Category: Advanced (300)

AWS Schema Conversion Tool (AWS SCT) makes self-managed data warehouse migrations predictable by assessing and converting the source database schema and code objects to a format compatible with Amazon Redshift. In this post, we describe how to perform a database assessment and conversion from Teradata to Amazon Redshift. To accomplish this, we use the AWS SCT and its CLI, because it provides support for Teradata as a source database, complementing the wide range of assessments handled by AWS Database Migration Service (AWS DMS) Schema Conversion (DMS SC).

In this post, we explore the benefits and features of Multi-AZ for RDS Custom for Oracle and how it helps improve the resilience of your database.

In this post, we demonstrate how to configure a standby replica for your RDS for Db2 instance. We also discuss best practices for setting up, monitoring, and managing standby replicas.

In this post, we show you how to evolve your DynamoDB table’s data model to meet changing application requirements while maintaining zero downtime in production systems. We explore two main techniques with examples that you can apply to your own applications: Adding new attributes and Creating new entities.

In this post, we discuss handling large collections that are approaching 32 TiB for Amazon DocumentDB. We demonstrate solutions for transitioning from uncompressed to compressed collections using AWS DMS. This migration not only accommodates larger uncompressed data volumes, but also significantly reduces storage, compute costs associated with Amazon DocumentDB and improves performance.

Last week, AWS announced Amazon Keyspaces change data capture (CDC) streams, a new feature that captures real-time data changes in your Amazon Keyspaces tables. In this post, we discuss the architecture of Amazon Keyspaces CDC streams, explore its use cases and benefits, and provide an example demonstrating how to set up CDC streams, stream data, and capture the streamed records.

The transition from SQL-based access patterns to a DynamoDB API-driven approach presents opportunities to optimize how your application interacts with its data layer. This final part of our series focuses on implementing an effective abstraction layer and handling various data access patterns in DynamoDB.

In this post, we explore strategies for designing DynamoDB data models, including entity identification, table design decisions, and relationship modeling approaches. We examine practical scenarios comparing different modeling strategies, helping you make informed decisions for your specific use case.

AWS DMS customers might choose not to use the data validation feature provided by the AWS DMS service due to the time it takes to complete validation after a load, a large dataset transfer or a data reload, where business requires rapid availability of data in the target environment. As a result, you might opt to perform validation manually or use a single pass full load only validation, which requires additional effort and time. In this post, we walk you through how to build a custom AWS DMS data validation solution with AWS serverless services.

Fluent Commerce, an omnichannel commerce platform, offers order management solutions that enable businesses to deliver seamless shopping experiences across various channels. Fluent uses Amazon Aurora PostgreSQL-Compatible Edition as its high-performance OLTP database engine to process their customers’ intricate search queries efficiently. Fluent Commerce strategically combined AWS-based upgrade approaches—including snapshot restores and AWS DMS ongoing replication—to seamlessly upgrade their 32 TB Aurora PostgreSQL databases with minimal downtime. In this post, we explore a pragmatic and cost-effective approach to achieve near-zero downtime during database upgrades. We explore the method of using the snapshot and restore method followed by continuous replication using AWS DMS.