AWS Database Blog

Category: Database

Convert the NUMBER data type from Oracle to PostgreSQL – Part 2

An Oracle to PostgreSQL migration in the AWS Cloud can be a multistage process with different technologies and skills involved, starting from the assessment stage to the cutover stage. For more information about the migration process, see Database Migration—What Do You Need to Know Before You Start? and the following posts on best practices, including […]

Read More

Convert the NUMBER data type from Oracle to PostgreSQL – Part 1

An Oracle to PostgreSQL migration in the AWS Cloud can be a multistage process with different technologies and skills involved, starting from the assessment stage to the cutover stage. For more information about the migration process, see Database Migration—What Do You Need to Know Before You Start? and the following posts on best practices, including […]

Read More

Supercharge your knowledge graph using Amazon Neptune, Amazon Comprehend, and Amazon Lex

Knowledge graph applications are one of the most popular graph use cases being built on Amazon Neptune today. Knowledge graphs consolidate and integrate an organization’s information into a single location by relating data stored from structured systems (e.g., e-commerce, sales records, CRM systems) and unstructured systems (e.g., text documents, email, news articles) together in a […]

Read More

SCRAM Authentication in RDS for PostgreSQL 13

The Salted Challenge Response Authentication Mechanism (SCRAM) greatly improves the security of password-based user authentication by adding several key security features that prevent rainbow-table attacks, man-in-the-middle attacks, and stored password attacks, while also adding support for multiple hashing algorithms and passwords that contain non-ASCII characters. PostgreSQL 10 added support for SCRAM authentication, and AWS customers have […]

Read More

Use AWS Lambda functions with Amazon Neptune

Many Amazon Neptune connected data applications for knowledge graphs, identity graphs, and fraud graphs use AWS Lambda functions to query Neptune. This post provides general connection management, error handling, and workload balancing guidance for using any of the popular Gremlin drivers and language variants to connect to Neptune from a Lambda function. The connection management […]

Read More
Diagram shows a Lambda function consuming the DynamoDB streams and interacting with Amazon Comprehend and with Kinesis Firehose.

Integrate your Amazon DynamoDB table with machine learning for sentiment analysis

Amazon DynamoDB is a non-relational database that delivers reliable performance at any scale. It’s a fully managed, multi-Region, multi-active database that provides consistent single-digit millisecond latency and offers built-in security, backup and restore, and in-memory caching. DynamoDB offers a serverless and event-driven architecture, which enables you to use other AWS services to extend DynamoDB capability. […]

Read More

Amazon DynamoDB now supports audit logging and monitoring using AWS CloudTrail

Amazon DynamoDB is a fully managed, multi-Region, multi-active database that delivers reliable performance at any scale. Because of the flexible DynamoDB data model, enterprise-ready features, and industry-leading service level agreement, customers are increasingly moving sensitive workloads to DynamoDB. Regulated industries (e.g., education, media, finance, and healthcare) may require detailed information about data access activity to […]

Read More

Key considerations in moving to Graviton2 for Amazon RDS and Amazon Aurora databases

Amazon Relational Database Service (Amazon RDS) and Amazon Aurora support a multitude of instance types for you to scale your database workloads based on your needs (see Amazon RDS DB instance classes and Aurora DB instance classes, respectively). In 2020, AWS announced Amazon M6g and R6g instance types for Amazon RDS and recently announced the […]

Read More

Cost-effective disaster recovery for Amazon Aurora databases using AWS Backup

You may have stringent regulatory compliance obligations that require an effective multi-Region disaster recovery (DR) plan to mitigate a Region-wide disaster. AWS offers multiple methods to meet these needs, taking into consideration different factors such as recovery time objective (RTO), recovery point objective (RPO), and costs. In this post, I focus on how to keep […]

Read More

Export and analyze Amazon DynamoDB data in an Amazon S3 data lake in Apache Parquet format

Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It’s a fully managed, multi-region, multi-active, durable database with built-in security, backup and restore, and in-memory caching for internet-scale applications. DynamoDB can handle more than 10 trillion requests per day and can support peaks of more than 20 million […]

Read More