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AWS Certified Data Analytics Specialty certification:

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Emmanuel Odenyire Anyira
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Exam preparation short notes AWS services and features

Category:- Analytics

AWS Service:- AWS Athena

Introduction:

Amazon Athena is a serverless, interactive query service that allows users to analyze data in Amazon S3 using SQL. It enables users to analyze data stored in different formats such as CSV, JSON, and Parquet. Athena does not require any infrastructure setup, and users only pay for the queries they run.

Athena is built on Presto, an open-source distributed SQL query engine, and is designed to handle ad-hoc queries against large datasets and is used by various industries, including finance, healthcare, retail, and more. With Athena, users can easily analyze data in S3 without having to manage any infrastructure or set up a database.

Features:

  • Serverless:

Athena is a serverless service, which means that users do not have to manage any infrastructure. There are no servers to provision or manage, and users only pay for the queries they run. It automatically scales and manages resources based on the size and complexity of the query being executed.

  • Interactive:

Athena is designed to provide fast query performance, even for large datasets and supports ANSI SQL queries.. Users can run SQL queries against data stored in S3 and get results in seconds.

  • Scalable:

Athena is designed to scale automatically, based on the amount of data being analyzed to meet the query demands.. It can handle datasets ranging from a few kilobytes to petabytes.

  • Support for multiple data formats:

Athena supports various data formats, including CSV, JSON, Parquet, and ORC. This makes it easy to analyze data in different formats without having to convert it first. Users can query data in different formats without having to convert it first.

  • Integration:

Athena can be integrated with various AWS services such as Amazon S3, Amazon Glue, AWS Lambda, and Amazon QuickSight. Users can use Athena to query data in S3 and store the results in different AWS services.

  • Security:

Athena integrates with AWS Identity and Access Management (IAM) and supports encryption of data at rest using Amazon S3 server-side encryption.

How Amazon Athena is used:-

Amazon Athena is used to perform ad-hoc analysis of data stored in Amazon S3 using SQL queries nd get results in seconds.. It is ideal for analyzing log data, performing data exploration, and running data warehousing queries. Athena is also used as a data source for business intelligence (BI) tools to analyze data such as Amazon QuickSight, Tableau, and others. Users can connect BI tools to Athena and use it as a data source to build interactive dashboards and reports. Athena can also be used as a part of a data lake solution. It can be used to query data stored in S3 as a part of a larger data processing pipeline.

Scenarios

Scenarios where Amazon Athena can be used:

  • Analyzing logs: Amazon Athena can be used to analyze logs stored in S3. For example, access logs from an Amazon S3 bucket can be analyzed to get insights into how the bucket is being used such as the number of requests and the size of the data being transferred.

  • Ad-hoc analysis: Amazon Athena can be used for ad-hoc analysis of data. Users can quickly analyze data by running SQL queries against it without having to set up any infrastructure. This is ideal for exploring data, testing hypotheses, and discovering patterns.

  • Business intelligence (BI): Amazon Athena can be used as a data source for BI tools. For example, BI tools such as Tableau and Amazon QuickSight can be connected to Athena to analyze data.Users can use Athena to query their data in S3 and visualize it using different chart types and interactive dashboards.

  • Data lake: Amazon Athena can be used as a part of a data lake solution. It can be used to query data stored in S3 as a part of a larger data processing pipeline.

  • Data warehousing: Amazon Athena can be used as a data warehousing solution for running complex queries against large datasets stored in S3. Users can use Athena to create tables, manage partitions, and optimize queries for performance.

  • ETL: Amazon Athena can be used as a part of an extract, transform, and load (ETL) pipeline. Users can use Athena to extract data from S3, transform it using SQL queries, and load it into different AWS services such as Amazon Redshift or Amazon RDS.

References:

  1. Amazon Athena Documentation: https://aws.amazon.com/athena/

  2. AWS Certified Data Analytics Specialty Exam Guide: https://aws.amazon.com/certification/certified-data-analytics-specialty/

  3. Amazon Athena Documentation: https://aws.amazon.com/athena/

  4. AWS Certified Data Analytics Specialty Exam Guide: https://aws.amazon.com/certification/certified-data-analytics-specialty/

  5. AWS Big Data Blog: https://aws.amazon.com/blogs/big-data/category/analytics/amazon-athena/

  6. AWS re:Invent session: Getting the Most Out of Amazon Athena: https://www.youtube.com/watch?v=kzIidp0sJmk

  7. AWS re:Invent session: Best Practices for Running Amazon Athena: https://www.youtube.com/watch?v=C8uZfX9V7qQ

  8. AWS Solutions Library: https://aws.amazon.com/solutions/implementations/athena-federation/

  9. AWS Athena FAQ: https://aws.amazon.com/athena/faqs/

The AWS Big Data Blog is a great resource for learning about new Athena features and best practices for using the service. The AWS re:Invent sessions provide in-depth information on using Athena and offer insights from AWS experts. The AWS Solutions Library provides implementation details for using Athena in various scenarios, while the AWS Athena FAQ answers common questions about the service.

In summary, Amazon Athena is a powerful and easy-to-use query service for analyzing data stored in Amazon S3. It provides a serverless and scalable solution for ad-hoc analysis, data warehousing, and business intelligence. With support for various data formats and integrations with other AWS services, Athena is a great tool for performing data analysis and gaining insights into your data.