Scaling a 100TB+ Data Pipeline for a Financial Platform
A rapidly growing financial platform was struggling with data pipeline failures, escalating cloud costs, and processing delays that impacted downstream analytics and compliance reporting. Azminds rebuilt their entire data infrastructure on Databricks, delivering 60% faster processing and 40% cost reduction.
The Challenge
The client's data platform had grown organically over three years, resulting in a fragile patchwork of batch jobs, cron scripts, and poorly documented pipelines. As data volumes exceeded 100TB daily, the system was breaking down.
- ✕Pipeline failures 3–5 times per week, causing stale dashboards and missed SLA windows for regulatory reporting
- ✕Cloud infrastructure costs growing 25% quarter-over-quarter with no corresponding improvement in performance
- ✕Data processing jobs taking 8–12 hours to complete, delaying analytics by half a business day
- ✕No data quality monitoring — downstream teams discovered data issues only after stakeholders flagged incorrect reports
- ✕Single points of failure across the pipeline with no retry logic or dead-letter handling
Our Approach
Azminds assembled a team of 4 senior data engineers who conducted a full audit of the existing infrastructure before designing a modern lakehouse architecture on Databricks.
Lakehouse Architecture on Databricks
Redesigned the data platform using a medallion architecture (bronze → silver → gold) with Delta Lake for ACID transactions, time travel, and schema enforcement.
Spark Job Optimization
Rewrote critical Spark jobs with proper partitioning strategies, broadcast joins, and adaptive query execution — reducing compute time by 60%.
Automated Pipeline Orchestration
Replaced fragile cron jobs with Apache Airflow, adding dependency management, retry logic, alerting, and SLA monitoring.
Data Quality Framework
Implemented Great Expectations for automated data validation at every pipeline stage, with Slack alerts for anomalies and freshness violations.
Cost Optimization
Right-sized Databricks clusters, implemented auto-scaling policies, and moved cold data to cost-effective storage tiers.
Results Delivered
Within 12 weeks, the client's data platform was processing 100TB+ daily with zero missed SLAs. The automated data quality framework caught issues before they reached downstream consumers, and the optimized Databricks clusters reduced monthly cloud spend by 40%. The client's analytics team went from receiving delayed, unreliable data to having fresh, validated data available within 30 minutes of ingestion.
Technology Stack
““Azminds didn't just fix our pipelines — they gave us a data platform we can trust and scale. The 40% cost reduction alone paid for the engagement in the first quarter.”
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