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Data Analytics on AWS: A Quick Walkthrough

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Introduction

When it comes to finding actionable insights out of data to drive decision-making capabilities and accelerate understanding of the business better and hence serve customers in the best feasible way, data analytics is the domain that comes into the picture.

Data Analytics is a collective, integrated, and collaborative approach that enables organizations to leverage various tools and technologies to become data-driven.

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Big Data Analytics

With digital transformation, customers interacting with no digital products are growing exponentially; hence, the amount of data organizations must deal with is also growing.

Performing analytics on such a huge amount of data while taking care of variety, velocity, veracity, and value refers to big data analytics.

While dealing with big data analytics, challenges can come along the way in terms of collection, storage, cataloging, governance, analytics, and visualization of such a huge amount of data.

Hence, a general flow or set of steps used to be employed that are arranged properly to enable big data analytics, which is called a big data pipeline. The set of steps, phases, or components of the pipeline are as follows:

  • Collection and Ingestion: Data sources must be identified, and appropriate ingestion techniques can be used to collect data.
  • Storage: Based on the nature of the data, data can be ingested and stored in appropriate repositories such as data lakes, databases, or data warehouses.
  • Cataloging and Governance: Data about data,e., metadata, is important, and its existence opens the door for ad-hoc exploration.
  • Processing: Raw data is not a good option to perform analytics on. It must be transformed, if needed, to be in a decent shape that drives better analytics.
  • Analytics and Visualization: Finally, the data is ready for analysis and creating visualization.

Data Analytics on AWS:

Considering the 5Vs and the need to satisfy the requirements of the components or phases of the big data pipeline, traditional or open-source tools, and technologies mostly used on-premises have limitations and challenges in terms of deployment, use, scalability, reliability, security, cost, management overhead, etc.

AWS has a big portfolio of data analytics services that organizations can use to build highly secure, highly performant, cost-effective, and scalable data analytics solutions on AWS Cloud.

 

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Some of the prominent services in action, including Amazon S3, Amazon EC2, Amazon Redshift, Amazon RDS (Relational Database Service)

Some of the prominent services in action include Amazon EMR and AWS DataSync.

Some prominent services include Amazon S3, Amazon RDS, AWS Glue, Amazon EMR, Amazon Athena, and Amazon Redshift.

Leveraged Amazon OpenSearch

Conclusion

A wide portfolio of data analytics services offered by AWS is helping organizations build modern data platforms. Building Data Lakes, Cloud-based Data Warehouse Solutions, and Real Time Streaming solutions is no longer challenging and can be easily built on AWS.

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CloudThat is an official AWS (Amazon Web Services) Advanced Consulting Partner and Training partner, AWS Migration Partner, AWS Data and Analytics Partner, AWS DevOps Competency Partner, Amazon QuickSight Service Delivery Partner, Amazon EKS Service Delivery Partner, and Microsoft Gold Partner, helping people develop knowledge of the cloud and help their businesses aim for higher goals using best-in-industry cloud computing practices and expertise. We are on a mission to build a robust cloud computing ecosystem by disseminating knowledge on technological intricacies within the cloud space. Our blogs, webinars, case studies, and white papers enable all the stakeholders in the cloud computing sphere.

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WRITTEN BY Muhammad Imran

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