Real Outcomesfrom Intelligent Execution
Explore how we help enterprises and fast-moving teams solve real-world problems — with measurable results, clean engineering, and scalable platforms.
Streaming ETL & Digital Metrics Optimization
Project Overview
Challenge
They needed a modular, high-performance ETL framework capable of handling batch + streaming use cases, with strong cost and performance optimizations.
Our Solution
We redesigned their digital metrics pipelines using Spark on AWS and added real-time ingestion layers with Kafka.
Modular ETL Redesign
Rebuilt legacy Impala jobs using PySpark and Spark SQL. Created reusable ETL modules with parameterized logic. Implemented schema evolution and retry handling logic
Streaming Integration
Ingested app/web logs using Apache Kafka. Processed real-time metrics via Spark Structured Streaming. Built event-based aggregation logic for instant insights
Performance Tuning
Applied optimizations: partitioning, caching, broadcast joins, and memory spill handling. Migrated to AWS EMR clusters for autoscaling. Reduced job runtime and improved overall cluster stability
Data Quality Automation
Integrated automated profiling checks using PySpark. Generated summary reports with alerting (email + Slack). Implemented Hive table validation and metadata consistency scripts
Results
90% memory reduction on Impala workloads
Achieved 90% memory reduction on Impala workloads through optimized query patterns and resource allocation.
60% improvement in pipeline runtime
Improved pipeline runtime by 60% through modular ETL design and Spark optimizations.
+45% increase in reporting freshness
Significantly improved reporting freshness through real-time data ingestion and processing.
Reusable ETL framework cut onboarding time for new datasets by half
Established a reusable ETL framework that reduced onboarding time for new datasets by 50%, enabling faster time-to-insight.
What Our Client Says
We went from fragile batch jobs to modular, observable pipelines. It’s the foundation of our data ecosystem now.