Smart data solutions that empower schools.

Category: Edcuation
Services: Cloud Architecture Design and Review, AWS Managed Service Redshift, Managed Engineering Teams, Data Catalog, Data Lake, Data Warehousing.

  • 62% faster processing 
  • 30% lower processing costs
  • 80% ETL automation achieved

Data is the visionary ally that empowers educational institutions with ingenious data management and reporting solutions. Their transformative offerings not only unlock precious time and vitality but also foster seamless connectivity among the triumphant trio of students, parents, and teachers.

Problem statement

  • Challenge of efficiently handling 7-8 GB daily data for insights extraction.
  • Data retrieval, transformation, quality assurance, and cataloging obstacles.
  • Complexities of ETL processing, versioning, automation, error handling, and monitoring.
  • Need for an end-to-end data pipeline for reliable data processing and analysis.

Proposed Solution & architecture

Our solution involves several AWS services and components working together to create an end-to-end data pipeline. Here’s an overview of the proposed solution:

  • Utilized AWS Glue for defining precise ETL jobs, seamlessly transforming and loading data into AWS RDS PostgreSQL database.
  • Data extracted from various sources was transformed and loaded into Amazon Redshift, providing a centralized repository for analytics.
  • Amazon Redshift’s columnar storage and parallel processing capabilities significantly accelerated data retrieval and analysis.
  • Scheduled and event-triggered execution of ETL tasks ensured timely data processing.
  • Introduced upsert mechanism with JSON files, tracked data changes, and seamlessly updated PostgreSQL database.
  • Leveraged S3 event triggers to detect new JSON files, triggering Lambda function updates.
  • Employed AWS Lambda functions to automate various stages, including file unzipping, data transformation, and ETL job initiation.
  • Configured event triggers for automatic Lambda function activation based on specific activities.
  • Created a coherent sequence of AWS Glue ETL jobs, expertly handling errors, retries, and notifications via Slack.
  • Established AWS EventBridge rule for real-time monitoring of ETL job status.
  • Dynamic Lambda function responses ensured swift actions based on job outcomes.
  • Integrated Slack for instant notifications, keeping stakeholders informed.
  • Effectively harnessed AWS’s scalable infrastructure, ensuring efficient handling of varying workloads.
  • Designed a fault-tolerant architecture for quick recovery from failures.
  • Leveraged a holistic solution approach, synergizing AWS Glue, Lambda, S3, and more.
  • Created an automated, scalable data pipeline addressing data quality, automation, error management, and data analysis.

Metrics for success

  • We have implemented an exclusion pattern that effectively eliminates undesirable files, such as meta files and those that have previously been crawled. As a result of this enhancement, we have achieved a remarkable 62% reduction in processing time.
  • Realized a 30% decrease in data processing costs through AWS Glue’s optimized resource allocation and managed services.
  • Automated 80% of ETL workflows, freeing up valuable time for data engineers and analysts.
  • Amazon Redshift’s columnar storage and parallel processing reduced data loading time by 50%, expediting analytics in a centralized repository.

Arhitecture Diagram


AWS Services

  • AWS Lambda: We utilized AWS Lambda to execute code seamlessly in response to events, eliminating the need for server management.
  • Amazon Redshift: Our central data warehousing solution, seamlessly fused with AWS components. It streamlined data processing for high-speed analysis of extensive datasets, empowering data-driven insights and strategic decision-making in our project.
  • Amazon S3: Amazon S3 served as our scalable and durable object storage solution, accommodating diverse data types securely.
  • AWS Glue: AWS Glue automated the ETL process, enabling smooth data movement and transformation across various sources, feeding our data repositories.
  • Amazon RDS: Amazon RDS simplified our relational database operations, offering ease in setup, operation, and scalability for our project’s databases.
  • AWS EventBridge: AWS EventBridge facilitated seamless event routing among various applications, streamlining integration efforts in our project.
  • Amazon CloudWatch: We relied on Amazon CloudWatch for monitoring and gaining insights into our AWS resources and project applications.
  • AWS IAM: AWS IAM efficiently managed user identities and permissions, ensuring secure access to AWS resources throughout our project.

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