AWS Database Blog
Optimize your database storage for Oracle workloads on AWS, Part 1: Using ADO and ILM data compression policies
In this two-part series, we demonstrate how to optimize storage for Oracle database workloads on AWS by using Oracle’s built-in features, such as Heat Map, Automatic Data Optimization (ADO), and hybrid partitioning. These features help classify data by its lifecycle stage and automate data management tasks to significantly reduce storage costs, while enhancing database performance, especially for growing datasets. In this post, we explore how to use ADO and Oracle ILM policies to automatically compress data based on usage patterns.
Benchmark Amazon RDS for PostgreSQL with Dedicated Log Volumes
In this post, we guide you through the process of benchmarking the performance of Amazon RDS for PostgreSQL using the Dedicated Log Volume (DLV) feature. To do this, we use pgbench – a tool for running benchmark tests on PostgreSQL databases, pgbench repeatedly executes a defined sequence of SQL commands across multiple concurrent database sessions. Through our benchmarking, you’ll learn how to quantify the performance improvements delivered by DLV.
New – Amazon DynamoDB lowers pricing for on-demand throughput and global tables
Our continued engineering investments on how efficiently we can operate DynamoDB allow us to identify and pass on cost savings to you. Effective November 1, 2024, DynamoDB has reduced prices for on-demand throughput by 50% and global tables by up to 67%, making it more cost-effective than ever to build, scale, and optimize applications. In this post, we discuss the benefits of these price reductions, on-demand mode, and global tables.
Pre-warming Amazon DynamoDB tables with warm throughput
We’re introducing warm throughput, a new capability that provides insight into the throughput your DynamoDB tables and indexes can instantly support and allows you to pre-warm for optimized performance. In this post, we’ll introduce warm throughput, explain how it works, and explore the benefits it offers for handling high-traffic scenarios. We’ll also cover best practices and practical use cases to help you make the most of this feature for your DynamoDB tables and indexes.
Automate the deployment of Amazon RDS for Db2 Instances with Terraform
Infrastructure as Code (IaC) is the practice of provisioning and managing your computing infrastructure using code, rather than manual processes and settings. Popular IaC tools, services, and platforms include Terraform, AWS CloudFormation, Ansible, and Pulumi, each offering unique features to automate and manage infrastructure across various cloud environments. In this post, we demonstrate how Terraform, one of our partner products, can be used to deploy and manage RDS for Db2 instance.
Understanding time-series data and why it matters
In this post, we discuss the nature of time-series data, its presence across different types of industries and various use cases it enables.Time-series data is one of the most valuable types of data used today by organizations across industries. Time-series data allows for a more in-depth understanding of changes, patterns, and trends over time. This enables organizations to gain insights into past behaviors and current states, as well as predict future values. The sequential tracking of data at precise time intervals enables both retrospective and prospective analysis that is extremely valuable for strategy, planning, and decision-making across industries. In this post, we discuss the nature of time-series data, its presence across different types of industries and various use cases it enables.
Build scalable, event-driven architectures with Amazon DynamoDB and AWS Lambda
By combining DynamoDB streams with Lambda, you can build responsive, scalable, and cost-effective systems that automatically react to data changes in real time. In this post, we explore best practices for architecting event-driven systems using DynamoDB and Lambda. DynamoDB provides two options for capturing data changes (CDC): DynamoDB streams and Amazon Kinesis Data Streams (KDS). In this post, we focus exclusively on DynamoDB streams.
Use Amazon ElastiCache as a cache for Amazon Keyspaces (for Apache Cassandra)
In this post, we show you how to use Amazon ElastiCache as a write-through cache for an application that uses an Amazon Keyspaces (for Apache Cassandra) table to store data about book awards. We use a Cassandra Python client driver to access Amazon Keyspaces programmatically and a Redis client to connect to the ElastiCache cluster.
Building a GDPR compliance solution with Amazon DynamoDB
In this post, AWS Service Sector Industry Solutions shares our journey in developing a feature that enables customers to efficiently locate and delete personal data upon request, helping them meet GDPR compliance requirements. The mission of the Service Sector Solutions Engineering Team is to accelerate AWS Cloud adoption across diverse industries, including Travel, Hospitality, Gaming, and Entertainment. We work with customers from Cruise Lines, Lodging, Alternative Accommodation, Travel Agencies, Airports, Airlines, Restaurants, Catering, Casinos, Lotteries, and more.
Heterogeneous data sources: Access your data in PostgreSQL from Amazon RDS for Oracle using Oracle Database Gateway
In certain customer scenarios, Amazon RDS for Oracle databases need to connect to external data sources, such as RDS for PostgreSQL. PostgreSQL can establish connections to Oracle databases using a foreign data wrapper (FDW). In this post, we walk you through setting up an EC2 instance as a database gateway server. You will install and configure Oracle Database Gateway for ODBC (DG4ODBC), ODBC drivers, a PostgreSQL client, and PostgreSQL libraries. With this setup, you can create database links on RDS for Oracle to connect to PostgreSQL through this gateway.