AWS Database Blog
Category: Technical How-to
Use Amazon Neptune Analytics to analyze relationships in your data faster, Part 2: Enhancing fraud detection with Parquet and CSV import and export
In this two-part series, we show how you can import and export using Parquet and CSV to quickly gather insights from your existing graph data. In Part 1, we introduced the import and export functionalities, and walked you through how to quickly get started with them. In this post, we show how you can use the new data mobility improvements in Neptune Analytics to enhance fraud detection.
Use Amazon Neptune Analytics to analyze relationships in your data faster, Part 1: Introducing Parquet and CSV import and export
In this two-part series, we show how you can import and export using Parquet and CSV to quickly gather insights from your existing graph data. Part 1 introduces the import and export functionalities, and walks you through how to quickly get started with them. In Part 2, we show how you can use the new data mobility improvements in Neptune Analytics to enhance fraud detection.
JSON serialization using Serde Rust crates in Amazon RDS for PostgreSQL
In this post, we showcase how to use PGRX and PL/Rust to efficiently access and manipulate all built-in PostgreSQL data types in Rust. We demonstrate how to write performant functions that create and serialize JSON objects that include these built-in types. These functions are directly usable in your database and use the newly supported serde and serde_json crates. We also walk through deploying an Amazon RDS for PostgreSQL instance with PL/Rust enabled, and how PGRX type mapping allows you to use all built-in PostgreSQL types in a JSON object.
Migrate spatial columns from Oracle to Amazon Aurora PostgreSQL or Amazon RDS for PostgreSQL using AWS DMS
In this post, we discuss configurations in AWS DMS endpoints and AWS DMS tasks to migrate spatial columns from Oracle to Aurora PostgreSQL-Compatible efficiently.
Monitoring your Amazon Aurora PostgreSQL-Compatible and Amazon RDS PostgreSQL from integer sequence overflow
In this post, we discuss integer sequence overflow, its causes, and—most importantly—how to efficiently set up alerts using Amazon SNS and use AWS Lambda to resolve such issues in Amazon Aurora PostgreSQL-Compatible Edition and Amazon RDS for PostgreSQL.
Querying and writing to MySQL and MariaDB from Amazon Aurora and Amazon RDS for PostgreSQL using the mysql_fdw extension, Part 2: Handling foreign objects
In this post, we focus on working with the features of mysql_fdw PostgreSQL extension on Amazon RDS for PostgreSQL to help manage a large set of data that on an external database scenarios. It enables you to interact with your MySQL database for importing individual/large/selectively number of objects at the schema level and simplifying how we get information about the MySQL/MariaDB schema, to make it easier to ultimately read/write data. We will also provide an introduction to understand query performance on foreign tables.
Dynamic data masking in Amazon RDS for PostgreSQL, Amazon Aurora PostgreSQL, and Babelfish for Aurora PostgreSQL
There are a variety of different techniques available to support data masking in databases, each with their trade-offs. In this post, we explore dynamic data masking, a technique that returns anonymized data from a query without modifying the underlying data. In this post, we discuss a dynamic data masking technique based on dynamic masking views. These views mask personally identifiable information (PII) columns for unauthorized users. This post discusses how to implement this technique in Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL including Babelfish for Aurora PostgreSQL.
Implement automatic conflict detection and resolution for Oracle GoldenGate bi-directional replication between Amazon RDS for Oracle databases
In this post, we show how to implement automatic conflict detection and resolution (Auto-CDR) for Oracle GoldenGate bi-directional replication between Amazon RDS for Oracle databases.
Improve Amazon Timestream for InfluxDB security posture by automating rotation for long-lived credentials
In this post, we walk you through how to make your Amazon Timestream for InfluxDB deployments more secure by offering a mechanism to automatically rotate long-lived credentials. We use AWS Secrets Manager to store your tokens and user credentials as secrets and rotate the secrets using the included AWS Lambda functions.
Transition from AWS DMS to zero-ETL to simplify real-time data integration with Amazon Redshift
The zero-ETL integrations for Amazon Redshift are designed to automate data movement into Amazon Redshift, eliminating the need for traditional ETL pipelines. With zero-ETL integrations, you can reduce operational overhead, lower costs, and accelerate your data-driven initiatives. This enables organizations to focus more on deriving actionable insights and less on managing the complexities of data integration. In this post, we discuss the best practices for migrating your ETL pipeline from AWS DMS to zero-ETL integrations for Amazon Redshift.