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Challenge, approach, and impact
Scalability & Performance
Ensuring the platform could efficiently process large datasets while maintaining high-speed performance.
Complex Data Integration
Centralizing data from multiple sources, including APIs and SQL Server, required robust ETL pipelines.
Workflow Orchestration
Managing dependencies and scheduling tasks efficiently with Apache Airflow for seamless automation.
Optimized Query Performance
Implementing SQL Server views, UDFs, and stored procedures for incremental updates and fast data retrieval.
Error Handling & Monitoring
Building a robust logging system to track execution times, errors, and import metrics for reliability and automated recovery.
Centralized Data Warehouse
Developed a unified data repository integrating SQL Server and API-based data sources for seamless access.
Scalable ETL Processing
Leveraged Apache Spark to handle large-scale data transformations efficiently with distributed computing.
Automated Workflow Orchestration
Used Apache Airflow to schedule, monitor, and optimize ETL pipeline execution.
Optimized Data Transformation
Applied Pandas for efficient data processing and structured transformations before loading into the warehouse.
High-Performance API Layer
Built FastAPI endpoints to ensure fast, modular, and scalable access to processed data.
Robust Monitoring & Reliability
Implemented detailed logging, automated error handling, and self-recovery mechanisms to ensure uninterrupted operations.
Bussines Impact
Enhanced Data Processing Efficiency
Apache Spark-powered ETL pipelines enabled faster, scalable data transformations, reducing processing times significantly.
Improved Decision-Making
A centralized Data Warehouse provided a unified, real-time view of business data, increasing accuracy and accessibility.
Automated Workflows & Reduced Manual Effort
Apache Airflow automated scheduling, monitoring, and orchestration, minimizing operational overhead.
Optimized Query Performance
SQL Server optimizations improved query execution speed, allowing for faster insights and reporting.
Reliable & Scalable Architecture
Robust logging, automated recovery, and scalable APIs ensured high availability and future growth.
Cost Savings & Resource Efficiency
Reduced manual data processing efforts and infrastructure costs by leveraging efficient cloud-based architectures.
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