Category: Learning Levels

Amazon Ads enables businesses to meaningfully engage with customers throughout their shopping journey, reaching over 300 million audience in the US alone. Delivering the right ad to the right customer in real time at a global scale requires highly available, low-latency infrastructure capable of processing tens of millions of requests per second. In this post, […]

In this post, I explore how Amazon Aurora DSQL uses Amazon Time Sync Service to build a hybrid logical clock solution.

Amazon Aurora DSQL employs an active-active distributed database design, wherein all database resources are peers and serve both write and read traffic within a Region and across Regions. This design facilitates synchronous data replication and automated zero data loss failover for single and multi-Region Aurora DSQL clusters. In this post, I discuss the individual components and the responsibilities of a multi-Region distributed database to provide an ACID-compliant, strongly consistent relational database.

In this third post of the series, I examine the end-to-end processing of the two transaction types in Aurora DSQL: read-only and read-write. Amazon Aurora DSQL doesn’t have write-only transactions, since it’s imperative to verify the table schema or ensure the uniqueness of primary keys on each change – which results them being read-write transactions as well.

In this second post, I examine Aurora DSQL’s architecture and explain how its design decisions impact functionality—such as optimistic locking and PostgreSQL feature support—so you can assess compatibility with your applications. I provide a comprehensive overview of the underlying architecture, which is fully abstracted from the user.

In this post, I dive deep into fundamental concepts that are important to comprehend the benefits of Aurora DSQL, its feature set, and its underlying components.

Today, we are launching dynamic data masking feature for Amazon Aurora PostgreSQL-Compatible Edition. In this post we show how dynamic data masking can help you meet data privacy requirements. We discuss how this feature is implemented and demonstrate how it works with PostgreSQL role hierarchy.

You can now use resource governor with Amazon RDS for SQL Server to optimize your database performance by controlling how compute resources are allocated across different workloads. This post shows you how to optimize your database performance using resource governor on Amazon RDS for SQL Server. We walk you through the step-by-step process of enabling and configuring the feature, including how to set up resource pools, create workload groups, and implement classifier functions for effective resource management.

Amazon RDS for SQL Server has enhanced SQL Server 2022 Web Edition by introducing high availability through block-level replication in Multi-AZ deployments. With this release, you can quickly set up and maintain highly available databases while significantly reducing operational overhead. In this post, we discuss the benefits of block-level replication and how to get started. For more information, see Licensing Microsoft SQL Server on Amazon RDS.

Amazon DynamoDB has announced support for up to 8 attributes in composite keys for Global Secondary Indexes (GSIs). Now, you can specify up to four partition keys and four sort keys to identify items as part of a GSI, allowing you to query data at scale across multiple dimensions. In this post we show you how to design similar data models more efficiently using Global Secondary Indexes with the additional attribute support in composite keys and provide examples of DynamoDB data models with reduced complexity.