AWS Database Blog
Category: Technical How-to
Assess and convert Teradata database objects to Amazon Redshift using the AWS Schema Conversion Tool CLI
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).
Multi-AZ deployment for Amazon RDS Custom for Oracle
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.
Volatility classification in PostgreSQL
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.
Configure Amazon RDS for Db2 standby replicas for high availability and faster disaster recovery
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.
Evolve your Amazon DynamoDB table’s data model
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.
Transform uncompressed Amazon DocumentDB data into compressed collections using AWS DMS
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.
Introducing Amazon Keyspaces CDC streams
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.
How to evaluate throughput utilization for Amazon DynamoDB tables in provisioned mode
In this post, we demonstrate how to evaluate throughput utilization for DynamoDB tables in provisioned mode. Understanding this metrics helps you determine whether switching to on-demand mode is the right choice. Moving to on-demand mode, where you pay-per-request for throughput, can optimize costs, eliminate capacity planning, minimize operational overhead, and enhance overall user experience for your applications.
SQL to NoSQL: Modernizing data access layer with Amazon DynamoDB
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.
SQL to NoSQL: Modeling data in Amazon 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.