Project snapshot
Intellias helped a client migrate their legacy data processing flows to a more efficient Microsoft Azure-based infrastructure, improving the performance of data management components and accelerating data pipelines. By implementing the most advanced cloud database services, our team transformed the client’s data environment, reducing processing times and improving the quality of data to be used in analytical algorithms.
Partnering with global leaders in the banking sector, asset managers, scholars, and academics, our client has established a reputation for providing professional technology services and solutions to financial institutions and retirement planning companies.
Business challenge
In implementing retirement management solutions, our client manages data from multiple sources in a variety of formats. To preprocess that data and prepare it for consumption by analytical algorithms, the company used cloud-based infrastructure that could no longer ensure the level of performance required to provide excellent customer service.
The client was looking for a solution allowing them to accelerate data processing, improve data quality, and enhance their system’s consistency and process continuity.
Solution delivered
Based on analysis of the client’s legacy infrastructure, our team of cloud engineers discovered that while the client’s environment was hosted in Microsoft Azure, it did not fully leverage Azure’s vast capabilities. To improve performance of the company’s data pipelines, we rethought the approach to data ingestion and storage, redesigning the processing flow with advanced software components:
- Replaced Azure SQL Database with Databricks to enable advanced data intelligence
- Implemented Snowflake data marts as a repository for dynamic access by data analytics and business intelligence tools
- Enhanced the infrastructure with Azure Data Lake to provide flexibility in the size, format, and speed of incoming data and ensure straightforward processing
- Created an event-driven architecture using Azure Functions
Business outcomes
Transforming the client’s data processing flows brought a noticeable improvement in performance and service consistency:
- Easier extraction and processing of incoming data files
- Improved reliability of data flows
- Accelerated data processing and reporting
- Ability to consume and process files in various formats and provide them to multiple users, for example, Salesforce system components
- Streamlined data pipelines
- Greater resilience due to a distributed file processing flow in which errors in a single file do not block the entire process