Case Study

E-Commerce Firm Achieves 70% Performance Increase and Centralized Storage with CloudThat

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Retail E-Commerce


Amazon S3, AWS Glue, AWS Lambda, Amazon QuickSight


70% performance increase, unified data schema creation, centralized storage for multi-source data, and a 70% reduction in time to build the sales register

About the Client

Tenovia is India’s top online pet pharmacy and care platform based in Bangalore and has served over 15,000 orders, 40,000 online consultations, and 150,000 pet parents nationwide. Offering a comprehensive range of pet medications and supplies, including preventive care and supplements, Tenovia is your one-stop shop for all your pet needs. 



Automation and Performance Boost

AWS Glue ETL job

Unified Data Schema

Amazon S3

Centralized Storage Solution

The Challenge

Tenovia needed a streamlined method to monitor sales across multiple websites. Despite accessing data through APIs, inconsistencies in format hinder decision-making. To address this, implementing an ETL tool is crucial for cleansing and standardizing data from various sources like Google Drive and online marketplaces, enabling comprehensive analytics in their BI tool. 


  • The goal of achieving a centralized data store for analytics is being achieved by using AWS Glue, AWS Lambda and Amazon S3. 
  • AWS Lambda functions are used to fetch sales data from Shopify, Zoho, Unicommerce, and Google Drive and store it in an Amazon S3 bucket as raw data in 9 different partitions.  
  • The data is stored in CSV format. 
  • AWS Lambda is triggered every day once, and it fetches the data sales data, including cancelled orders from the different APIs provided. 
  • The received JSON payloads are converted to CSV at an AWS Lambda level to enrich the data and store it. 
  • The AWS Glue job is created to process the raw csv data from the 9 partitions and to create a sales register from the last month of sales data. This job is scheduled and runs five times every month. 
  • This AWS Glue job maps the fields among the files from each partition and appends the data to create one single file. 
  • Another AWS Glue job is also created to extract the dates and details of the cancelled orders of the last two days. This job is scheduled to run daily.  
  • This job also maps the field among the files from each partition and appends the data to create a single file. 
  • The AWS Glue jobs store the two files in a centralized location to create the datasets for Amazon Quicksight visualization.

The Results

Achieved zero manual intervention, resulting in a 70% performance increase, established a unified schema, reduced data sources for the BI tool, implemented centralized storage, and slashed the time to build the sales register by 70%. 

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