Rebuilding a platform
for data extraction from
3d party analytical sources
elt platform
The client's platform allows users to extract data from various third-party analytical sources such as Adwords, Facebook, Google Analytics, Google Management, or Big Query. Users can then transform and visualize this data according to their requirements and upload it to their desired destination. The platform offers flexibility in selecting specific data, extraction frequency, and output format.
Lack of Admin Panel
The platform needed an admin panel, limiting the ability to manage and configure settings effectively.
Scalability Issues
The existing AWS-based architecture hindered scaling and failed to accommodate the growing user base & increasing data processing demands.
Slow Data Processing
The database needed help to process and transform user data efficiently, impacting the platform's overall performance.
Logic Entanglement
Platform logic was intertwined with the database, making maintenance and updates complex.
Architecture Redesign
The monolithic architecture was revamped to a microservices architecture, leveraging multiple independent Lambdas to ensure seamless functionality and isolate failures.
Database Enhancements
The platform's database was further enhanced to facilitate the efficient upload and transformation of the user's analytical context based on selected pre-settings.
Frontend Development
A user-friendly web application with an admin panel was designed and developed. Users gained the ability to add data sources, configure data extraction intervals, specify transformation rules, and access server-usage statistics.
backend Transformation
The project was migrated from a JS framework to Python, enhancing data processing speed and enabling the integration of AI algorithms. Our team has decoupled the platform's logic from the Postgres database and transferred it to the backend using Python.
Improved Performance
The redesigned architecture and backend transformation significantly improved data processing speed, enabling users to extract and transform data more efficiently.
Enhanced Flexibility
Adding an admin panel empowered users to easily manage and configure their data sources, extraction schedules, and transformation rules according to their needs.
Streamlined Maintenance
Untangling the platform logic from the database simplified maintenance and updates, making it easier to implement future enhancements.
Scalability and Growth
The revamped architecture provided scalability, enabling the platform to handle a growing user base and increased data processing requirements.
AI Integration
Adopting Python and supportive AI algorithms enhanced the quality and capabilities of data transformation, enabling users to derive more valuable insights.
// sum upImpact
The client's platform successfully addressed its scalability, performance, and functionality limitations through a comprehensive redevelopment process.
The new architecture, backed by efficient backend and frontend implementations, empowered users to extract, transform, and manage their data efficiently. The enhanced database and streamlined logic improved performance, scalability, and future growth possibilities for the platform and its users.
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