What is Amazon Athena?
AWS Athena is an interactive query service that makes SQL-based data analysis in Amazon Simple Storage Service simpler. Athena can locate your Amazon S3 data and use conventional SQL to conduct ad-hoc searches with results in seconds with a few AWS Management Console clicks.
Why Athena?
Using normal SQL to analyze data directly in Amazon S3 is made easy with AWS Athena, an interactive query service. Because Athena is serverless, you can choose to pay according to the number of queries you run or the computation required by your queries, and there is no infrastructure to set up or maintain. Run interactive queries, handle logs, and do data analytics using Athena. Even with complicated queries and big datasets, Athena scales automatically to run queries in parallel and deliver quick responses.
Benefits of AWS Athena
Since AWS Athena is serverless, no infrastructure needs to be maintained. As your datasets and user base expand, you won’t have to worry about configuration, software updates, malfunctions, or extending your infrastructure. You can concentrate on the data rather than the infrastructure because Athena handles all of this automatically.
Simple to begin
To begin, open the Athena console, add DDL statements or use the console wizard to define your schema, and then instantly begin querying using the integrated query editor. Additionally, you can use AWS Glue to automatically search data sources to find data and add new and updated table and partition definitions to your Data Catalogue.
Results are instantly written to a location of your choosing in S3 and seen in the console in a matter of seconds. They can be downloaded to your desktop as well. Complex ETL tasks are not necessary when using Athena to get your data ready for analysis. This facilitates the rapid analysis of big datasets by anyone with SQL capabilities.
Simply use regular SQL to query
The open source, distributed SQL engines Trino and Presto, which are tuned for low latency, interactive data processing, are the foundation of AWS Athena. This implies that you may use ANSI SQL to conduct queries against big datasets in Amazon S3, fully supporting arrays, window functions, and huge joins. Numerous data formats, including CSV, JSON, ORC, Avro, and Parquet, are supported by Athena.
You can query other data stores and combine the data with data from Amazon S3 using Athena’s federated data source connectors. Through Athena’s JDBC and ODBC drivers, you may access Athena and execute queries from the Athena interface, API, CLI, AWS SDK, and supported business intelligence and SQL development applications.
Adjustable prices
There are two adjustable pricing options available on AWS Athena. Default billing is dependent on the number of terabytes (TB) of data scanned for each query. This enables you to send queries for computation without prior planning. Use the capacity-based pricing offered by Provisioned Capacity if you would rather pay according to the amount of computation your queries require, or if you wish to manage concurrency and priorities workloads. You can simultaneously utilize capacity-based pricing and per query invoicing in the same account for more flexibility.
Quick performance
Managing or fine-tuning clusters to achieve quick performance is not a concern with Amazon Athena. With Amazon S3, Athena is tuned for quick performance. Even on big datasets, Athena automatically runs queries in parallel, giving you query results in a matter of seconds.
Extremely durable and accessible
Being highly available, AWS Athena uses computational resources to perform queries across several facilities, automatically rerouting requests to the appropriate location in the event that a certain facility is inaccessible. Because Athena’s underlying data store is Amazon S3, your data is extremely durable and accessible. Amazon S3 offers robust infrastructure for storing critical data and is built to last for 99.999999999% of objects. Multiple locations and devices within each location store your data redundantly.
Safe
Using Amazon S3 bucket policies, access control lists (ACLs), and AWS Identity and Access Management (IAM) policies, Amazon Athena lets you manage who can access your data. You may provide IAM users fine-grained control over your S3 buckets by implementing IAM policies. You can prevent users from using Athena to query data in S3 by managing access to it. Additionally, Athena enables you to write encrypted results back to your S3 bucket and query encrypted data stored in Amazon S3. There is support for both client-side and server-side encryption.
Combined
AWS Athena interfaces with AWS Glue in an unconventional way. You may maintain schema versioning, crawl data sources to find data, and add new and updated table and partition definitions to your Data Catalogue using Glue Data Catalogue. It also allows you to construct a single metadata repository across several services. Additionally, you may optimize query performance and cut costs by transforming data or converting it into columnar formats using Glue’s fully-managed ETL capabilities. Study up on AWS Glue.
Federated inquiry
30 well-known AWS, on-premises, and other cloud data stores, including as Redis, Snowflake, SAP Hana, Google BigQuery, Google Cloud Storage, Azure Synapse, Azure Data Lake Storage, Amazon Redshift, and Amazon DynamoDB, have built-in interfaces to Athena. You can use the Athena SQL syntax to generate insights from numerous data sources without having to relocate or alter your data by utilising Athena data source connectors.
Data connectors can be set up for cross-account access to scale SQL searches to hundreds of end users and operate as AWS Lambda functions. See Available data source connectors for a list of sources that are supported. See the Athena connection SDK for information on creating a custom data source connector.
Learning by machine
To perform inference, you can use an Athena SQL query to invoke your SageMaker Machine Learning models. Complex activities like anomaly detection, customer cohort analysis, and sales projections are made as easy as creating a SQL query with to the ability to apply ML models in SQL queries. Anyone with SQL skills may run ML models deployed on Amazon SageMaker with ease to Athena.
Use cases
Execute queries on different clouds, S3, or on-site
To analyse data from relational, nonrelational, object, and custom data sources operating on S3, on-site, or in multicloud settings, submit a single SQL query.
Get data ready for machine learning models
To make complicated operations like anomaly detection, customer cohort analysis, and sales forecasting easier, use machine learning models in Python or SQL queries.
Execute analytics across several clouds
Use Amazon QuickSight to query Azure Synapse Analytics data and see the results.
