Client’s existing data engineering pipeline for big data processing were inefficient and less efficient in terms of data processing speed. This resulted in limitations in scaling the pipeline for multiple use cases.
Cognilytic built the solution in line with the following tenets-
Big Data Management:
Enabling client to successfully process terabytes of data through spark based parallel processing
Lower Turnaround Times:
Reduced job turnaround times through ease of building, training and deploying Databricks notebooks for workloads such as recommendation engines.
Implementation of Lambda Architecture:
Implement Lambda architecture to handle both real time and batch processing scenarios.
Cognilytic built & delivered fully functional end to end big data analytics pipelines (Databricks notebooks) to the ISV customer as per the below specifications:
Created end to end fully functional Scala notebooks
Created fully functional Python notebooks
Data storage and processing pipelines leveraging several Azure services.