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Case Study
Expanding User Base and Enhancing Multilingual Engagement for a Social Media Platform with Amazon DynamoDB
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Industry
Social Media
Expertise
Amazon DynamoDB, Amazon Kinesis Data Firehose, Amazon Kinesis Data Stream, AWS Glue Service, Amazon Athena, AWS Lambda, AWS IAM, Amazon SQS, Amazon Transcribe, Amazon Translate, Amazon SNS, Amazon Comprehend, Amazon CloudFront, AWS CloudTrail, Amazon CloudWatch, Amazon CloudFormation.
Offerings/solutions
Handles large workloads rapidly, offers service in six Indian languages, and has doubled its userbase with 55% engagement and 15K active monthly users after implementing AI/ML solutions
About the Client
The client’s application is a dynamic social media platform catering to a diverse audience from urban and rural areas across India. They have emerged as a destination for individuals seeking to engage in unforced conversations in their native language. Their offerings are designed to provide a seamless and intuitive user experience, with a wide range of features that facilitate social interactions and sharing of ideas and opinions.
Highlights
55%
Increase in User Engagement
6 Indian Languages
Multilingual Support
15,000+ Users
Monthly Active Users
The Challenge
The client’s initial user base focused on Kannada and Malayalam, and they encountered hurdles in expanding across India, including real-time data processing, multilingual content, user interaction, and efficient AI/ML implementation. Their goal is to grow from 50k users with 25% engagement to a more engaged user base of 5k active monthly users.
Solutions
- The solution centralizes data analysis and AI/ML tasks, utilizing Amazon DynamoDB as the primary database.
- Raw data is transferred to a Data Engineering account using Amazon SQS and AWS Lambda, then stored in DynamoDB for its scalability and performance.
- Data flows from Amazon DynamoDB streams to Amazon Kinesis, distributed to AWS Lambda functions and Amazon S3 for processing.
- AWS Lambda functions handle tasks like audio transcription, translation to SRT format via Amazon Translate, and metadata storage in Amazon DynamoDB.
- Translated text undergoes Key Phrase and Sentiment analysis via Comprehend Lambda functions, with results in Amazon DynamoDB and Amazon S3.
- AWS Glue Crawler performs ETL on S3 data for further analysis.
- Amazon Athena queries transformed data, yielding insights like trending hashtags in DynamoDB.
- User posts, failed tasks, and processed tasks are extracted via AWS Lambda and Amazon API Gateway for the client application.
- The AWS services leveraged in the solution are Amazon DynamoDB, Amazon Kinesis Data Firehose, Amazon Kinesis Data Stream, AWS Glue Service, Amazon Athena, AWS Lambda, AWS IAM, Amazon SQS, Amazon Transcribe, Amazon Translate, Amazon SNS, Amazon Comprehend, Amazon CloudFront, AWS CloudTrail, Amazon CloudWatch, and Amazon CloudFormation.
The Results
Implementing AI/ML solutions enabled rapid workload processing, expanded the platform’s language capabilities to six Indian languages, and doubled the user base with 55% engagement and 15K monthly active users.
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