I use Amazon Athena in my day-to-day work for our project. What I like most about Athena is that it can query large datasets directly from Amazon S3 without having to manage any infrastructure. In my workplace, I use Athena regularly to validate processed data after Spark ETL jobs complete, and I compare source and target datasets with the help of Athena. After the ETL pipelines finish writing Parquet or CSV files into S3, I use Athena tables connected through the AWS Glue Data Catalog, then run SQL queries to verify record counts, null values, duplicates, partitioned data, and transformation outputs. Review collected by and hosted on G2.com.

I don’t have any major dislikes. It’s a good final product, and it helps me a lot. Review collected by and hosted on G2.com.

What I like best about Amazon Athena is that it is very easy to use and fully serverless. We don’t need to manage any infrastructure, and we can directly query data stored in S3 using simple SQL. It saves time for data analysis and is very cost effective because we only pay for the data we scan. It also integrates well with other AWS services, which makes it very useful for analytics and reporting use cases Review collected by and hosted on G2.com.

One thing I dislike about Amazon Athena is that the cost can increase quickly if queries are not optimized properly, since pricing is based on data scanned. For large and unpartitioned datasets, it becomes expensive. Also, performance sometimes depends heavily on how well the data is structured in S3. It is not ideal for very low-latency or real-time analytics use cases. Query debugging can also be slightly difficult for beginners. Review collected by and hosted on G2.com.

What I value most is that it is serverless: it allows you to run standard SQL queries directly on data in Amazon S3, without having to provision infrastructure or move the information through ETL processes.

In practice, it is super useful for performing quick and ad-hoc exploratory analysis on Parquet or JSON files within our Data Lake. Additionally, by integrating natively with the AWS Glue Data Catalog, metadata management becomes almost transparent and much simpler. Review collected by and hosted on G2.com.

What I like the least is that the charge per TB scanned can become quite unpredictable if the queries are not well optimized (for example, when there are no partitions or compression is not used). A simple "SELECT" on a large dataset can turn out to be surprisingly expensive.

Additionally, performance drops significantly with the typical problem of many small files in S3 (small file problem), and that ends up forcing us to add extra compaction processes with AWS Glue to maintain efficiency.

And, finally, it is not the best option for low-latency reports or with high concurrency: in those scenarios, Redshift still performs better. Review collected by and hosted on G2.com.

Amazon Athena helps us explore and test data easily without having to set up a lot of things. It’s very simple to use, works well with AWS, and delivers faster results. It also saves money, is easy to get started with, and includes smart features that help us use our data better. Review collected by and hosted on G2.com.

The screen is simple, but it can be confusing for beginners. Query costs can also get high, and performance really depends on how the data is organized. Review collected by and hosted on G2.com.

It's easy to use, very fast performance, uses ANSI SQL, relatively cheap compared to other options and it's easy to integrate with programming languages using libraries. Review collected by and hosted on G2.com.

It’s harder to monitor performance issues. Since it’s basically file-based on S3, concurrency can also be a problem when using regular tables, especially compared to Iceberg. Review collected by and hosted on G2.com.

It’s been great working with S3 data. I really loved the data extraction feature that uses an S3 bucket—it made pulling the data I needed straightforward. Review collected by and hosted on G2.com.

I’m not able to use UPDATE statements in Athena, which is a drawback for me. Review collected by and hosted on G2.com.

I like that Amazon Athena offers serverless querying directly on data without having to manage any infrastructure. It's very fast to set up, which is ideal for our analytics needs. Review collected by and hosted on G2.com.

Query cost can increase with large datasets and file formats if they are not optimized properly. Performance tuning also requires careful data organization. Review collected by and hosted on G2.com.

It is easy to use Athena, whether you are a tech or a non-tech person. It has a very user-friendly interface, and the thing that I like the most is that you can query many types of source input data. Review collected by and hosted on G2.com.

I don't find it helpful when I need to query the data and make updates. Review collected by and hosted on G2.com.

Athena is a great fit when I need to run analysis on data stored in S3. Review collected by and hosted on G2.com.

It is cloud-native with respect to AWS. We need a solution that sits on top of the AWS cloud and also supports a multi-cloud setup. Review collected by and hosted on G2.com.

Amazon Athena is a very powerful serverless solution and it's very cost-effective. I like the user-friendly interface that Athena provides to query data from any table present in the S3. I use Athena to query data almost on a regular basis. The customer support is also very helpful with the queries regarding anything. It was very easy to implement because of the very powerful documentation provided by AWS. Review collected by and hosted on G2.com.

There are very few things that I don't like about Athena, like occasional lags when querying large datasets, there are also some features missing that could have helped the user a lot. Review collected by and hosted on G2.com.