#1 most frequent
United States of America
49.2%3,981 installations of 8,096
What is Amazon Athena?
Amazon Athena is an interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL. With a few clicks in the AWS Management Console, customers can point Athena at their data stored in S3 and begin using standard SQL to run ad-hoc queries and get results in seconds. Athena is serverless, so there is no infrastructure to setup or manage, and customers pay only for the queries they run. You can use Athena to process logs, perform ad-hoc analysis, and run interactive queries. Athena scales automatically – executing queries in parallel – so results are fast, even with large datasets and complex queries.
Categories & Use Cases
Top Performing Features
Database security provisions
Provision for database encryption, network isolation, and identity access management
Category average: 8.9
Automatic host deployment
Compute instance replacement in the event of hardware failure
Category average: 6.8
Database scalability
Ease of scaling compute or memory resources and storage up or down
Category average: 9.2
Areas for Improvement
Automatic software patching
Patches applied to database automatically
Category average: 8.3
Monitoring and metrics
Built-in monitoring of multiple operational metrics
Category average: 6.9
Automated backups
Automated backup enabling point-in-time data recovery
Category average: 8
Who Buys & Uses Amazon Athena
#2 most frequent
India
7.6%615 installations of 8,096
#3 most frequent
Brazil
6.6%538 installations of 8,096
AWS Athena: From S3 to Insight.
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.
Return on Investment
- It's easy to store and query data on S3. Multiple teams can query the same data to generate their reports. It removes the need for a full-fledged data warehouse for a startup. Saves costs.
- Improved team efficiency on monitoring user activities by easy logging and reporting.
- As the dataset gets heavier on S3, one needs to understand partitioning and that leads to the requirement of expertise.
Usability
Alternatives Considered
Google BigQuery
Other Software Used
Amazon Kinesis, NGINX, Razorpay Payment Gateway, Amazon Aurora, Apache ActiveMQ, Apache Airflow, Elasticsearch, Google Kubernetes Engine, Firebase
Great value for the money
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.
Return on Investment
- The query speeds help us make more decisions in a day (speed).
- If you need more horsepower for specific times in the day this option helps scale.
- The security of your environment is well protected too.
Alternatives Considered
Azure Synapse Analytics (Azure SQL Data Warehouse)
Other Software Used
Azure SQL Database, Azure Synapse Analytics (Azure SQL Data Warehouse), Microsoft Power BI
Amazon Athena - Faster and interactive query processing engine on Amazon S3
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
Return on Investment
- Easy to query terabytes of data with faster response
- Pricing model is also cheap
- No indexing and partitioning
Alternatives Considered
Amazon Redshift and Amazon EMR (Elastic MapReduce)
Other Software Used
Amazon EMR (Elastic MapReduce), Amazon QuickSight, Amazon Aurora
Didn't knew I could do a lot more with this
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
Return on Investment
- We have saved about 30% team bandwidth using Athena
- We have saved about 100$ a month using athena for our analytics
Alternatives Considered
Traefik Mesh, DigitalOcean Kubernetes and Amazon DynamoDB
Other Software Used
New Relic, Datadog, Stoplight