AWS Database Blog

Category: Amazon Athena

Create a Virtual Knowledge Graph with Amazon Neptune and an Amazon S3 data lake

It’s common in an enterprise for data that logically fits together to be separated into different databases. Some data is better suited for one storage than another, and it may not be feasible to locate all your data in one data store. But this data often needs to be linked back together to provide a […]

Joining historical data between Amazon Athena and Amazon RDS for PostgreSQL

While databases are used to store and retrieve data, there are situations where applications should archive or purge the data to reduce storage costs or improve performance. However, there are often business requirements where an application must query both active data and archived data simultaneously. Developers need a solution that lets them benefit from using […]

Writing results from an Athena query to Amazon DynamoDB

Many industries are taking advantage of the Internet of Things (IoT) to track information from and about connected devices. One example is the energy industry, which is using smart electricity meters to collect energy consumption from customers for analytics and control purposes. Vector, a New Zealand energy company, combines its energy knowledge with Amazon Web […]

Export and analyze Amazon QLDB journal data using AWS Glue and Amazon Athena

Amazon Quantum Ledger Database (Amazon QLDB) is a fully managed ledger database that maintains a complete, immutable record of every change committed to the database. As transactions are committed to the database, they are appended to a transaction log called a journal and are cryptographically hash-chained to the previous transaction. Once committed, the record of […]

Access Bitcoin and Ethereum open datasets for cross-chain analytics

In this post, we share an open-source solution for running cross-chain analytics on public blockchain data along with public datasets for Bitcoin and Ethereum available through AWS Open Data. These datasets are still experimental and are not recommended for production workloads. You can find the open-source project on GitHub here and the public blockchain datasets […]

Architecture Diagram

Build interactive graph data analytics and visualizations using Amazon Neptune, Amazon Athena Federated Query, and Amazon QuickSight

Customers have asked for a way to interact with graph datasets in Amazon Neptune using business intelligence (BI) tools such as Amazon QuickSight. Although some BI tools offer generic HTTP connectors that allow you to define a set of REST API calls to extract data from REST endpoints, you have to predefine either Gremlin or […]

Analyze database performance with Amazon CloudWatch metric streams

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. With the announcement of Amazon CloudWatch Metric Streams, you can now stream near-real-time metrics data to a destination such as Amazon Simple Storage Service (Amazon S3). Metric Streams supports two primary use […]

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

January 2023: Please refer to Accelerate Amazon DynamoDB data access in AWS Glue jobs using the new AWS Glue DynamoDB Export connector  for more recent updates on using Amazon Glue to extract data from Amazon DynamoDB. Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It’s a fully […]

Performing analytics on Amazon Managed Blockchain

Managed Blockchain follows an event-driven architecture. We can open up a wide range of analytic approaches by streaming events to Amazon Kinesis. For instance, we could analyze events in near-real time with Kinesis Data Analytics, perform petabyte scale data warehousing with Amazon RedShift, or use the Hadoop ecosystem with Amazon EMR. This allows us to use the right approach for every blockchain analytics use case.
In this post, we show you one approach that uses Amazon Kinesis Data Firehose to capture, monitor, and aggregate events into a dataset, and analyze it with Amazon Athena using standard SQL.

Building data lakes and implementing data retention policies with Amazon RDS snapshot export to Amazon S3

Amazon Relational Database Service (RDS) helps you easily create, operate, and scale a relational database in the cloud. In January 2020, AWS announced the ability to export snapshots from Amazon RDS for MySQL, Amazon RDS for PostgreSQL, Amazon RDS for MariaDB, Amazon Aurora PostgreSQL, and Amazon Aurora MySQL into Amazon S3 in Apache Parquet format. […]