AWS Analytics Use Cases | Tools Used in Amazon Analytics

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In our tutorial, we talked about AWS Developer Tools. Here, in this session, we will discuss the tools used for AWS Analytics. Moreover, we will discuss types of Amazon Analytics and their use cases.

AWS Analytics is a data analysis process which analyzes the data with a broad selection of analytic tools and engines.

So, let’s start the AWS Analytics Tutorial.

Amazon Analytics Use Cases

AWS provides the broadest and most cost-efficient set of analytic services that run on the data lake.

AWS Analytics

Amazon Web Services Analytics

The AWS Analytics service is specially made for analytics use cases such as-

a. Interactive Analytics

i. Amazon Athena
Amazon Athena makes it simple to research knowledge in Amazon S3 and Amazon Glacier using normal SQL queries. Pallas Athena is serverless, therefore there’s no infrastructure to set up or manage.

You’ll be able to begin querying knowledge instantly, get leads to seconds and pay just for the queries you run. Merely purpose to your knowledge in Amazon S3, outline the schema, and begin querying using normal SQL. Most results are delivered among seconds.

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AWS Analytics Use Cases | Tools Used in Amazon Analytics

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AWS Analytics Use Cases

b. Big Data Processing

i. Amazon EMR
Amazon EMR helps by facilitating a managed service which makes it easy to manage a large amount of data. AWS EMR supports nineteen completely different ASCII text file comes as well as Hadoop, Spark, HBase, Presto, and more.

Every project is updated in EMR among thirty days of a version unharness, making certain you’ve got the most recent and greatest from the community.

c. Data Warehousing

i. Amazon Redshift
Amazon Redshift is designed to run and power complicated queries against large structured data. Structured or unstructured data in S3 is not a necessity for excess data movement. Amazon Redshift is a smaller amount than a tenth of the value of ancient solutions.

Begin little for simply $0.25 per hour, and scale resolute petabytes of information for $1,000 per terabyte p.a.

d. Real-Time Analytics

i. Amazon Reaction
For the period of time AWS analytics, this makes it simple to gather method and analyze streaming knowledge like IoT (Internet of Things)measuring knowledge, application logs, and website clickstreams.

This alters you to the method and analyzes knowledge because it arrives in your knowledge lake, and responds in the period of time rather than having to attend till all of your knowledge is collected before the process will begin.

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e. Operational Analytics

i. Amazon ElasticSearch Service
For operational analytics like application observance, log analytics, and clickstream analytics, Amazon ElasticSearch Service permits you to go looking, explore, filter, aggregate, and visualize your knowledge in close to the period of time.

Amazon ElasticSearch Service provides the service to the workloads and it delivers easy-to-use APIs and enhances with capabilities such as provision, scalability, and security.

f. Dashboards and Visualizations

i. Amazon QuickSight
For dashboards and visualizations, Amazon QuickSight helps the user to create excellent visualizations and makes dashboard which can be accessed with the help of a browser or mobile device.

Tools for AWS Analytics

These are Tools used for AWS Analytics, let’s discuss them one by one:

Tools for AWS Analytics

Tools for AWS Analytics

a. Amazon Athena

Athena is simple to use. Merely purpose to your knowledge of Amazon S3, outline the schema, and begin querying using commonplace SQL. Most results deliveres among seconds.

With Athena, there’s no would like for complicated extract, transform, and cargo (ETL) jobs to arrange your knowledge for analysis. This makes it straightforward for anyone with SQL skills to quickly analyze large-scale datasets.


AWS EMR provides straightforward, fast, and efficient to method immense amounts of data across dynamically ascendible EC2 instances which is produces by a managed Hadoop framework.

The user can execute alternative common distributed frameworks like Apache Spark, HBase, Presto, and Flink in AWS EMR.

AWS EMR firmly and faithfully handles many use cases such as

  • Log Analysis
  • Net Categorization
  • Knowledge Transformations (ETL)
  • Machine Learning
  • Money Analysis,
  • And Bioinformatics.

c. Amazon CloudSearch

Amazon CloudSearch performs tasks such as lineup, management, and scale a probe resolution for the website or application. AWS CloudSearch includes thirty-four languages. It also comes with a common search option like highlight, autocompletes, and geospatial search.

d. Amazon ElasticSearch Service

Amazon ElasticSearch Service makes it simple to deploy, operate, and scale ElasticSearch for log analytics, full-text search, application watching, and more. AWS ElasticSearch Service, a managed service which delivers ElasticSearch’s easy-to-use arthropod genus.

Elasticache easily merges with Kibana, Logstash, and AWS service. It also merges with Amazon reaction Firehose, AWS Lambda, and Amazon CloudWatch which helps to transfer from data to unjust insights quickly.

e. Amazon Kinesis

Amazon Kinesis is a platform for streaming knowledge on AWS, providing powerful services to form it straightforward to load and analyze streaming knowledge, and additionally providing the flexibility for you to create custom streaming knowledge applications for specialized desires.

AWS Kinesis services modify you to try and do that merely and at a coffee value.

AWS Kinesis presently offers 3 services:

  • Amazon Reaction Firehose
  • Amazon Reaction Analytics
  • AWS Reaction Streams

So, this was all about AWS Analytics Tutorial. Hope you like our explanation.


Hence, we studied AWS Analytics helps the industry to integrate. It provides various tools and benefits through which the company increases its outcome. It is easy to maintain and many basic things are either free of cost or only for the basic cost.

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