What is “The Weight of Data Gravity” Issue and How to Use it with AWS Modern Data Architecture

Introduction

Data is the “Gold” that is getting generated and is also flowing exponentially. To accelerate the business initiatives, create new customer experiences, building new revenue streams, technically the same “Gold” has to be handled and treated smartly.

However, as mentioned, the rate of volume with which “Data” is exploding has increased the probability of falling down under this gravity wherein organizations have to face challenges to meet scalability, easy access, rich analytics, customer preferences, market dynamics, data security, privacy, compliance among other challenges.

AWS helps organizations tackle this “Weight of Data Gravity” by offering a suite of rich data stores and purpose-built analytics services while integrating the pieces in such a way that enables reinvention, innovation, and analytical goals.

Need for a Modern Data Architecture

The way a huge amount of “Data” generated and coming from various sources in various formats, is flowing is an important piece of data systems.

Inside-out data movement

Data Lakes are a great way to store data coming from a variety of sources which can be referenced by other purpose-built data stores or analytics services to perform different analytics including descriptive, diagnostic, prescriptive, and predictive.

(Image Source: Amazon Web Services)

Outside-in data movement

Other than data lakes, purpose-built data stores such as data warehouses or databases are used to store customer data which may need to flow back in the data lakes to support ML use cases such as running product recommendation algorithms against a large dataset stored in data lake by moving sales product information by region from the warehouse to that data lake.

(Image Source: Amazon Web Services)

Around-the-perimeter movement

There can be a need to move data seamlessly from one purpose-built store to another around the perimeter to support use cases such as allowing users to search for product catalogs by offloading the queries to a search service under the hood for which catalog data from the warehouse needs to move to the search service.

(Image Source: Amazon Web Services)

AWS enables the above-mentioned “Data Movement” while tackling the “Gravity” by allowing organizations to build data architectures around a “Central Data Lake” whose essential pillars are Amazon S3, AWS Lake Formation, Amazon Athena, AWS Glue while surrounding it with a ring of purpose-built data and ML services such as Amazon Aurora, Amazon DynamoDB, Amazon SageMaker, Amazon Redshift, Amazon EMR, Amazon Kinesis. Analytics on AWS

This enables:

• Building a scalable data lake rapidly
• Deliver performance using purpose-built data and ML services
• Ensure seamless data movement
• Maintains compliance with security, monitoring, and management of data access
• Cost-effective and reliable services

(Image Source: Amazon Web Services)

AWS Modern Data Architecture Pillars

What is required to architect a modern data platform, AWS focuses on the following pillars to give  solid foundation to Modern Data Architectures that organizations can build, run, and manage using broadest and deepest portfolio of AWS services including scalable data lakes, analytics services, and machine learning services to withstand against the “Gravity.”

(Image Source: Amazon Web Services)

Case Studies for reference:

BMW Group Uses AWS-Based Data Lake to Unlock the Power of Data

BMW Group Case Study (amazon.com)

(Image Source: Amazon Web Services)

Nielsen Builds Cloud-Native Data Reporting Platform on AWS

Nielsen Case Study (amazon.com)

(Image Source: Amazon Web Services)

Conclusion

With a Modern Data Architecture on AWS while leveraging the capabilities of AWS scalable data lakes and purpose-built analytics and machine learning services, organizations can come out with limitless possibilities for reinventing “End-to-End Modern Data Architectures” without the fear of “Weight of Data Gravity”.

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