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Data Warehouse Modeling Bootcamp (Basic to Advance)

By Sahil In Grow Data Skills

Live Classes (22-Nov-2025)
English
Completion Certificate

Demo Class Video

1 Year

Validity (From the date of Enrollment)

40 Hours

Duration

13

Sessions

6

Projects

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This course includes

Content Duration40 Hours
Total Live Sessions13
Total Industry Projects6
Quality Assignments & quizzes after each module
Interview preparation guide
Dedicated placement assistance
Resume & Linkedin profile making
Doubt solving in live classes and offline doubt support on private discord group
Certificate of completion

Course Content

Introduction to Data Modeling
Stages of Data Modeling
Creating logical data models using Excalidraw/Draw.io
Creating physical data model using Snowflake, Power BI, Tableau
ERD Concepts
Entity
Attributes
Types & Notations
Cardinalities
Relationships
Types of Data Models
Star Schema Data Model
Snowflake Schema Data Model
Galaxy Schema Data Model

Snowflake Account Setup
Creating a Snowflake trial account
Understanding Snowflake architecture
Navigating the Snowflake Web UI
Snowflake Tool Overview
Key architectural components: Virtual Warehouses, Databases, Schemas
Separation of Storage and Compute
Understanding Compute Credits & Pricing
UI, worksheets, the history tab, and Role switching
Snowflake Objects
Creating Views, Tables, Materialised Objects, CTEs, Temporary Tables
Sizing virtual warehouses (XS, S, M…)
Monitoring the Snowflake Infrastructure
Designing Models
Identifying facts and dimensions from business requirements
Query performance on OBTs vs star schema
Identifying grain of the fact table
Designing Star & Snowflake data models in Snowflake
Types of Keys: Primary Keys, Foreign Keys, Clustering keys
Schema comparison: Star vs Snowflake
Join strategies in snowflake schema
Flattening dimension tables into a wide table

Understanding the Power BI layers
Data preparation layer/Power Query
Data model layer
Data visualisation layer
What data modeling means in Power BI
Semantic model 101
Building an efficient data model
Star schema and snowflaking
Transactional OBT modeling vs. Star schema modeling
Understanding denormalisation
Relationships in tables
Time intelligence in data modelling
Valid dates in date dimension
Period-over-period calcs
Dynamic measure selection with field parameters
Date table with DAX
Marking a date table

Importing Data into Power BI
Importing data from snowflake
Importing data from CSVs
Creating dims & facts
Introduction to Power Query Editor & M-language
Tables, functions, parameters, constant values
Query Settings & Applied Steps
Data View pane, Status bar
Advanced Editor
Data Preparation Steps
Conversions & Column Transformations
Data type conversion
Splitting, merging, duplicating, and adding columns
Filtering rows, Group By
Appending, merging, referencing queries
Replacing values, extracting numbers
Date/Time handling
Create Data Model
Star Schema Modelling
Identify dimensions and facts
Tables, linkage, granularity, cardinality
Table properties, featured, calculations
Create Dimension tables
Calculated columns, grouping, binning, column properties
Create Fact tables
Aggregations
Date level, Year-Month level
Agg awareness
Multi-fact design
Active & Inactive relationships between dims & facts
Active relationships
Inactive relationships
1:1, 1:M, M:M, M:1
Calculation Groups for time Intelligence

Creating & Implementing Row-Level Security
Applications of RLS
Creating Users
Creating Security Rules
Creating & Implementing Object-Level Security

Data Quality Management for data models
Column quality, distribution, and profiling
Documenting data models
Organising data models into virtual/physical namespaces
Managing access & future requests into data models
Implementing best practices into data model
Query folding strategy
Query-size optimisation
SQL folding
Parameterising

✅ Project - 1: Amazon sales Domain Data Model
Objective: Design a model to track
Product sales across distributors, brands, and customer segments at a granular point-of-sale level.
Integrate data from delivery operations, vendors, invoices, and promotional documents.
Enable time-based analysis with snapshot and time intelligence capabilities.
Implement Row-Level Security (RLS) to restrict access based on region or distributor.
Implement Time Intelligence features like YTD, QTD, MOM using a proper date table.
Create report to analyse:
Sales performance by region, brand, and customer segment
Distributor efficiency and profitability dashboard
Month-over-month and year-to-date sales trend analysis
Delivery performance tracking (on-time, delayed, pending)
Promotional effectiveness using invoice and document types

✅ Project - 2: Goldman Sachs Financial Data Modelling
Objective: Design a model to track
Financial statements across multiple countries and departments.
Enable profit & loss, balance sheet, and cash flow reporting.
Build a scenario analysis layer (actual vs. budget vs. forecast).
Ensure consistency with accounting standards using a normalized chart of accounts.
Include time intelligence mechanisms
Implement RLS for department- or country-specific access.

