Partner Name

We helped a manufacturing conglomerate to optimize their data processing jobs with the help Amazon EMR backed with Spark that reduced the manual errors by 20% through streamlined data handling

We made a visible and measurable impact to our client's business

20%

Reduction in manual errors

60%

Reduction in man hours

7000

Tables being processed near real time

Challenge

Industry Overview

The customer is a dynamic conglomerate with a multifaceted presence across manufacturing, technology, consumer goods, and sustainability sectors. Their legacy is built on manufacturing excellence, cutting-edge technology, and a commitment to quality and sustainability.
To address the current data fragmentation, scalability concerns, and limited AI/ML capabilities, Godrej & Boyce Mfg. Co. Ltd. wanted to design and deploy a robust, scalable, and data-centric architecture.

THE PROBLEM

To generate prices based on a scientific pricing model with all demand driving factors considered, along with elimination of manual interventions. Gaps in the current system

With 40 separate data cubes residing in various systems, there exists a significant data fragmentation issue
The current architecture must evolve to accommodate increasing data volumes and changing requirements.
The organization lacks the capability to develop and scale AI/ML models across its operations.

Why were we brought in?

Ganit has made a significant dent in various industries using data science and analysis. Ganit partners with clients to translate their data into a tangible, insightful plan of action that delivers on a measurable impact to the clients’ topline & bottom-line growth.

Specifically for this use case since Ganit has worked with various customers in the past and provided them cutting edge solutions to create a measurable impact to our client’s business a major chunk of these solutions involved an AWS based architecture or services at some step making Ganit a perfectly viable choice for delivering solutions to related business problems.

Our approach

Methodology

Ingest data from Info LN satellite systems and SQL servers using AWS Database Migration Service (DMS) into AWS S3. EMR is used to process the data in raw S3 to transform it to curated S3 layer.

ETL is then performed on the curated data and moved to Amazon Redshift using Amazon EMR in near real time.

Crawlers are used for schema evolution. AWS Glue Crawler can detect changes in the schema or structure of the data sources and automatically update the table definitions in the AWS Glue Data Catalog. This helps to keep the metadata in sync with the changing data source.