Top 5 AWS Data Analytics Services to Master in 2022

Overview of AWS Data Analytics

Today’s data management systems have progressed beyond typical data warehouses to complex structures capable of managing complicated requirements like batch and real-time processing, unstructured data, and high-speed transactions.

Amazon Web Services (AWS) provides various data analytics services that allow you to create, scale, secure easily, and deploy extensive data capabilities. The capabilities for gathering, storing, processing, and analyzing big data differ substantially.

The below architecture depicts AWS helps you optimize query performance and cut costs when you install data warehouses on AWS. You can conduct data transformations (ETL) on Apache Hadoop, for instance, using Amazon EMR. The transformed data can be loaded into Amazon Redshift and made ready for BI (business intelligence) procedures.

AWS Data Analytics Services

AWS enables you to build end-to-end analytics solutions for your business. You may use Amazon Machine Learning (ML) to add predictive capabilities to your apps.

Let us understand some of the AWS Data Analytics Services:

AWS EMR

Amazon EMR provides a managed Hadoop framework for processing large amounts of data in a simple, rapid, and cost-effective manner. Other frameworks that Amazon EMR supports include Presto, Apache Spark, and HBase.

Amazon EMR also allows you to transform and move massive amounts of data into and out of other AWS data stores and databases, such as Amazon S3 and Amazon DynamoDB. EMR provides capabilities for collaborative analysis and ad hoc querying in the form of EMR Notebooks, which are based on the Jupyter Notebook.

Use Cases:

1. Machine Learning – For scalable machine learning techniques, EMR has built-in machine learning tools
2. Extract Transform Load (ETL) – EMR can be used to conduct data transformation workloads (ETL) such as sort, join, and aggregate on massive datasets at a low cost
3. Clickstream analysis – You can segment users and offer successful advertisements by analyzing user preferences using EMR in conjunction with Apache Hive and Apache Spark
4. Real-time streaming – Analyzing events from Amazon Kinesis, Amazon Kafka, or any other streaming data source is possible using EMR and Amazon Spark Streaming
5. Interactive Analytics – With EMR Notebooks, you’ll get a managed analytic environment built on open-source Jupyter, which helps data analysts, developers, and scientists to prepare and generate reports for interactive analysis

Benefits:

1. Easy to use
2. Cost-Effective
3. Elasticity
4. Reliability
5. Security

AWS Athena:

Amazon Athena has interactive querying capabilities using standard SQL. It simplifies data analysis in Amazon S3. There is no need to manage infrastructure when utilizing Athena. Athena is a serverless platform that only charges for queries that are actually executed.

To get started with Athena, you must choose an Amazon S3 bucket, build a data schema, and start querying with SQL. The results are usually visible in mere seconds.

Use Cases:

1. Archival log analysis – Run Athena query on required logs, gather the results, then analyze
2. Validate new datasets as soon as possible – The user can run a quick query to see the results and see whether they appear logical or if they need to be fixed first
3. Time-critical ad-hoc data queries 

Benefits:

1. Easy to use
2. Serverless
3. Pay per query
4. Fast performance
5. Easy integrations with other AWS services

Amazon Kinesis:

Amazon Kinesis offers four types of services—Kinesis Data Analytics, Kinesis Data Firehose, Kinesis Video Streams, and Kinesis Data Streams.

Amazon Kinesis is a service that allows you to collect, process, and analyze streaming data in real time. Amazon Kinesis can handle various data formats, including real-time audio and video streams, website clickstreams, and application logs.

Use Cases:

1. Analyze real-time stock data
2. Real-time social media tracking
3. Real-time digital advertising updates based on data

Benefits:

1. Processing streaming data
2. Real-time insights
3. Serverless
4. Scalability
5. Pay-as-you-go model

Amazon Redshift:

Redshift is a data warehouse that is highly scalable, quick, and cost-effective. Amazon Redshift uses machine learning, parallel query execution, and columnar storage on the high-speed disc to achieve fast performance.

Use Cases:

1. Optimizes the business intelligence – Amazon Redshift makes it possible to create data-driven reports and dashboards
2. Enables collaboration and shares data – Amazon Redshift facilitates the securely sharing of the data among accounts, organizations, and partners
3. It improves financial, and demand forecasts – Amazon Redshift automates the creation, training, and deployment of machine learning models for predictive insights, allowing economic and demand forecasts to be improved

Benefits:

1. Fast performance and easy to use
2. Cost-effective
3. Scalable
4. Highly secure data warehousing solution
5. It is Cloud-Based and Managed

Amazon QuickSight:

Amazon QuickSight is a business intelligence service from Amazon. QuickSight allows you to share insights with collaborators. Amazon QuickSight integrates your data in the cloud and brings in information from various sources.

QuickSight can combine AWS data with third-party data, big data, spreadsheet data, and more in a single data dashboard. QuickSight allows decision-makers to study and comprehend data in an interactive visual environment.

Use Cases:

1. Connecting to Data Warehouses
2. Connecting to Operational Databases
3. Connecting to Data Lakes

Benefits:

1. Quick access to data sources and easy to use
2. Fast calculation
3. Effective dashboards with different visualizations
4. Easy embedding with websites and portals
5. Better insights

Conclusion

Data analytics demands scalable, flexible, and high-performing technologies to give timely insights as more and more data is generated and collected.

AWS offers a variety of big data analytic options. Most big data architecture solutions rely on various AWS products to develop a complete solution.

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FAQs

1. How many EMR clusters can be run simultaneously?

You can start as many clusters as you like. You are limited to 20 instances across all your clusters when you get started.

2. How long can you store data in Kinesis?

A Kinesis data stream stores record for 24 hours by default, up to 365 days.

3. How much data can a Redshift database hold per cluster?

Depending on the node type, a Redshift data warehouse cluster can contain 1–128 compute nodes.

4. What is Glue ETL?

AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores and data streams.

5. How do I create a calculated field in QuickSight?

In your analysis, choose to Add at the top left, then choose to Add calculated field. Then, enter a name for the calculated field. Enter a formula using fields from your dataset, functions, and operators.

WRITTEN BY Daneshwari Mathapati