What is Amazon Athena? – The New Serverless Data Analytics Tool

Introduction To Amazon Athena

On November 20, 2016, Amazon launched Athena as one of its services. As described earlier, Amazon Athena is a serverless query service that makes the analysis of data, using standard SQL, stored in Amazon S3 simpler. With a few clicks in the AWS Management Console, customers can point Amazon Athena at their data stored in Amazon S3 and run queries using standard SQL to get results in seconds.

With Amazon Athena, there is no infrastructure to set up or manage, and the customer pays only for the queries they run. Amazon Athena scales automatically, executing queries in parallel, which gives fast results, even with a large dataset and complex queries. Now, that you what is Amazon Athena let me take you through the difference it has compared to SQL Server.

Amazon Athena is a serverless data analytics service that allows you to analyze data stored in Amazon S3 using SQL. It is designed to be easy to use, and you can get started in just a few minutes by simply pointing to your data in S3 and setting up a table. Athena is serverless, so there is no infrastructure to manage, and you only pay for the queries that you run. It is also highly scalable, so you can analyze large amounts of data very quickly. In addition, Athena integrates with other Amazon services such as Amazon QuickSight for visualization and Amazon CloudWatch for monitoring and alerting.

Data analysis is a very complex process and there have always been attempts to ease it. There are many tools for analytics, and even the popular tech giant Amazon provides an AWS service named Amazon Athena. This Amazon Athena tutorial will guide you through the basics and advanced usage of Amazon Athena.

Amazon Athena is an interactive data analysis tool used to process complex queries in relatively less time. It is server-less hence, there is no hassle of setting up, and doesn’t require managing the infrastructure. It is not a Database service hence, you just pay for the queries you run. You just point your data in S3, define the schema required, and with a standard SQL you are good to go.

Difference Between Microsoft SQL Server And Amazon Athena

Microsoft SQL Server and Amazon Athena are both relational database management systems, but they have some key differences.

SQL Server is proprietary software developed and maintained by Microsoft. It is a full-featured, enterprise-class RDBMS that can be installed on-premises or in a virtualized environment. It supports a wide range of data types and includes features such as support for complex queries, full-text search, and business intelligence capabilities. Additionally, it provides a number of different tools for managing and maintaining the database, including a graphical user interface and various command-line utilities.

On the other hand, Amazon Athena is a serverless query service that is built on top of the Amazon S3 storage system. It allows you to analyze data stored in S3 using SQL-like queries. It doesn’t require any infrastructure setup and management, making it an easy-to-use and cost-effective solution for ad-hoc querying of large data sets. Unlike SQL Server, it doesn’t support advanced features like full-text search or business intelligence, but it can be integrated with other Amazon services such as Amazon QuickSight to add this functionality.

In summary, Microsoft SQL Server is a full-featured RDBMS that is best suited for enterprise-level data management and analysis, while Amazon Athena is a serverless query service that is well-suited for ad-hoc querying of large data sets stored in S3. If you need to store and analyze large amounts of structured data, and want a more flexible and powerful tool for data management, SQL Server would probably be the better choice. But if you need an easy way to query and analyze large data sets stored in S3, and don’t require advanced features like full-text search or business intelligence, Athena would be a better choice.

MySQL Vs Amazon Athena

Use Of Amazon Athena

If you are a Data Analyst and have experience analyzing data stored on S3, you will relate to this,

Amazon Athena is a serverless, interactive query service that allows you to analyze data in Amazon S3 using SQL. With Athena, you can analyze data stored in Amazon S3 using SQL and other standard data processing frameworks and BI tools. Athena is particularly well-suited to querying large datasets stored in S3 because it uses a distributed query engine that can process data in parallel, reducing the time it takes to run complex queries.

Some common use cases for Athena include:

Analyzing log data stored in S3 for security and compliance purposes

Running ad-hoc queries on data stored in S3 for data exploration and analysis

Querying data stored in S3 as part of a larger ETL (extract, transform, load) pipeline

Running interactive queries on data stored in S3 for business intelligence and reporting purposes

Athena is a fully managed service, which means that you don’t have to worry about setting up and maintaining any infrastructure. You simply pay for the queries that you run, making it a cost-effective solution for querying data stored in S3.

Data Analysts/Developers: Do you offer Storage?

AWS: Yes.

As data analysts or developers, it is not typically within our scope of responsibilities to offer storage for data. Storage is typically provided by the company or organization that we work for or by a third party storage provider. Our role would be to analyze and develop solutions using the data stored in the provided storage location.

Data Analysts/Developers: Do you have tools for Analytics?

AWS: Not sure.”

Amazon worked on this and came up with Amazon Athena. Now, you have a tool to play with your data. Athena helps you analyze unstructured, semi-structured, and structured data that is stored in Amazon S3. Using Athena you can create dynamic queries for your dataset. Athena also works with AWS Glue to give you a better way to store the metadata in S3.

Using AWS CloudFormation and Athena, you can use named queries. Named query allows you to name your query and then call it using the name.

This interactive service from AWS can be used by Data Scientists, and developers to take a sneak peek into the table instead of running the complete query. It is also used to fetch data from S3, and load it to different data stores using Athena JDBC driver, for log storage/analysis and Data Warehousing events.

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