- INDUSTRY: Education
- Project Type: BigData, Data Lake
- Location: USA
- Completed Date: 13-07-2023
Executive Summary
A prominent university sought to modernize its data infrastructure by migrating its Student and Employee Data Mart from an on-premises Data Warehouse to a robust, scalable AWS Data Lake. This initiative, aimed to enhance data accessibility, improve system maintainability, and empower the university with advanced analytics capabilities. This case study focuses on detailing the active development and implementation phase, the technologies used, and the resulting benefits of this strategic migration.
Challenge
The university's legacy data warehouse, while functional, presented limitations in terms of scalability, accessibility, and maintainability. To address these challenges and unlock the full potential of its data, the university embarked on a journey to build a modern data platform on AWS.
Several key challenges faced by the university are:
-
Limited Scalability: The on-premises data warehouse struggled to scale with growing data volumes and increasing user demands.
-
Accessibility Constraints: Data access was restricted, limiting self-service analytics and data-driven decision-making.
-
Maintenance Overhead: Maintaining the legacy system was complex and resource-intensive.
-
Lack of Agility: Adapting to changing data requirements and business needs was difficult.
-
Need for Advanced Analytics: The university aimed to leverage advanced analytics capabilities offered by cloud platforms.
Solution
The university, in collaboration with a OktaBytes , executed the plan of AWS Data Lake migration, which included:
-
Data Lake Implementation: Migrating Student and Employee Data Mart to AWS and building a multi-layered data lake (Bronze, Silver, Gold).
-
Data Accessibility: Making data readily accessible from the AWS Data Lake for various users and tools, including Tableau.
-
Rules Engine and Orchestration: Implementing a Rules Engine and Orchestration Step Function for automated data processing and workflows.
-
Silver Layer Consolidation: Consolidating the Silver layer of the data lake for improved data quality and consistency.
-
Priority and History Table Design: Designing priority, non-priority, and history tables for efficient data management.
-
Infrastructure and Code Pipeline Setup: Establishing the necessary infrastructure and CI/CD pipelines for deployment and maintenance.
-
AWS Cost Optimization: Implementing strategies for optimizing AWS costs.
Tech Stack
Python
PostgreSQL
Oracle
AWS Services
S3
RDS
StepFunction
Glue
Redshift
CodePipeline
CodeBuild
CloudWatch
IAM
Impact
-
Successful Data Migration: Migrated Student and Employee Data Mart to AWS, resulting in immediate cost savings and improved scalability.
-
Enhanced Data Accessibility: Data accessibility from the AWS Data Lake, enabled self-service analytics and empowered departments to make data-driven decisions quickly.
-
Improved Data Quality: Consolidated Silver layer ensures better data quality and consistency, reducing errors in reporting and analysis.
-
Automated Processes: Rules Engine and Orchestration Step Function automate data processing and workflows, freeing up valuable IT resources for strategic initiatives.
-
Scalability and Performance: AWS infrastructure provides scalability and improved performance, ensuring the data platform can handle future growth and demands.
-
Cost Optimization: Implemented strategies for optimizing AWS costs, leading to ongoing savings and efficient resource utilization.
-
Foundation for Future Growth: Established a modern data platform that can support future data and analytics initiatives, positioning the university for continued innovation and success.
Conclusion
OktaBytes team in close coordination with the client, successfully transformed the university's data management capabilities by migrating to a modern, scalable AWS Data Lake. This initiative not only addressed the limitations of the legacy system but also provided a robust foundation for future data-driven decision-making. The successful implementation demonstrates the value of cloud migration and the importance of a phased approach for complex projects. This transformation has empowered the university to leverage its data more effectively, paving the way for enhanced analytics and improved outcomes.
