AWS Database Blog
Category: Amazon Athena
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 […]
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 […]
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. […]
Understanding Amazon DynamoDB encryption by using AWS Key Management Service and analysis of API calls with Amazon Athena
As applications evolve to be more scalable for the web, customers are adopting flexible data structures and database engines for their use cases. Using NoSQL data stores has become increasing popular because of NoSQL’s flexible data model for building modern applications. Amazon DynamoDB is a fast and flexible NoSQL database service that can provide consistent […]
How to perform advanced analytics and build visualizations of your Amazon DynamoDB data by using Amazon Athena
You can reap huge analytical value from billions of items and millions of requests per second in your Amazon DynamoDB service. However, you need to export your data in order to get that analytical value. Copying the data from a DynamoDB table to an analytics platform allows you to extract rich insights. In order to […]
Audit Amazon Aurora Database Logs for Connections, Query Patterns, and More, using Amazon Athena and Amazon QuickSight
Amazon Aurora offers a high-performance advanced auditing feature that logs detailed database activity to the database audit logs in Amazon CloudWatch. If you are using Aurora 1.10.1 or greater, you can use advanced auditing to meet regulatory or compliance requirements by capturing eligible events like tables queried, queries issued, and connections and disconnections. You can […]
Using AWS Database Migration Service and Amazon Athena to Replicate and Run Ad Hoc Queries on a SQL Server Database
Prahlad Rao is a solutions architect at Amazon Web Services. When you replicate a relational database to the cloud, one of the common use cases is to enable additional insights on the replicated data. You can apply the analytics and query-processing capabilities that are available in the AWS Cloud on the replicated data. To replicate […]