An interactive data analysis tool, Amazon Athena, is utilized to process complex queries quickly. It has no servers. As a result, setup is simple, and infrastructure administration is unnecessary. The service is not a database.
Amazon debuted Athena as one of its services on November 20, 2016. Amazon Athena is a serverless query tool that examines data stored in Amazon S3 using conventional SQL. Customers can direct Amazon Athena to their Amazon S3 data and run queries using traditional SQL to receive results in seconds with just a few clicks in the AWS Management Console.
With Amazon Athena, the customer pays for the queries they execute, and there is no infrastructure to set up or administer. Even with large datasets and complex queries, Amazon Athena automatically scales by running queries in parallel and providing quick results. This article covers detailed knowledge of AWS Athena.
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Features of Athena
Amazon offers a wide range of services, and Athena is one of them. It is suitable for data analysis due to several properties. Let’s examine each of the aspects individually.
Athena does not need to be installed, and the AWS CLI can access it right from the AWS console.
The end-user need not be concerned about infrastructure, configuration, scaling, or failure because it is serverless.
Pay Per Query
You are only charged for the queries you run with Athena or the amount of data each query manages. One can save a lot of money if you compress them and adequately format the dataset.
A tool for quick analysis is Athena. It can swiftly process complex queries by splitting them into simpler ones, running them concurrently, and merging the results to get the necessary output.
Athena gives you total control over the data set using IAM policies and AWS identities. S3 buckets store data, so IAM policies can help you manage user permissions.
Athena is highly available and users can run queries whenever they want. Athena shares AWS’s 99.999% availability.
The ability to integrate with AWS Glue is Athena’s finest feature. The user can create a more seamlessly integrated data repository with the aid of AWS Glue. It aids in the production of improved data versions, tables, views, etc.
Cons of AWS Athena
No Data Optimization
There are only a few customizing options available with AWS Athena. You can only get as far as this by optimizing the queries rather than the underlying data.
All AWS Athena customers, no matter where they are, utilize the same resource while executing queries, as stated by Amazon’s Service Level Agreement (SLA). This multi-tenancy strategy occasionally causes resource stress, which affects query performance.
Reduction in Data Manipulation Operations
There is only one query engine available here because AWS Athena is merely a query service. It lacks a Data Manipulation Language (DML) interface that allows for data insertion, deletion, and updating.
Requires Data Partitioning
Consider dividing the data sets kept in Amazon S3 if you want to conduct the SQL queries effectively. The number of divisions you need to establish will significantly impact how quickly and effectively your queries run. For example, your query time will increase by one second for every 500 partitions scanned.
Lack of Indices
While indexing has always been a standard feature in conventional databases, AWS Athena does not grant you this privilege.
Amazon Athena Introduces New Engine
AWS recently released version 3 of the engine for Amazon Athens, the serverless interactive service that allows users to query S3 data using standard SQL. The cloud provider claims that by adding over 50 new SQL functions and 30 new analytics features, the new engine improves performance and supports new use cases.
The open-source Trino and PrestoDB projects contributed most of the improvements for Athena engine version 3, with AWS expediting the integration of community enhancements and bug fixes.
Athena now supports T-Digest functions for rank-based statistics and new geographic functions, among other new capabilities, and the inclusion of MATCH_RECOGNIZE for row pattern matching aids in the identification of data patterns in applications like fraud detection and sensor data analysis.
AWS claims that the new engine speeds up query execution, reduces the quantity of data scanned, and increases the performance of joins, including comparisons with the <,<=, >,>= operators, queries that contain JOIN, UNION, UNNEST, GROUP BY clauses, and queries employing IN predicate.
Applications of Amazon Athena
You may analyze unstructured, semi-structured, and structured data kept in Amazon S3 with the aid of Athena. Some examples are CSV, JSON, or columnar data formats like Apache Parquet and Apache ORC.
Cross-account access to S3 buckets owned by other users is also made possible by Athena. Additionally, Athena stores data and schemas relevant to searches on Amazon S3 data in controlled data catalogs.
What Integration Options does Amazon Athena have?
Athena connects with a wide range of other AWS services. For example, AWS Glue integrated with Athena makes it possible to use more advanced data catalog capabilities like a metadata repository, automated schema and division detection, and Python-based data pipelines. Table metadata for S3 data is stored and retrieved by Glue Data Catalog.
You only pay with Amazon Athena for the queries you submit. Based on how much information each query scans, you get charged. You can significantly reduce costs and improve speed by compressing, splitting, or converting your data to a columnar format. Each action reduces the amount of data that Athena must scan to answer a query.
Amazon Athena immediately accesses data from Amazon S3. There are no extra storage fees when using Athena to query your data. For storage, requests, and data transmission, you are billed at standard S3 rates. By default, query results are charged at regular Amazon S3 rates and stored in the S3 bucket of your choice.
Standard AWS Glue Data Catalog fees apply when using Athena with the AWS Glue Data Catalog.
The AWS services you use with Athena, such as Amazon S3, AWS Lambda, AWS Glue, and Amazon SageMaker, are also subject to regular fees. For instance, storage, requests, and cross-regional data transfer are billed at S3 prices. By default, query results are charged at regular Amazon S3 rates and stored in the S3 bucket of your choice. If you use AWS Lambda, you will be paid according to the volume of requests for your functions and the execution time of your code.
Amazon Athena is a versatile and powerful tool for querying data in various ways. It is simple to set up and use and has many potential applications. Its low-cost pricing model makes it an appealing option for those on a tight budget who want to query data.
Athena is an excellent choice for those who need to query data in S3 and is especially well-suited for SQL users. However, it is essential to remember that Athena is not replacing a traditional data warehouse. It works best for specific tasks like ad hoc analysis or data exploration.