In this entry, we introduce an open-source solution for executing cross-chain analytics on public blockchain data. We also provide access to public datasets for Bitcoin and Ethereum, which can be accessed through AWS Open Data. It’s important to note that these datasets remain experimental and are not suitable for production workloads. For further information, you can refer to this blog post here.
Customers have expressed a desire for enhanced interaction with graph datasets in Amazon Neptune using business intelligence (BI) tools like Amazon QuickSight. While some BI tools provide generic HTTP connectors that allow a series of REST API calls to extract data from REST endpoints, users must predefine whether to use Gremlin or other methods, which can be limiting.
With the introduction of Amazon CloudWatch Metric Streams, you can now stream near-real-time metrics data to destinations such as Amazon S3. This functionality supports various use cases, effectively enhancing database performance analysis. If you’re interested in more details about this topic, check out these experts.
Managed Blockchain operates on an event-driven architecture, allowing for diverse analytic methodologies by streaming events to Amazon Kinesis. This enables near-real-time event analysis with Kinesis Data Analytics or large-scale data warehousing with Amazon RedShift. In this post, we showcase a method using Amazon Kinesis Data Firehose to capture, monitor, and aggregate events into a dataset, which can be analyzed with Amazon Athena using standard SQL.
Additionally, Amazon RDS enables you to easily create, manage, and scale relational databases in the cloud. Since January 2020, AWS has allowed the export of snapshots from various Amazon RDS engines into Amazon S3 in Apache Parquet format, which can be beneficial for data lake implementations and retention policies.
As applications continue to evolve for scalability, many customers are opting for flexible data structures and database engines. Amazon DynamoDB has become a popular choice due to its fast and flexible NoSQL database service that offers consistent performance and scalability. To fully leverage the analytical potential of your data, exporting it from a DynamoDB table to an analytics platform is essential.
You can also audit Amazon Aurora database logs for connections, query patterns, and more using Amazon Athena and Amazon QuickSight. The advanced auditing feature in Amazon Aurora allows you to log detailed database activity to Amazon CloudWatch. This capability is particularly useful for meeting regulatory requirements by capturing significant events such as queried tables and issued queries.
Lastly, utilizing the AWS Database Migration Service in conjunction with Amazon Athena can facilitate the replication and execution of ad hoc queries on SQL Server databases. By replicating a relational database to the cloud, you can gain valuable insights from the replicated data, enhancing your analytical and query-processing capabilities. For a comprehensive understanding, consider watching this excellent resource.
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