Data Design: A Smart Data Reporting Solution That Empowers Schools

Category: Education

Services:  Managed Engineering Teams, Cloud Architecture Design and review, Data Processing, Data Ingestion, and Data Governance

  • 35% reduction in time to extract daily volume of data
  • 45% improvement in data processing and analysis reliability
  • 30% increase in operational efficiency

About Data Design provides data management and reporting solutions for educational institutions. It streamlines communication between students, parents, and teachers, saving valuable time.


  • The client needed effective management of daily 7-8 GB of data extraction.
  • It also needed a robust and resilient system for uninterrupted operation.
  • Data Design needed a robust disaster recovery plan to ensure quick and efficient data restoration.
  • Needed to simplify ETL processing, versioning, automation, error handling, and monitoring.
  • An end-to-end data pipeline was needed for reliable data processing and analysis.
  • Data recovery mechanisms were required to comply with privacy regulations.   


  • Simform’s experts designed the cloud-native architecture for Data Design to enhance system resilience.
  • We used AWS Glue to establish reliable ETL jobs and ensured smooth data transformation. 
  • Our team ensured data loading into the AWS RDS PostgreSQL database was seamless.
  • Simform experts scheduled and event-triggered execution of ETL tasks to ensure timely data processing.
  • We created an update mechanism with JSON files to track data changes and seamlessly update the PostgreSQL database.
  • Our experts used S3 event triggers to detect new JSON files, triggering Lambda function updates.
  • We used AWS Lambda functions to automate various stages, including file unzipping, data transformation, and ETL job initiation.
  • Our team of experts implemented the AWS EventBridge rule for real-time monitoring of ETL job status.
  • We designed a fault-tolerant architecture for quick failure recovery, ensuring no disruptions to Data Design’s operations.
  • We created an automated, scalable data pipeline addressing data quality, automation, error management, and analysis issues.
  • 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.
  • We utilized Amazon API Gateway to create accessible APIs for data extraction, which the platform uses to provide reliable analytics.
  • Our team utilized AWS Secrets Manager to securely store the credentials for the database containing the data, effectively safeguarding them from unauthorized access.
  • Our team secured the database credentials using AWS Secrets Manager to prevent unauthorized access.


  • Achieved a 35% reduction in time to extract daily volume of data utilizing highly optimized compute resources and data extraction methods.
  • Achieved 45% improvement in data processing and analysis reliability with the  introduction of an end-to-end data pipeline
  • Data Design achieved a 30% increase in operational efficiency, including data processing, extraction, transformation, loading, and analytics.

Arhitecture Diagram


AWS Services

  • AWS Lambda: We utilized AWS Lambda to execute code in response to events, eliminating the need for server management.
  • Amazon S3: We utilized Amazon S3 as a secure, scalable, and dependable storage solution for various data formats.
  • AWS Glue: Our team utilized AWS Glue to automate ETL, facilitating seamless data movement and transformation across multiple sources and feeding our data repositories.
  • Amazon RDS: We utilized Amazon RDS to simplify database operations, providing an easy setup, scalability, and operation for our project.
  • AWS EventBridge: Our team utilized AWS EventBridge to streamline integration efforts and facilitate seamless event routing among various applications.
  • Amazon CloudWatch: We used Amazon CloudWatch to monitor and gain insights into our AWS resources and applications.
  • AWS IAM: Our team efficiently used AWS IAM to manage user identities and permissions, ensuring secure access to AWS resources throughout the project.
  • Amazon API Gateway: We leveraged Amazon API Gateway to create APIs for data extraction that the platform can access to provide reliable analytics.
  • AWS Secrets Manager: Our team used AWS Secrets Manager to store the credentials for the database that contains the data. This will help ensure the credentials are not exposed to unauthorized users.
  • Amaz0n VPC: We leveraged Amazon VPC to isolate the data extraction process from the rest of the network. This helps Data Design protect the data from unauthorized access.

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