Amazon Athena Reviews

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.

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.

Excellent for quick data analysis on large datasets stored in a data lake.

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.

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.

It helps us validate ETL job data, catch problems early, and make sure our ETL jobs are working the right way. This saves time and improves data quality.

Athena is a great fit when I need to run analysis on data stored in S3.

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.

Athena helps me run analysis on data stored in S3, and it can also connect with other AWS data stores.

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

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.

Amazon Athena is solving the problem of quickly analyzing large amounts of data stored in S3 without setting up servers or managing infrastructure. Earlier, we needed dedicated databases or clusters for analytics, which required time and cost to maintain. With Athena, we can directly run SQL queries on data and get insights within minutes.

This is benefiting us by reducing infrastructure cost, saving operational effort, and enabling faster decision-making. It is especially helpful for ad-hoc analysis, log analysis, and reporting purposes. Overall, it improves productivity and makes data analytics more accessible for the team.

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.

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.

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.

I don't find it helpful when I need to query the data and make updates.

I use Athena by querying data in s3, our Data LakeHouse. Athena has a very nice, easy user interface, it is cheap and very helpful for querying many types of data.

It's handling of data very efficiently and its a cost effective

It will take some time to fetch the data

I want to store a large dataset and Athena helps me a lot. We can store as much as data we want in a very efficient manner.

The best part is that once the query gives output, you can download it in a csv which helps data analysis easier.

At times, it got confusing with whether to use double quotes or single quotes for strings.

I upload my data to Amazon S3 and then run my scripts to move data post-transformation.

Pricing and ease to use, you can do queries of data with SQL syntax

Not all SQL syntax allowed, you can't create tables from a query.

Query information from an

Amazon athena is 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 very powerful documentation provided by AWS.

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.

It is helping me by removing manual scaling of infrastructure when more data starts flowing in, it saves a lot of time. And also its helping by handling queries to extract large datasets. It has made my life very easy.