed into an Amazon Athena table by AWS Glue. Amazon QuickSight uses the Athena table to generate orphan snapshot insights.

In this post, we discuss temporary tablespace and Flash Cache features with local NVMe SSD-based instance storage, configuration options, typical use cases, and feature availability by engine and storage configuration. We dive deep into the tiered cache capability and how it can improve the query performance of latency-sensitive workloads. We also provide an overview of the temporary object capability.

In this post, we provide a comprehensive guide for addressing performance considerations when migrating Oracle databases from Exadata to Amazon RDS for Oracle. We explore methods to analyze Exadata workload characteristics, including determining Smart IO usage, examining database-level I/O patterns, and identifying SQLs that utilize Exadata-specific features. We also discuss various alternatives available on RDS for Oracle to mitigate potential performance impacts.