Create report to analyse:
Profit and Loss by region and department
Scenario variance analysis (actual vs. budget vs. forecast)
Time-based trends in revenue and expenses
Country-level financial comparisons

✅ Project - 3: DHL Logistics & Supply Chain Data Modelling
Objective: Design a model to track
Demand, inventory, shipments, and production across warehouses and manufacturing plants.
Map product flow from source to destination warehouses.
Align demand forecasts with actual sales to analyze planning accuracy.
Integrate inventory levels, in-transit goods, and stock aging metrics.
Enable production plan vs output tracking.
Include calendar-based planning granularity (week, month, quarter).
Implement RLS for plant/warehouse-level access.
Add Time Intelligence to analyze rolling demand and trends.

Create report to analyse:
Demand forecast vs actual sales variance
Inventory trends and stock alerts
Shipment lead time and delivery performance

✅ Project - 4: Transportation Domain Data Model
Objective: Design a model to track
Shipment movements, vehicle performance, and route efficiency across delivery networks.
Capture distance traveled, fuel costs, shipment weight, and delivery timeliness.
Support visibility across origin-destination pairs, transport zones, and delivery SLAs.
Integrate vehicle metadata, driver info, and transportation modes.
Enable Time Intelligence for route-wise performance trends.
Implement RLS by transport zone or logistics manager.

Create report to analyse:
On-time vs delayed shipments by route
Fuel efficiency and cost per km across vehicles
Weekly/monthly shipment trends
Route-level performance analysis
Vehicle utilization and idle time analytics

✅ Project - 5: Bosch Global Treasury Data Model
Objective: Design a model to track
Cash inflows and outflows across multiple sources, currencies, and regions.
Classify cash movements into operational, investment, or financing activities.
Support currency conversion for consolidated cash flow analysis.
Analyze net cash positions, liquidity, and working capital trends.
Incorporate forecast vs actual cash flows across time.
Enable Time Intelligence for rolling cash flow planning.
Apply RLS for treasury roles by country or fund type.

Create report to analyse:
Net cash flow over time by source
Cash position by currency and region
High/low liquidity risk indicators
Source-wise fund utilization analysis

✅ Project - 6: Shopify e-commerce Data Model
Objective: Design a model to track
Online orders, product returns, and sales performance across digital channels.
Capture data by customer demographics, device type, and purchase channel (app/web/mobile).
Integrate customer behavior with sales funnel analysis.
Track return rates, product reviews, and discount usage.
Enable RLS for category managers or regional marketing leads.

Create report to analyse:
Sales by product category, device type, and marketing channel
Return rate trends by brand or product
Promotion-wise, sales uplift during campaigns

Building a Strong Analytics Engineer Portfolio
Resume Optimization for Data Roles
Preparing for Technical Interviews
Networking and Job Search Strategies

Course Schedule

Course Starts On:

22-Nov-2025

Mode of the Course:

Live

Course Duration:

40 Hours

Total Sessions:

Total Projects:

Validity:

1 Year (Starting From The Date Of Enrollment)

Class Timing:

Saturday & Sunday [2:00 PM - 4:00 PM Live Teaching, 4:00 PM to 4:45 PM Live Doubt Session] (IST)

Class Duration:

2 Hours Live Teaching, 45 minutes Doubt Solving

Class Recording Provided:

Yes

Prerequisite:

Passion And Willingness To Learn

⚠️ Important Notice :

The video may not work on Linux due to DRM restrictions. It is only accessible on Chrome when using Windows or macOS.

Workaround: To access the video on Linux, you can create a Windows virtual machine (VM) and watch the video through the VM. Alternatively, you can use our Android or iOS application to view the video on your mobile device.

Instructor

Sahil Choudhary

Sahil is a Google Certified Data Analyst and a Senior Data Analyst at Sigmoid Analytics. He has worked with startups and Fortune 500 companies, helping them make scalable data-driven decisions. He mentored over 350 students in analytics domain and developed third party products and has written multiple whitepapers for BI applications.