PROJECT P1
Retail Sales Lakehouse
A pipeline that explains sales, returns and product performance — with reconciliation, quarantine and failure recovery.
INGEST · ADF→LAND · ADLS Gen2→TRANSFORM · Databricks→SERVE · Delta + SQL
OUR PROGRAM
Full Stack Snowflake Data Engineering.
Learn the foundations. Build connected systems. Develop the practical skills to explain, debug and improve your work.
Approx. 2–2.5 months · Live classes
UPCOMING LIVE BATCH
Full Stack Snowflake Data Engineering
Free Demo Classes: November 13 & 14, 2026
Batch Starts: November 16, 2026
- Relational foundations, constraints and transactions
- Joins, aggregations, CTEs and subqueries
- Window functions: ROW_NUMBER, RANK, LAG and running totals
- Data modelling, star schemas, grain and analytical workloads
Hands-on lab
Build a retail star schema and answer sales questions with joins and window functions.
Your outcomeWrite explainable SQL and validate the results against the business question.
- Cloud concepts, subscriptions, resource groups and cost controls
- Azure storage, ADLS Gen2, landing zones and permissions
- Data Factory linked services, datasets and Copy Activity
- Parameters, triggers, monitoring and incremental loads
Hands-on lab
Build a parameterised Azure Data Factory pipeline that lands source files in ADLS.
Your outcomeExplain cloud resources, data ingestion, access and pipeline monitoring.
- Snowflake architecture, virtual warehouses and access control
- Stages, file formats, COPY INTO and Snowpipe
- Streams, tasks and change-data processing
- Time Travel, zero-copy cloning, query tuning and cost awareness
Hands-on lab
Load structured and JSON data, maintain customer history, and recover a table.
Your outcomeBuild a Snowflake workflow and explain its performance, quality and recovery choices.
- Workspaces, notebooks and compute
- PySpark DataFrames, Spark SQL and explicit schemas
- Joins, window functions and business-key deduplication
- Jobs, task dependencies and pipeline monitoring
Hands-on lab
Combine customers, products and orders into a clean sales dataset using PySpark.
Your outcomeTransform data in Databricks and explain how the pipeline runs.
- Repositories, commits, branches and meaningful change history
- Pull requests, reviews and merging changes
- Resolving conflicts and collaborating safely
- Project structure, run instructions, .gitignore and secret handling
Hands-on lab
Publish a version-controlled pipeline project with a README and a reviewed change.
Your outcomeExplain your project history and follow a clean team development workflow.
Supporting skills that connect the stack
- Programming fundamentals and data structures
- CSV, JSON and pandas
- REST APIs, pagination and timeouts
- Validation, exception handling and reusable scripts
Hands-on lab
Create a file-cleaning script that separates valid and rejected records.
Your outcomeRead, validate and transform source data using a reusable Python workflow.
- Drivers, executors, partitions and Spark execution
- Bronze, silver and gold responsibilities
- Delta MERGE, SCD Type 1 and duplicate prevention
- Auto Loader, checkpoints, incremental files and recovery
Hands-on lab
Build bronze-to-gold Delta tables and rerun a batch without duplicating results.
Your outcomeExplain data checks and recovery in a repeatable lakehouse pipeline.
- dbt run, test and build in a Snowflake workflow
- Staging, intermediate and mart layers
- Unique, not-null, relationship and freshness tests
- Jinja macros, snapshots, documentation and CI workflow
Hands-on lab
Build tested sales and customer marts in a dbt project.
Your outcomeExplain a transformation model's dependencies, tests and documentation.
From learning to building
Two connected, end-to-end projects.
PROJECT P2
Retail Analytics Warehouse
Trusted analytics for revenue, repeat purchases and product categories — tested models, docs and a prepared CI workflow.
LOAD · Snowflake→STANDARDISE · dbt staging→MODEL · Facts + Dimensions→VALIDATE · Tests + SQL
WHO IT'S FOR
A starting point.
And a next step.
Freshers, career switchers and working professionals are welcome. We start with SQL and work towards connected projects. Consistent practice matters more than collecting tools.
Career preparation includes resume and Naukri profile support, mock interviews and project walkthroughs. Placement and salary are not guaranteed.
Discuss your background →