Case Study

RedoQ Optimizes Data Management and Achieves 50% Performance Improvement with CloudThat

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Amazon S3, Amazon Redshift, AWS IAM, AWS Lambda, Amazon API Gateway, Amazon CloudWatch, Amazon Athena, AWS Glue, AWS CloudTrail


Boosts query performance, enhances data security, executes complex queries efficiently, facilitates dataset generation for visualization, offers scalable data processing, and is the primary backend for BI and user dashboards.

About the Client

RedoQ is a leading UK-based boutique outsourcing firm offering global customized software, web, and network solutions. Specializing in dynamic operations, we craft innovative web, mobile, and cross-platform solutions to keep clients ahead of the competition.



Improved Query Performance

Restricted Access

Enhanced Data Security

Executes complex queries

Efficient Data Processing

The Challenge

RedoQ faces a critical challenge in optimizing operations and strategic decision-making without an enhanced data warehouse solution. With two Business Wings, DineOrder and Kuick, specializing in online food services, they struggle to leverage data effectively due to a lack of BI tools.


  • Utilized AWS Glue jobs and crawlers to seamlessly migrate data from RDS Aurora MySQL and MongoDB Atlas into Amazon Redshift. 
  • Optimized ETL processes to handle the migration of 10 tables, each containing 20-25 columns, ensuring data integrity and efficiency, given the total data size of 25 GB. 
  • Leveraged Amazon Redshift as the primary data warehousing solution, chosen for its scalability and performance benefits, essential for efficiently processing large data volumes. 
  • Implemented daily data pushes from Amazon Aurora to Amazon Redshift to maintain up-to-date analytics and reporting capabilities. 
  • Utilized Amazon QuickSight’s to create interactive and visually appealing dashboards tailored to RedoQ’s food delivery and restaurant business segment. 
  • Implemented materialized views in Amazon Redshift to optimize query performance and reduce latency in dashboard generation. 
  • Employed AWS Lambda functions to convert materialized views into JSON format, ensuring efficient data transmission to client applications. 
  • Deployed the solution in the Europe (London) AWS region to minimize latency and comply with data residency requirements.

The Results

Query performance increased by 50%, data security, efficient processing of complex queries, diverse dataset generation, scalability for gigabytes of data, and serves as the primary backend for BI and user dashboards.

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