Overview
A leading EdTech company partnered with our data engineering team to modernize their data pipeline infrastructure. The client faced delays in data availability, limited real-time insights, and difficulties scaling analytics and machine learning workloads due to their traditional batch-oriented ETL pipelines. To overcome these challenges, they aimed to implement a streaming-first, unified data architecture using Microsoft Fabric and its Streaming capabilities to Onelake Lakehouse.
700+
Enterprise Tables Integrated
60M
Records Ingested per Hour
45%
Reduced Manual Effort
27%
Operational Cost Reduction
Customer Challenges
The client’s existing data infrastructure created several obstacles that limited efficiency, scalability, and compliance. To address these challenges, the client required a scalable, streaming-first architecture built on Microsoft Fabric, providing end-to-end support for ingestion, transformation, and secure delivery of data.
Latency in Data Availability
Traditional batch ETL pipelines delayed the delivery of critical data, slowing time-sensitive decision-making.
Limited Real-Time Analytics
Inability to process streaming data restricted live insights for educators and administrators.
Challenges in Machine Learning Experimentation
Disjointed pipelines and delayed data hindered iterative ML model development and testing.
Scalable Data Sharing
Lack of a unified platform made it difficult to share data across teams and systems efficiently.
Compliance & Data Privacy
Managing sensitive student information was complex, with no streamlined approach for secure data handling.
Solutions
Our team designed and implemented a robust streaming data pipeline to ingest, process, and deliver real-time data from over 700 SQL Server tables from multiple databases into Microsoft Fabric’s OneLake Lakehouse. The architecture was built using the following key components
01.
Real-Time Data Ingestion
Kafka and Debezium were used to capture Change Data Capture (CDC) events from SQL Server databases. These events were published to Kafka topics in real-time, enabling continuous data flow from multiple sources without relying on batch processing.
02.
Microsoft Fabric Streaming Integration
The Kafka topics were seamlessly integrated with Microsoft Fabric Streaming Dataflows, which streamed the data directly into OneLake Lakehouse. This integration ensured near real-time availability of raw data, allowing downstream analytics and reporting to be continuously updated.
03.
Data Transformation & Schema Evolution
A multi-layered data architecture was implemented, following a Raw → Bronze transition using Microsoft Fabric’s transformation tools. Schema evolution logic was added to automatically handle changes in source systems, eliminating the need for manual intervention and preventing pipeline downtime.
04.
Data Obfuscation for ML Use Cases
Data masking and obfuscation techniques were applied during transformation to protect sensitive information. This approach enabled secure data sharing with internal machine learning teams, accelerating model development workflows while maintaining compliance and data privacy.
Contact Us
We’d love to hear from you.
Lets discuss how we can transform your business with AI. Talk to our AI expert team. Lets do AI journey together.
