Amazon Athena Reviews & Insights

We generate lots of user action data from the platform, which is saved in S3 via AWS Firehose Kinesis. These logs are queried occasionally for debugging ETL or business-specific reporting purposes. We use Athena to run SQL-like queries and generate structured reports.

Suitable for: - Log Level Analysis - Reporting Queries - Web Analytics Not Suitable for: - Realtime Streaming Data as it has low latency - Low latency queries as it does take some time to generate the response.

In my current organization, we use Amazon Athena for querying data from AWS S3 location. It provides faster access to data as compared to the traditional relational database management system. Also, it helps to work with complex data structures such as JSON, Parquet, CSV, and Avro. Earlier we were using some traditional RDBMS for reporting Ecommerce related KPIs which has lots of transactional data coming in. Performance was not much good for querying huge amount of real-time inventory data. So, we moved to Amazon Athena to support fast interactive querying of data and processing.

Best suited for analyzing huge amounts of data by just querying on Amazon Athena. Amazon Athena is also best to integrate with Amazon Quickight for visualization and reporting of data. Easy to work with CSV, JSON, and columnar data formats like Parquet, and ORC. Less appropriate to work with AVRO data format and also stored procedures are not supported in Amazon Athena. The size of a single row is also limited to 32 MB.

We extensively use AWS Load balancers and a lot of traffic needs retrospection. Athena makes it quite simple and useful to query our traffic and analyze the service architecture. We also use AWS S3 extensively. Athena makes it quite simple to query around half a million records daily. We have tried other open source tools, none of which has been able to work in as fewer efforts as Athena did. I would definitely recommend it to others.

We use Athena extensively on our load balance traffic analyser. It helps us filter out bad requests and reason them quickly. Hence, a useful toolkit for monitoring stack We also use Athena on our report generation on a million per day datasets. It helps report generation quickly and easily on daily basis, which would've taken a lot of engineering efforts.

We use Amazon Athena to overlay a bunch of direct-to-consumer click stream data. The most common queries are looking at attribution analysis. Things like first touch attribution versus last touch attribution. The data volume is significant and we needed an easy way to pull insights from our data stores and hand them back to the marketing business side users. At the end of the day, SQL is a very popular language to use for 99% of data problems.

If you are looking to take a lot of the traditional "database administration" work off someone's plate, going with Amazon Athena certainly has "no code" options to optimize lots of database tasks. I would say this option is less appropriate if you have other Microsoft things at play, such as Power BI.