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Case Studies
Engineering Governance & Platform Build
Client: SiParadigm

Challenge

  • No architecture oversight

  • No structured code review process

  • No data team or unified platform

  • Uncontrolled AWS costs

Solution

  • Created Architecture Board

  • Introduced mandatory code review process

  • Built data team from scratch

  • Designed AWS-based data platform

  • Implemented cost optimization framework

Impact

  • 40% faster onboarding

  • 50% reduction in production bugs

  • ~30% AWS cost reduction

  • Enabled AI and analytics initiatives company-wide

Snowflake ELT Optimization
Client: NorthBay Solutions

Challenge

Massive datasets required transformation. Traditional ETL approaches took 3–4 hours.

Solution

  • Designed ELT pattern inside Snowflake

  • Implemented JavaScript stored procedures

  • Pushed transformation logic into compute layer

Impact

  • Processed 4 TB in 20 minutes

  • ~90% processing time reduction

  • Reports available at start of business day

Real-Time Change Data Capture (CDC) Pipeline
Client: S&P Global

Challenge

Batch ETL created 4–6 hour latency for analytics. Decision-makers operated on stale data.

Solution

  • Designed Apache Kafka-based CDC streaming pipeline

  • Ensured data consistency under high-velocity change streams

  • Enabled real-time ingestion into analytics platform

Impact

  • Data latency reduced to under 5 minutes

  • Enabled near real-time financial analytics

  • Improved responsiveness for time-sensitive reporting

Telecom Data Platform Transformation
Client: Lebara (via Confiz)
Domain: Telecom Data Lake Platform

Challenge

Challenge A: Escalating Cloud Costs

Azure Databricks and Synapse costs were increasing without transparency.

Challenge B: Daily Pipeline Failures

Platform instability due to missing validation layer and tight coupling.

Challenge C: Monolithic Architecture

200+ tightly coupled ETL pipelines causing cascading failures.

Solution

Solution A:

  • Workload-level cost analysis

  • Right-sized clusters

  • Introduced serverless workloads

  • Conducted POCs for evidence-based architectural decisions

Solution B:

  • Designed Data Quality Scorecard system

  • Implemented lineage tracking (Purview + Unity Catalog)

  • Introduced validation checkpoints

Solution C:

  • Proposed and introduced Data Mesh architecture

  • Designed independent domain-based data products

  • Delivered proof-of-concept implementations

Impact

Impact A:

  • ~35% reduction in cloud costs

  • CFO-level cost visibility

  • Cost governance embedded into design process

Impact B:

  • 70% reduction in failures

  • Mean time to resolution reduced from hours to minutes

  • Restored trust in analytics outputs

Impact C:

  • Independent failure isolation

  • Recovery time reduced from days to hours

  • Business domain ownership established

High-Performance Big Data API Platform
Client: S&P Global
Domain: Financial Data & Analytics

Challenge

Multiple teams were attempting to expose terabytes of analytical data via REST APIs. Existing solutions were too slow. Business users required fast, queryable access to large datasets.

Parallel development efforts were consuming time and resources without convergence.

Solution

  • Designed Elasticsearch-based architecture for high-speed querying

  • Implemented ingestion pipelines using Apache NiFi

  • Built API layer using Node.js & Express

  • Optimized indexing and aggregation strategies

  • Loaded 1.5 TB of data in 10 minutes

Impact

  • Sustained 575 queries per second

  • Sub-second response time for complex aggregations

  • Unified fragmented team efforts

  • Recognized in leadership review