Features
Amazon Athena is a query service that allows you to interact with your data in order to perform basic SQL analysis on data stored in Amazon S3. Athena allows users to direct it at their S3 data with a few clicks in the AWS Management Console, at which point they can begin using conventional SQL to conduct interactive queries and obtain responses in seconds. Due to Athena’s serverless nature, users only need to pay for the queries they execute and not for any associated infrastructure. Athena can be used for a variety of log processing, data analysis, and interactive querying tasks. With Athena, scaling is handled automatically, with queries executed in parallel, allowing for quick returns even with big datasets and sophisticated queries.
Serverless with an inexistent support system and no management
Managing infrastructure is unnecessary because Amazon Athena does not have any servers. As your datasets and user base expand, you can focus on developing new applications rather than worrying about your infrastructure. Athena handles this mechanically so you may concentrate on the data rather than the underlying infrastructure.
Easy-to-use
To get started, log into the Athena console, define your schema with the console wizard or DDL statements, and start querying with the built-in editor. AWS Glue can automatically explore data sources to find data and update your Data Catalog with new table and partition definitions. In seconds, results are presented in the console and written to S3. You can also save them. With Athena, you don’t require sophisticated ETL jobs to analyze data. Anyone with SQL expertise can evaluate massive datasets fast.
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Use regular SQL to query
Amazon Athena leverages Presto, a low-latency, interactive SQL query engine. You can perform ANSI SQL queries against big Amazon S3 datasets with support for massive joins, window functions, and arrays. Athena supports CSV, JSON, ORC, Avro, and Parquet. With Athena’s federated data source connectors, you may query and join data from Amazon S3. Athena’s JDBC and ODBC drivers let you perform queries via the console, API, CLI, and AWS SDK, and support BI and SQL development apps.
Pay per query
With Amazon Athena, you pay just for queries. You’re charged based on the amount of data scanned by each query. You can save money and improve speed by compressing, splitting, or transforming your data to a columnar format. Each of these processes minimizes the amount of data Athena has to scan to run a query.
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Quick speed
The quick performance you need doesn’t necessitate any expertise in cluster management or tweaking thanks to Amazon Athena. Athena’s integration with Amazon S3 is intended for speed. Athena automatically parallelized query execution, allowing for rapid response times even on massive datasets.
High availability and long life span
Because of its high availability, Amazon Athena performs queries using compute resources from many data centers, automatically rerouting them if necessary. Your data is very accessible and durable because Athena uses Amazon S3 as its underlying data store. Amazon Simple Storage Service (S3) is a reliable data storage service with an object durability design of 99.999999999%. Your information is saved in various locations, and at each location, it is stored on multiple devices.
Secure
Through the use of AWS Identity and Access Management (IAM) policies, access control lists (ACLs), and Amazon S3 bucket policies, you may restrict who has access to your data in Amazon Athena. If you utilize IAM policies, you can give specific permissions to users for your S3 buckets. Data in S3 can be protected from Athena queries by limiting access to specific users. Athena makes it simple to query encrypted Amazon S3 data and save the decrypted results back to the same bucket. It is possible to encrypt data either on the server or on the client’s end.
Integrated
Amazon Athena is preconfigured to work with AWS Glue. Glue Data Catalog allows you to centralize metadata across several services, discover new data through crawling, populate your Data Catalog with updated table and partition definitions, and manage schema versions. Additionally, Glue’s fully-managed ETL features can be used to transform data or convert it into columnar forms for better query performance and lower costs.
Distributed query
Connectors for common third-party data stores including MySQL, PostgreSQL, and Redis are supported by Athena, along with Amazon DynamoDB, Amazon Redshift, Amazon OpenSearch, and many more. Athena’s data connectors make it possible to draw conclusions from disparate data sets without having to manually move or transform the data using ETL scripts. You can extend SQL queries to hundreds of users when you run them as AWS Lambda functions and offer cross-account access using data connectors.
Learning by machine (ML)
With an Athena SQL query, you may conduct inference using a model you created in SageMaker Machine Learning. The use of ML models into SQL queries simplifies previously difficult operations like anomaly detection, customer cohort analysis, and sales forecasting. All one needs is some familiarity with SQL queries in order to use Athena to execute machine learning models hosted on Amazon SageMaker.
General Use Cases
Amazon Athena provides a solution for executing ad-hoc queries on data sets stored in Amazon S3 using Presto and ANSI SQL. It is especially useful for situations where you need to work with structured, semi-structured, and unstructured data formats. With Athena, you can perform analysis on your data in real-time, making it ideal for scenarios such as real-time data analysis, clickstream events, and log analysis.
On the other hand, Microsoft SQL Server is a robust relational database management system that caters to a wide range of purposes. It offers extensive support for various applications like Business Intelligence (BI), Analytics, and transaction processing. SQL Server enables the management, organization, and retrieval of structured data effectively. It facilitates the implementation of complex queries, data manipulations, and data storage, making it an optimal choice for scenarios that require handling large structured data sets and performing advanced analytics.
In summary, Amazon Athena serves as a powerful tool for querying and analyzing data stored in Amazon S3, encompassing structured, semi-structured, and unstructured formats, while Microsoft SQL Server provides a reliable solution for managing structured data and supporting a variety of applications, including BI, Analytics, and transaction processing.
Advantages
Instant querying
Athena is an ETL that doesn’t require a server. You can run queries on your data fast without needing to manage servers or data storage facilities. Simply connect to your Amazon S3 data, define the schema, and begin querying with the integrated query editor. You can access all of your S3 data with Amazon Athena, and there’s no need to put up any elaborate procedures to extract, transform, and load the data (ETL).
Get charged per search
Data scanning is an add-on service.
Costs for using Amazon Athena are decoupled from the number of queries you send it. There is a $5 fee for each terabyte that your queries scan. By compressing, splitting, and turning your data into columnar formats, you can save 30%-90% on per-query expenses and improve performance. Athena conducts queries on Amazon S3 files directly. The S3 storage plan is free of any additional fees.
Fully accessible, very effective, and universally accepted
Powered by Presto and regular SQL.
Amazon Athena is built on Presto and supports ANSI SQL, allowing it to read and write to common file formats like CSV, JSON, ORC, Avro, and Parquet. Athena excels in complicated analysis, including massive joins, window functions, and arrays, and it also facilitates interactive querying. Amazon Athena has excellent availability because it distributes query processing over numerous data centers and multiple devices in each data center. Since Amazon S3 is the underlying data store for Amazon Athena, you can rest assured that your data will be both accessible and stable.
Incredibly fast
Despite the size of the data set, the performance remains interactive.
You can rest assured that Amazon Athena will provide you with the computational resources you need for quick, interactive query performance. Because Amazon Athena processes queries in parallel automatically, you may expect to receive the most results in a matter of seconds.
Pricing
When comparing the pricing of Amazon Athena and Microsoft SQL Server, there are notable differences. Amazon Athena has a starting price of $5 per TB of data scanned. On the other hand, Microsoft SQL Server offers a free option called Express edition. However, for the Enterprise SQL Server 2022, the cost can go up to $15,123. Therefore, while Amazon Athena has a fixed pricing model based on data scanned, Microsoft SQL Server has various editions with different associated costs, ranging from free to a significantly higher price.
Jake is a writer and marketing associate for AllCode with a wealth of experience in a variety of industries.
