Data Design: A Smart Data Reporting Solution That Empowers Schools

Category: Education

Services: Cloud architecture design and review, Resilience, Data recovery planning and implementation, managed engineering teams, Data Catalog, Data lake, Data warehousing, Data modeling and Schema design, Cost Optimization

  • 62% reduction in processing time and improved system resilience 
  • 99.9% system uptime for the AWS RDS PostgreSQL database.
  • 40% reduction in data errors and discrepancies
  • Reduced 30% in data processing costs

About Data Design offers data management and reporting solutions for educational institutions. Its transformative offerings save time and improve communication between students, parents, and teachers.


  • Data Design required efficient daily management of 7-8 GB of data for insights extraction.
  • Data Design needed resilient systems and recovery strategies to ensure uninterrupted services.
  • Data Design should have a robust disaster recovery plan to ensure quick and efficient restoration of data and systems in case of a disaster.
  • ETL processing, versioning, automation, error handling, and monitoring are complex.
  • Need for an end-to-end data pipeline for reliable data processing and analysis.
  • Data Design needed data recovery plans that comply with privacy regulations like GDPR.

Proposed Solution & Architecture

  • Our AWS experts designed cloud-native architecture to ensure high resilience and availability. 
  • We used AWS Glue to establish reliable ETL jobs, ensuring smooth transformation and data loading into the AWS RDS PostgreSQL database, focusing on resilience and data recovery.
  • The data extracted from diverse sources was transformed and loaded into Amazon Redshift, establishing a resilient and centralized repository optimized for analytics.
  • Our team scheduled and event-triggered execution of ETL tasks ensured timely data processing.
  • We introduced an update mechanism with JSON files, tracked data changes, and seamlessly updated the PostgreSQL database.
  • Our team leveraged S3 event triggers to detect new JSON files, triggering Lambda function updates.
  • We employed AWS Lambda functions to automate various stages, including file unzipping, data transformation, and ETL job initiation.
  • Our experts configured event triggers for automatic Lambda function activation based on specific activities.
  •  We used the AWS EventBridge rule for real-time monitoring of ETL job status.
  • Our team designed a fault-tolerant architecture for quick recovery from failures.
  • We created an automated, scalable data pipeline addressing data quality, automation, error management, and analysis.
  • We used AWS IAM to manage user identities and permissions, ensuring secure access to resources throughout the project based on client security and compliance requirements.

Metrics for success

  • We have achieved a remarkable 62% reduction in processing time and improved system resilience by implementing an exclusion pattern design architecture.’
  • Our team achieved 99.9% system uptime for the AWS RDS PostgreSQL database using AWS Glue’s resilient ETL jobs and data transformation processes.
  • We achieved a 40% reduction in data errors and discrepancies after migrating to Amazon Redshift’s centralized repository, which improved data integrity and resilience.
  • Reduced 30% in data processing costs through AWS Glue’s optimized resource allocation and managed services.

About Data Design


AWS Service

  • AWS Lambda: We used AWS Lambda to execute code seamlessly in response to events, eliminating the need for server management.
  • Amazon Redshift: Our centralized data warehousing solution is seamlessly integrated with AWS components, allowing for high-speed analysis of extensive datasets and data-driven insights.
  • Amazon S3: We used Amazon S3 as a secure, scalable, and reliable storage solution for a diverse range of data types.
  • AWS Glue: Our experts leveraged AWS Glue to automate the ETL process, enabling smooth data movement and transformation across various sources and feeding our data repositories.
  • Amazon RDS: We used Amazon RDS to simplify relational database operations, offering ease in setup, operation, and scalability for our project’s databases.
  • AWS EventBridge: Our team of experts used AWS EventBridge to facilitate seamless event routing among various applications, streamlining integration efforts in our project.
  • Amazon CloudWatch: We relied on Amazon CloudWatch to monitor and gain insights into our AWS resources and project applications.
  • AWS IAM: Our team used AWS IAM efficiently to manage user identities and permissions, ensuring secure access to AWS resources throughout our project.

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