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Software Development
Amazon S3, AWS Lambda, Amazon QuickSight, AWS IAM, AWS Glue, Amazon Athena
Increased performance insights and reduced data lag with automated ETL and analytics.
The client specializes in computer vision solutions, focusing on products like BirdiIns, which provides detailed player performance analysis and feedback to enhance gameplay.
Increased goal attainment by better insights
Enhanced Data Processing Efficiency
Reduction in Dataset Creation Lag
The client faces challenges integrating player data from Amazon S3 with metadata from Amazon DynamoDB for their Amazon QuickSight dashboard. The process requires substantial big data processing to join these datasets, complicating the analytics and visualization of player performance across age groups and assessments.
• Initialized a Glue job to extract and join data from S3, storing results in Amazon S3.
• Ran crawlers to update Glue catalog tables and processed data into Parquet files.
• Used AWS Lambda to batch-read DynamoDB data and store it in S3.
• Ran crawlers and Glue jobs to update and refresh catalog tables, scheduled daily.
• Created datasets in Amazon Athena for Amazon QuickSight visualization.
• Developed and refreshed 14 datasets for comprehensive data analysis.
Boosted player goal achievement by 30% and reduced dataset lag by 20% with automated ETL and predictive analytics.
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Ondemand Webinar
AWS
May 2024
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