Big Data Architect
in Analytics & BIAbout this course
Build the Data Infrastructure Companies Run On
ProDAC's Big Data Architect program takes you from advanced SQL and Python to Spark, Kafka, Airflow and cloud data platforms in 90 hours of live, mentor-led learning, built for people who want to design and run the pipelines the rest of the data team depends on.
Built for Career Outcomes, Not Just Course Completion
Every module, project and mentoring session is designed around one goal: making you genuinely employable as a data engineer.
Industry-Aligned Curriculum
From advanced SQL to Spark, Kafka and cloud platforms, the same stack real data engineering teams run in production.
Real Pipelines, Real Scale
Work with datasets large enough that a naive query or script actually breaks, so you learn to design for scale, not just correctness.
From SQL to Distributed Systems
A deliberate progression: advanced SQL and modeling first, then distributed processing, then orchestration and streaming.
A Portfolio You Can Show
Leave with a portfolio of real-world pipeline projects and a capstone you can walk an interviewer through, step by step.
Structured Interview Prep
Practice SQL deep-dives, pipeline design questions and system-design style interviews before you sit a real one.
Learn-by-Doing Format
Every tool, from a Spark job to an Airflow DAG, is built hands-on in the same session it's taught.
Where This Program Can Take You
Data engineering skills are in demand everywhere data volume is growing: BFSI, e-commerce, healthcare, logistics and SaaS all need reliable pipelines.
Indicative Salary Progression (India)
Indicative industry ranges, not a guarantee. Actuals vary by city, company and experience.
Every company generating meaningful data volume needs someone to move, clean and structure it reliably, which is why data engineering remains one of the most consistently in-demand and well-compensated paths in tech.
The Data Engineering Stack You'll Master
A focused, in-demand stack: real tools, real colors, hover to see the name.
Your 90-Hour Journey, Mapped Out
A deliberate progression from SQL and systems basics to distributed pipelines and cloud platforms, about 11 to 12 weeks at a steady, structured pace.
Foundation
Career prep, SQL & Python for DE
Systems Basics
Linux, modeling & warehousing
Batch Processing
ETL, Spark & PySpark
Orchestration & Streaming
Airflow, Kafka & streaming pipelines
Cloud Platforms
Azure Data Factory & Databricks
Career Ready
Capstone & interview prep
90 Hours, 15 Modules, Zero Filler
Tap any module to see exactly what you'll learn, build and submit.
Topics
- Resume building & LinkedIn setup
- GitHub basics & version control
- SDLC & Agile methodology
- Understanding the data engineering ecosystem
Practical Exercises
- Create and push your first GitHub repository
- Set up a baseline data engineering resume
Assignments
- Publish a GitHub profile README
- Complete a self-assessment of current skill gaps
Topics
- Window functions & CTEs
- Query optimization & execution plans
- Indexing strategies
- Complex joins & subqueries at scale
Practical Exercises
- Optimize slow queries against a large dataset
Assignments
- Rewrite and benchmark 10 inefficient queries
Topics
- Scripting for automation & file processing
- Working with APIs & JSON data
- Error handling & logging for pipelines
- Writing modular, reusable pipeline code
Practical Exercises
- Build a script that pulls, cleans and saves data automatically
Assignments
- Build a small automated data-ingestion script
Topics
- File system navigation & permissions
- Shell scripting basics
- Process & job monitoring
- Cron jobs & scheduling
Practical Exercises
- Automate a repetitive task with a shell script
Assignments
- Schedule a script to run automatically via cron
Topics
- Star & snowflake schemas
- Normalization vs. denormalization
- Fact & dimension tables
- Modeling for analytics vs. transactions
Practical Exercises
- Design a star schema for a real business scenario
Assignments
- Submit a data model with justification for design choices
Topics
- Warehouse architecture & concepts
- Slowly changing dimensions
- Partitioning & clustering strategies
- Warehouse performance tuning
Practical Exercises
- Design and populate a small warehouse for a sample business
Assignments
- Implement slowly changing dimensions on a sample table
Topics
- ETL vs. ELT patterns
- Batch vs. incremental loads
- Data quality & validation checks
- Idempotent pipeline design
Practical Exercises
- Design an ETL flow for a multi-source dataset
Assignments
- Document an ETL design with failure-handling built in
Topics
- Distributed computing concepts
- Spark architecture & execution model
- RDDs, DataFrames & transformations
