Who is Yogendra Raghuvanshi?

Yogendra Raghuvanshi is an AI & Data Transformation Leader | Program Manager based in Indore, India, with 13+ years delivering enterprise AI, analytics, and data platforms. He leads programs spanning Generative AI, SQLMesh pipelines, StarRocks benchmarking, Python automation, Power BI analytics, and responsible AI governance — with proven impact at Modern Data, Capgemini Invent, and GlobalLogic.

What technical skills does Yogendra Raghuvanshi have?

Yogendra Raghuvanshi specializes in ACOS Optimization, AI Agents, Amazon Marketplace, Apache Spark, Bitbucket, CI/CD Concepts, Data Benchmarking, Data Engineering, Data Quality, Databricks, Decision Intelligence, Digital Transformation, Digital Twins, Documentation, Enterprise AI, Enterprise Analytics, ERD tooling, ETL Pipelines, GCP, GenAI, and related enterprise data and AI technologies.

How can I contact Yogendra Raghuvanshi?

You can contact Yogendra Raghuvanshi via email at yogendra.raghuvanshi31@gmail.com, phone at +91-8130647994, or through LinkedIn at https://www.linkedin.com/in/yogendraraghuvanshi/.

Scalable ETL & Analytics Platform: Architecture, Stack & Delivery

Introduction

In this article I break down how I designed and delivered Scalable ETL & Analytics Platform — from the original business pain point through architecture, technology choices, implementation phases, and lessons learned. This is the same project featured in my portfolio's Built Solutions section, documented here in full technical depth for engineers, architects, and hiring managers who want to understand how the work was actually done.

I led this initiative as part of my broader program delivery work across enterprise AI, data platforms, and analytics transformation. The approach reflects how I operate: start with the business outcome, choose the minimum viable architecture, instrument everything, and iterate with real users.

Business problem

High-volume datasets exceeded legacy pipeline throughput.

Engineered ETL and analytics on StarRocks, Apache Spark, and MinIO for large-scale processing.

Architecture decisions

Key design choices that shaped reliability, performance, and maintainability of the solution.

Medallion pattern: raw MinIO, curated Spark, gold StarRocks
Spark handles heavy transforms; StarRocks optimized for interactive SQL
Cost tracking per pipeline run for FinOps reviews

Technology stack in depth

This project was built with StarRocks, Apache Spark, MinIO, Python. Each technology was selected for a specific role in the architecture — not because it was trendy, but because it solved a measured bottleneck.

StarRocks: production component with documented integration patterns and operational runbooks
Apache Spark: production component with documented integration patterns and operational runbooks
MinIO: production component with documented integration patterns and operational runbooks
Python: production component with documented integration patterns and operational runbooks

Implementation timeline

Delivery followed phased milestones with explicit deliverables at each gate. This kept stakeholders aligned and made progress auditable for program reviews.

Landing zone (2 weeks): MinIO buckets, partitioning strategy, and ingestion contracts.
→ Bucket layout
→ Partition keys
→ Ingestion SLAs
Spark transformation (3 weeks): Curated jobs with data quality checks and idempotent writes.
→ Spark jobs
→ DQ gates
→ Job scheduling
StarRocks serving (3 weeks): Load patterns, MVs, and BI connectivity for analysts.
→ Load pipelines
→ Materialized views
→ Access roles

Medallion architecture on object storage

Raw data lands in MinIO with date-partitioned prefixes and ingestion SLAs. Spark jobs transform raw into curated Parquet with data quality gates at each stage. StarRocks loads gold tables optimized for interactive analyst queries and BI dashboards.

Separating heavy transforms (Spark) from serving (StarRocks) keeps cost predictable: batch compute scales horizontally while the warehouse stays right-sized for query concurrency.

Bronze: MinIO raw buckets with Avro/Parquet and partition keys by event_date
Silver: Spark curated jobs with DQ checks (null rates, referential integrity)
Gold: StarRocks tables with materialized views for common aggregations
FinOps: per-pipeline cost tracking for executive reviews

StarRocks tuning decisions

Load patterns use broker load from MinIO for bulk ingest and routine upserts for slowly changing dimensions. Materialized views pre-aggregate finance and operations metrics that analysts query dozens of times daily.

Duplicate key vs aggregate key table selection per access pattern
Colocate join groups for high-cardinality fact-dimension queries
Role-based access: analysts read gold only; engineers access silver for debugging

Business outcomes

Supported TB-scale workloads with improved query performance.

Success was measured against adoption, latency/throughput targets, and stakeholder feedback — not just deployment dates. Program reviews tracked these KPIs alongside technical milestones.

Lessons learned

Benchmark before commit-object storage plus columnar stores need workload-specific tuning.

If I were starting again, I would invest even earlier in observability and golden test sets. The cost of retrofitting guardrails after pilot launch always exceeds building them in from day one.