TABLE OF CONTENTS
1. Overview of AWS Data Analytics2. AWS Data Analytics Services3. AWS EMR4. AWS Athena5. Amazon Kinesis6. Amazon Redshift7. Amazon QuickSight8. Conclusion9. CloudThat10. FAQs
Overview of AWS Data Analytics
Data management systems today have evolved beyond traditional data warehouses to complex structures capable managing complex requirements such as batch and real-time processing and high-speed transactions.
Amazon Web Services (AWS), which offers a variety of data analytics services, allows you to easily create, scale, secure, and deploy large data capabilities. There are many options for analyzing, storing, processing and analyzing large amounts of data.
The following architecture shows how AWS can help you optimize query performance, cut costs, and reduce costs when you create data warehouses on AWS. Amazon EMR is a good option to perform data transformations (ETL), on Apache Hadoop. The transformed data can then be loaded into Amazon Redshift, and made available for BI (business Intelligence) procedures.
AWS Data Analytics Services
AWS allows you to create end-to-end analytics solutions that work for your business. Amazon Machine Learning (ML), which can be used to enhance predictive capabilities in your apps, is also available.
Let’s look at some AWS Data Analytics Services.
Amazon EMR is a managed Hadoop framework that allows you to process large amounts of data quickly, efficiently, and economically. Amazon EMR also supports Presto, Apache Spark, HBase, and other frameworks.
Amazon EMR allows you to transform, move and store large amounts of data in and out of other AWS data storage and databases such as Amazon S3 or Amazon DynamoDB. EMR Notebooks are based on Jupyter Notebooks and allow for collaborative analysis as well as ad-hoc querying.
Machine Learning – EMR has integrated machine learning tools to enable machine learning that is scalable.
Extract Transform Load – EMR can be used for data transformation workloads (ETL), such as sorting, join and aggregate on large datasets at a low price.
Clickstream analysis – You can segment users and deliver successful ads by using EMR in conjunction Apache Hive and Apache Spark to analyze user preferences
Real-time streaming – Analyzing streaming events from Amazon Kinesis, Amazon Kafka or any other streaming data source using EMR/Amazon Spark Streaming is possible
Interactive Analytics – EMR Notebooks is a managed analytic environment based on open-source Jupyter that allows data analysts, scientists, and developers to create reports for interactive analysis.
It is simple to use
Amazon Athena allows interactive querying using standard SQL. It simplifies data analysis in Amazon S3. Athena does not require infrastructure management. Athena is a serverless platform and charges only for actual executions.
You will need to choose an Amazon S3 bucket and create a data schema before you can start querying with SQL. The results are often visible within seconds.
Archival log analysis – Run Athena query to retrieve the required logs, then analyze them.
Validate new data as soon as possible – The user can run a quick query and see the results to determine if they are logical or need to be fixed.
Time-critical ad-hoc data queries
It is simple to use
Pay per query
Integrations with other AWS services are easy
Amazon Kinesis offers four types services: Kinesis Data Analytics (Kinesis Data Firehose), Kinesis Video Streams (Kinesis Data Streams), and Kinesis Data Streams (Kinesis Data Streams).
Amazon Kinesis allows you to gather, process, and analyze streaming data instantly. Amazon Kinesis can handle a variety of data formats, including re.