Amazon Athena Reviews and Ratings

Rating: 8.1 out of 10

Score

8.1 out of 10

Reviews

4 Reviews

AWS Athena: From S3 to Insight.

Rating: 10 out of 10

Incentivized

January 8, 2025

Use Cases and Deployment Scope

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.

Pros

Log Analysis.
Real Time Reports.
Data Integration with other components. Makes ML Data ingestion super easy.

Cons

Response caching can be improved.
Data Partitioning is tricky and understanding of the same could be improved.

Likelihood to Recommend

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.

Alok Pabalkar

Co-Founder & CTO in Research & Development at GIDE.AI (51-200 employees)

Vetted Review

10 years of experience

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Amazon Athena - Faster and interactive query processing engine on Amazon S3

Rating: 10 out of 10

Incentivized

September 5, 2022

Use Cases and Deployment Scope

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.

Pros

Nested Schemas like JSON data structure
Ability to adapt the data model to fit your queries better
Performance Improvement

Cons

Complex query optimization
Limited performance on AWS S3
Partioning and columnar format to maximize MPP

Likelihood to Recommend

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.

Riyaz Khan

Data Engineer in Information Technology at Reliance Jio Platforms Ltd. (10,001+ employees)

Vetted Review

5 years of experience

Didn't knew I could do a lot more with this

Rating: 10 out of 10

Incentivized

August 30, 2022

Use Cases and Deployment Scope

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.

Pros

Load Balance traffic analysis
Big data report generation
Micro services pattern query analysis

Cons

Query manager can incoperate GUI based query designer
Auto-completion engine sometimes overwrite the query
Time range selection should be implicit

Likelihood to Recommend

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.

Verified User

Team Lead in Engineering (201-500 employees)

Vetted Review

2 years of experience

Great value for the money

Rating: 8 out of 10

Incentivized

July 25, 2022

Use Cases and Deployment Scope

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.

Pros

The most obvious, is you can use SQL programming language, which a lot of people understand.
You can scale up to meeting higher processing times.
The data return speed (query speed) is great.

Cons

Every dialect of SQL has some missing functions. I wish there was automated GROUP BY options here.
There are connection problems back to Power BI occasionally.
If you don't watch certain queries, it's possible that it takes a long time to run and charges you a lot of money.

Likelihood to Recommend

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.

Scott Kennedy

Sr Director, BI in Marketing at Franklin Sports (201-500 employees)

Vetted Review

3 years of experience

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