Microsoft Synapse Implementation Overview

We recommend an integrated data platform solution structured around a Data Lakehouse architecture, utilizing Azure Data Lake Storage (ADLS) for storage.

Case Study Overview:

About Client:

Client is a globally renowned digital identity solution provider trusted by multinational clients in the Banking, Insurance, Retail, E-commerce, Health, Gaming, and Education sectors. These clients rely on our expertise to effortlessly verify the identities of both individuals and entities.

Our clients face challenges such as streamlining customer onboarding within 30 seconds across various devices, addressing significant drop-offs in onboarding and authentication processes, and complying with stringent industry regulations. They are committed to adopting a future-proofed digital solution that prioritizes the security and privacy of client data, aligning with regulations like POPI and GDPR.


To improve the project’s scalability and analytical integration, think about putting the following changes into practice and this project is intended to implement below information:

Adjust Diagnostic settings to route data for batch processing while preserving log ingestion in Application Insights. Send EventHub data in near real-time for processing. Duplicate and store payload via backend API in the data lake for prolonged reporting and analysis. Utilize Azure Data Factory for orchestrating preprocessing, ensuring scalability in a cluster-based analytics environment. Avoid single-machine designs for scalability; explore alternatives like Data Explorer Cluster and serverless SQL pool.

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