Case Study

Building a Comprehensive Data Ecosystem for an Indian Voice-Based Social Media Platform to Enhance User Engagement

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Social Media


Amazon S3, Amazon SQS, AWS Lambda, Amazon SNS, Amazon DynamoDB, Amazon API Gateway, Amazon EC2


Scalable Data Engineering for Global Impact with 100k Users and 55% Engagement

About the Client

Amtex, with over 25 years of expertise, a team of 1500+ professionals, and a clientele of 250+ global entities, excels as a Systems Integrator in voice-based social media. Originating in India, it facilitates free expression in native languages, connecting diverse urban and rural audiences for authentic dialogue and idea sharing. 


100k users

Achieved a user base increase


User Engagement


Active users per month

The Challenge

The client lacks data engineering experts and wants to enhance customer experience through modern data-driven solutions. Key technical hurdles include data comprehension, real-time processing, multilingual content, user interaction, AI-generated content, automation, and data analytics system monitoring. 


  • CloudThat’s analytics team created a separate AWS account for data-driven solutions. 
  • Established cross-account access to the primary AWS account. 
  • In the data engineering account, set up a centralized data lake using Amazon S3 for raw JSON data storage. 
  • Transferred data between accounts using Amazon SQS and AWS Lambda, streaming it to Amazon DynamoDB and Amazon Kinesis. 
  • Utilized Amazon Transcribe to transcribe audio to text in various languages, storing the data in Amazon DynamoDB and Amazon S3. 
  • Translated transcribed text into SRT format for multiple languages and stored it in Amazon DynamoDB and Amazon S3. 
  • Processed Amazon API Gateway requests and stored data in Amazon DynamoDB. 
  • Employed Amazon SQS to send raw transcribed text to Amazon Comprehend and Translate AWS Lambda functions, filtering for hashtags and storing them in Amazon DynamoDB. 
  • Stored data from Amazon Kinesis Data Firehose in Amazon S3, with data transformation through AWS Glue Crawler and ETL jobs. 
  • Queried data from Amazon S3 using Amazon Athena and fetched required features with AWS Lambda for storage in Amazon DynamoDB. 
  • Stored data from automated and manual hashtags in Amazon DynamoDB in an Amazon S3 bucket. 

The Results

Delivered a scalable data system achieving a 100k user base, 55% engagement, and 15k active users monthly, featuring global hashtag queries, AI/ML support, and enhanced user experience with trending hashtags, multilingual closed captions, sentiment analysis, and streamlined Slack integration for monitoring. 

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