Interactive, real-time, and analytical data warehousing systems are becoming more common. With the help of modern tools, cloud-based data warehousing technologies have risen to new heights.
This article will discuss the following:
Amazon Athena is a search engine that allows you to run ad hoc queries over huge datasets. It will enable you to create SQL queries and perform them on data stored in Amazon S3 buckets directly. Moreover, users can utilize Amazon Athena to examine data on Amazon S3 using SQL or Structured Query Language. The intention of the software is for ad hoc and advanced analysis.
Analysts do not need to operate any basic compute infrastructure to use Athena because it is a cloud-based query service. Additionally, analysts do not have to import S3 data into Amazon Athena or alter it before analyzing it, making gaining insights simpler and faster.
What is AWS Athena?
What is AWS Athena, you may ask? On Amazon Web Services or AWS Athena, Amazon S3 gets used for online backup and preservation of data and applications. With use cases like information storage, archiving, web hosting, data backup, data loss prevention, and program hosting for deployment, Amazon S3 was intended to make web-scale computing easier for developers.
The shared responsibility model stipulates that AWS Athena security safeguards the core functionality as the cloud service provider while AWS users defend their workloads.
AWS Athena Tutorial
Below is an overview of the AWS Athena Tutorial.
- An AWS Athena user can access encrypted data and encrypt query results using AWS Key Management Service keys. The AWS Management Console, an application programming interface, or a Java Database Connectivity driver are all ways for a data analyst to access AWS Athena. The analyst can then design the structure and execute SQL queries on S3 data using the built-in query editor.
- AWS IAM is used to manage access to Athena. Users can use IAM to reject or allow actions on Athena by attaching identity-based policies to principles like groups or users. Each identity-based policy has statements that describe which acts are prohibited or permitted.
- While connecting to Athena over the public Internet is available, connecting via AWS PrivateLink, an interface VPC endpoint accessible from within the Virtual Private Cloud, is a more secure option.
What is AWS Athena used for?
AWS Athena gets used for online backup and the preservation of data and applications. Fundamentally, the interactive query service provides an analytical tool for analyzing data stored in Amazon S3. Unstructured, semi-structured, and structured data sets can all get processed by Amazon Athena.
Amazon Athena Architecture
In Amazon Athena Architecture, smaller, independent development teams are a systematic strategy for organizations that operate in changing market conditions and must constantly correct their course. Architects could not simply rely on static upfront design to keep up with the rate of change required to succeed in such a setting.
Amazon Athena Architecture’s microservices and two in combination are notable instances of this method. However, having smaller units is not the sole key to winning: These two units must be autonomous in most decision-making to remove organizational constraints and produce high-quality decisions rapidly.
Fitness functions assist users in gathering the information they need to plan the development of the architecture. They establish quantitative metrics to determine how near the solution is to meet the objectives. Moreover, as the architecture changes, fitness functions can and should be adjusted to facilitate a preferred change process. This option gives architects a tool to lead their teams while still allowing them to operate independently.
AWS Cloud services get rendered regulated, fully automated through API activities, and designed to be observable. This function enables users to automatically generate measures for fitness functions such as availability, reactivity, and integrity. AWS account activity, such as configuration changes, can be used to construct fitness functions. This construction is when AWS CloudTrail comes in handy. Most AWS services record account information and system events, which you can later examine with Amazon Athena.
Fitness functions allow architects to concentrate on their work. Once formed, teams in portability and cost can use the data from fitness functions to make choices and act towards a more shared and measurable goal. The architects can then use the data points obtained from fitness functions to corroborate their theory about the existing state of the design.
Amazon Athena Use Cases
Athena assists users in analyzing data stored in Amazon S3 that is unstructured, semi-structured, or structured. Without aggregating or loading the data into Athena, you can use Athena to conduct ad-hoc queries using ANSI SQL. For quick data visualization, Athena works with Amazon QuickSight. Athena can be used to create reports or to study data using data analytics or SQL tools.
Another Amazon Athena use case works with Amazon S3’s Glue Data Catalog, which provides permanent metadata storage for the data. This option makes it easy to create tables and query data in Athena using a central metadata store. It is also accessible throughout the Amazon Web Services account and gets connected with AWS Glue’s ETL and data discovery functionalities.
Things to Consider when Choosing Amazon Athena
Below is an additional list of considerations for choosing Amazon Athena.
Amazon has implemented several query restrictions. Users are only allowed to submit one query per account while performing five concurrent inquiries. Each account can only have 100 databases, and each database can only have 100 tables. While Athena may access data from an area other than the one that began the query, only a few regions are currently supported.
- Data Formats
Converting data to columnar storage formats is recommended by Amazon. Ensure that the team is aware of this improvement, as separating computing and storage is a key component of an interactive query service. Using a compacted and columnar format can help you save money on queries and storage while boosting performance.
- Table and Structure Definitions
To analyze data, users must first ensure that they have data on S3 as a resource. One of the primary benefits of an interactive query service is that the datasets get separated from the compute query architecture. All databases and tables are automatically stored within the system after the process gets completed. Tables allow for the construction of views in circumstances where it is beneficial.
- Performance and Speed
AWS makes it simple to conduct Athena queries on S3 data without the need to set up servers, create clusters, or perform any other maintenance that other query systems necessitate.
Users have control over who has access to their data on S3. It is possible to create delicate security to allow different people to examine various data sets and permit access to data belonging to other users. Users can also use tools to restrict data access further.
AWS Athena Pricing
In AWS Athena, users only pay for the queries they run using Amazon Athena. They are charged based on how much data each query scans. Compressing, dividing, or transforming the data to a columnar format can save you money and improve speed. Each of these procedures minimizes the amount of data that Athena must scan to run a query.
Moreover, the quantity of bytes scanned by Amazon Athena gets rounded up to the nearest megabyte, with a 10MB minimum price per inquiry. Data Definition Language statements, statements for managing partitions, and unsuccessful searches are all free. Charges for canceled queries depend on the amount of data scanned.
Athena can scan less data if users compress their data. When users convert the data to columnar formats, Athena can read only the columns to process the data. Additionally, AWS Athena may also limit the amount of data scanned by partitioning the data. As a result, there are cost savings and better performance.
Lastly, in AWS Athena Pricing, data is retrieved straight from Amazon S3 by Amazon Athena. There are no additional storage fees if you use Athena to query your data. Storage, requests, and data transmission are all charged at typical S3 rates. Query results are saved in an S3 bucket of your choice by default and get charged at standard Amazon S3 prices.
AWS Athena VS Redshift
It is not easy to compare AWS Athena VS Redshift. Athena has the upper hand in portability and cost, whereas Redshift dominates productivity and scalability.
AWS Athena is a serverless service that creates, manages, and scales data sets without requiring any infrastructure. It works with Amazon S3 data sets directly. From an S3 standpoint, it works as a read-only service because it builds external tables and does not change S3 data sources. On the other hand, Redshift is a petabyte-scale data warehouse used in conjunction with business intelligence tools. Unlike Athena, Redshift necessitates the creation of a cluster, for which users must upload data extracts and create tables before querying.
Protecting Your Amazon Athena Data Lake with Satori
Satori enables you to enforce security policies such as dynamic data masking and row-level security on your Athena data access. In addition, Satori continuously discovers and classifies sensitive data, enables self-service data access to datasets on Athena and other data platforms, and keeps an enriched audit log on all data accessed through Athena.
As the world continues to evolve digitally, the capabilities of cloud-based data warehousing systems have become to greater levels as well. It is only right to keep up with its evolution to allow for more robust growth and development of data warehousing services for the public.