The AWS Advantage In Data Analytics

For one of the Retail Operating system companies, where Brands, Retailers, and Consumers are connected, Locuz migrated their on-prem data lake to a more agile and scalable architecture. We have set it up on AWS using EMR with Hudi/Presto and Metabase (Open Source BI).

As the world becomes more digital, the amount of data created and collected constantly grows and accelerates. Analysis of this ever-growing data becomes a challenge with traditional analytical tools. Innovation is required to bridge the gap between generated data and data that can be analyzed effectively. To analyze such large datasets involves a reasonable amount of compute capacity which can vary in size depending on the amount of input data and the type of analysis required.

Scale Instantly and Transform with Your Data

This specific nature of big data workloads is best suited to the pay-as-you-go cloud computing model, where applications can scale up and down basis the demand. That’s how AWS with it’s pay-as-you-go model is best suited for managing your large data sets, as requirements change, AWS allows you to resize your environment horizontally or vertically to meet your needs. All of this without having to any additional hardware or investment requirement to provision your new capacity.

On AWS, you can provision more capacity and compute in just a matter of minutes, meaning that your big data applications can grow and shrink as demand dictates, and your system runs as close to optimal efficiency as possible.

In addition, you get flexible computing on a global infrastructure with access to the many different geographic Regions that AWS offers, along with the ability to use other scalable services that augment to build sophisticated big data applications.

As your data continues to grow, you need various options for your various data compute needs, AWS serves you with all such options to get your data to the cloud safely and instantly. This variety of Big Data Analytics offerings make it an ideal fit for solving all your Data needs.

• Amazon Simple Storage Service (Amazon S3) to store data.
• AWS Glue to orchestrate jobs to move and transform the data easily.
• AWS IoT, which lets connected devices interact with cloud applications and other connected devices.
• Amazon Redshift, Fastest and most widely used cloud data warehouse. Break through data silos and gain real time and predictive insights from your data.
• AWS SageMaker, With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment.
• Amazon QuickSight, allows you to understand your data by asking questions in natural language, exploring through interactive dashboards, or automatically looking for patterns and outliers powered by machine learning.
• Amazon EMR, easily run and scale Apache Spark, Hive, Presto, and other big data workloads.