- Partitioning & performance basics
Practical Exercises
- Run and inspect Spark jobs on a sample cluster
Assignments
- Process a large dataset using Spark transformations
Topics
- PySpark DataFrame API in depth
- Joins, aggregations & window functions at scale
- UDFs & performance tuning
- Reading & writing multiple file formats
Practical Exercises
- Build a full transformation pipeline in PySpark
Assignments
- Process and aggregate a multi-gigabyte dataset with PySpark
Topics
- DAGs, tasks & operators
- Scheduling & dependency management
- Retries, alerts & monitoring
- Orchestrating multi-step pipelines
Practical Exercises
- Build and schedule a DAG that runs a multi-step pipeline
Assignments
- Orchestrate an end-to-end ETL flow using Airflow
Topics
- Topics, partitions & brokers
- Producers & consumers
- Message delivery guarantees
- Kafka in a streaming architecture
Practical Exercises
- Set up a producer-consumer pair streaming sample events
Assignments
- Build a small event-streaming demo using Kafka
Topics
- Pipelines, datasets & linked services
- Data flows & transformations
- Triggers & scheduling
- Monitoring pipeline runs
Practical Exercises
- Build a cloud-native pipeline in Azure Data Factory
Assignments
- Orchestrate a multi-source ingestion pipeline in ADF
Topics
- Notebooks & cluster management
- Delta Lake fundamentals
- Job scheduling in Databricks
- Collaborative pipeline development
Practical Exercises
- Build and run a transformation job on Databricks
Assignments
- Implement a Delta Lake table with versioned updates
Topics
- Object storage fundamentals
- Data lake vs. data warehouse
- Bronze, silver & gold layer design
- Access control & storage cost basics
Practical Exercises
- Design a layered data lake structure for a sample project
Assignments
- Organize a raw-to-curated data lake for a chosen dataset
Topics
- Batch vs. streaming architectures
- Structured streaming with Spark
- Windowing & watermarking
- Monitoring streaming jobs
Practical Exercises
- Build a streaming pipeline processing live event data
Assignments
- Deploy an end-to-end streaming pipeline from source to sink
The End-to-End Pipeline Capstone
One complete build that mirrors a real data engineer's project: ingest raw data from multiple sources, transform it at scale with Spark, orchestrate the whole flow with Airflow, land it in a cloud data lake and warehouse, then present the architecture and design decisions to a mentor panel, just like a real design review.
This is the project you'll lead with in interviews.
Pull raw data from multiple sources, including a streaming feed.
Clean and reshape it at scale using Spark and PySpark.
Automate and schedule the full flow with Airflow.
Walk a mentor panel through your architecture and tradeoffs.
From Last Module to First Offer
Skill-building is half the journey. The other half is making sure you can sell those skills.
Resume Building
Craft a results-driven, data-engineering-ready resume that survives the first filter.
LinkedIn Optimization
Build a profile that recruiters actually find and message.
Mock Interviews
Practice live with SQL deep-dives, pipeline design and system-design style rounds.
Portfolio Reviews
Get mentor feedback on every pipeline and project before you ship it.
Career Mentoring
1:1 guidance on roles, companies and negotiation.
System Design Training
Sharpen the pipeline and architecture reasoning most data engineering rounds test first.
Built for Every Starting Point
Hover over each path to see who it's built for. Whichever stage you're starting from, the curriculum meets you there.
Students & Freshers
Build job-ready data engineering skills, especially useful if you come from a CS or engineering background.
Working Professionals
Move from DBA, backend or analytics roles into data engineering with weekend and evening batches that fit your job.
Career Switchers
Move into data engineering from a non-tech background with a structured, mentor-guided path.
Software Developers
Already write code? Add distributed systems, pipelines and cloud data platforms to a strong existing engineering base.
Big Data Architect Program
Verify at prodac.iokode.com
A Certificate That Verifies Real Skill
Your ProDAC Certified Big Data Architect certificate is a digitally verifiable record of what you actually built.
Each certificate carries a unique ID that can be validated on ProDAC's verification page.
Add it directly to your profile to signal verified, project-backed skills to recruiters.
Comes with real-world pipeline projects and a capstone as proof, alongside the certificate.
Ready to Accelerate Your Career?
Talk to our admissions team and find out if this program is the right next step for you.
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