Ch 1: Introduction to Snowflake

• Database, DWH Introduction
• Cloud Data Warehouse
• Cloud DWH Implementations
• Snowflake Cloud Intro
• Snowflake: SaaS Platform

Ch 2: Snowflake Concepts

• Snowflake Account (Cloud)
• Snowflake Components
• Snowflake Editions, Credits
• Snowflake Editions
• Virtual Private Edition (VPS)
• Snowflake Pricing

Ch 3: Architecture, Warehouse

• Compute Architecture
• Shared Disk Architecture
• CPU & Memory in Clusters
• Database Query & Data Cycle
• ColumnStore, Virtual Warehouse
• Classic UI with Snowflake
• Massively Parallel Processing

Ch 4: Snowflake Databases & Tables

• Snowflake Database Types
• Snowflake Table Types
• Retention Time, Connections
• Permanent, Transient Types
• CREATE TABLE AS SELECT (CTAS)

Ch 5: Time Travel, Recovery

• Time Travel in Snowflake
• Invoking Time Travel Feature
• Timestamp, Offset, Query ID
• Data Recovery, TIMESTAMP
• Fail Safe and UNDROP, OFFSET
• Transient Tables, Real-time

Ch 6: Schemas and Session Context

• Schema Creation Usage
• Permanent, Transient Schemas
• Managed Schemas in Snowflake
• Invoking Schemas & Cloning
• Session Context & Schema
• Data Loading with GUI

Ch 7: Snowflake Cloning

• Cloning with Snowflake
• Zero Copy, Schema Cloning
• Snapshot, Metadata
• Storage & Metadata Layer
• Real-time Considerations
• Transactions & Injection

Ch 8: Procedures & Views

• Procedures and Functions
• SQL and JavaScript & CALL
• sqlText:command
• Cursoring Data and Operations
• Dynamic DML with SPs
• RETURN, RETURNS Statements

Ch 9: Security Management

• Security with Snowflake
• Users & Roles in Snowflake
• Privileges and Groups
• Organization, Account, Users
• Creating, Using Roles, Users
• System Defined Roles Usage
• Role Hierarchy in Realtime
• Views For Security
• RBAC & DAC in Real-time

Ch 10: Snowflake Transactions

• Transaction ACID Properties
• Implicit, Explicit and Auto
• Durability and Data Storage
• Current transaction() Usage
• to_timestamp_ltz and Usage
• Failed Transactions with SPs
•  Transactions and SPs
• Scoped & INNER Transactions

Ch 11: Snowflake Streams & Audits

• Snowflake Streams & Usage
• Streams and DML Auditing
• Snapshot Creation, Offset
• METADATA Options & Streams
• Data Flow & Snowflake Streams
• Streams on Transient Tables
• Time Travel with Stream Tables

Ch 12: Snowflake Tasks

• Tasks, Serverless Compute
• Tasks Tree: Root and DAG
• Tasks Schedules and RESUME
• User & Snowflake Managed
• CRON Syntax with Tasks
• Virtual Warehouse Concepts
• Multi Cluster Warehouse
• Auto Scale Options, Billing

Ch 13: SnowSQL and Variables

• SnowSQL Configurations
• DDL, DML & SELECT
• SnowSQL Command Line
• Variables and Batch Process
• DECLARE, LET, BEGIN & END
• EXECUTE IMMEDIATE, FOR
• Creating Virtual Warehouse
• Writing Output to Files

Ch 14: Snowflake Partitions, Stages

• Snowflake Partitions, Views
• Micro Partition with DML, CDC
• Cluster Key, Depth and Overlap
• Internal Partition Types & Usage
• List, Range and Hash Partitions
• Snowflake Stages, Types
• Internal and External Stages
• COPY Command, Bulk Loads

Ch 15: Azure / AWS External Stages

• Azure Storage Account, BLOB
• SAS: Shared Access Signature
• Using SAS Key and FILE PATH
• Azure Storage with BLOB
• COPY INTO Command Usage
• Snowflake Patterns & RegEx
• File Formats: Creation, Usage

Ch 16: Snow Pipes & Incr Loads

• SnowPipe Incremental Loads
• Azure Queues & Integrations
• Azure Active Directory
• External Stage, Enterprise AD
• Snow Pipes and Data Loads
• Incremental Data Loads
• File Format with Reg Expr

Ch 17: Power BI with Snowflake

• Power BI: Big Data Analytics
• Snowflake Data Access, Views
• Datawarehouse Access, Views
• Server URL & View Access
• Data Analytics with Views

Ch 18: SnowPro Certification Exam Guidance

• SnowPro Certification Overview & Exam Structure
• Certification Domains & Key Topics
• Important Snowflake Concepts for the Exam
• Topic-wise Exam Preparation Strategy
• Practice Questions & Scenario-Based Questions
• Mock Tests & Exam Readiness Assessment
• Common Exam Mistakes & Tips to Avoid Them
• Time Management & Question-Solving Techniques
• Revision Checklist & Last-Minute Preparation
• Guidance on Certification Registration & Exam Process

Ch 1: Databricks Introduction

• Cloud ETL, DWH
• Cloud Computing
• Databricks Concepts
• Databricks Account
• Big Data in Cloud

Ch 2: Databricks Architecture

• Databricks Runtime (DBR)
• RDD & DAG
• Databricks Lakehouse Architecture
• Spark Compute Concepts
• Workers & Drivers
• Databricks Framework
• Databricks APIs

Ch 3: Unity Catalog

• Unity Catalog Concepts
• Metastore
• Catalogs & Schemas
• Managed & External Tables
• Storage Credentials
• External Locations
• Volumes
• GRANT / REVOKE
• Data Permissions
• Lineage
• Data Governance
• Databricks Workspace UI
• Spark Table Creations

Ch 4: Spark SQL: Basics

• Spark SQL Notebooks
• Creating Catalog
• Creating Schemas
• Creating Tables
• Spark Data Types
• PySpark API: SQL Queries
• Dropping Objects
• Notebooks: Exports, Clone

Ch 5: Spark SQL: Table Types

• Delta Tables
• Managed Tables
• External Tables
• Data Partitioning
• Union, Views in Spark
• External Volumes

Ch 6: Spark SQL: Functions

• Math, Sort Functions
• String, DateTime Functions
• Conditional Statements
• SQL Expressions with expr()
• Volume for our Data Assets
• File Formats, Schema Inference
• Spark SQL Aggregations

Ch 7: Spark SQL: Time Travel

• Time Travel Concepts
• Spark DB: Logical Architecture
• Spark DB: Physical Store
• Data File Store
• Log File Store
• Time Travel
• DESCRIBE, EXTENDED
• HISTORY, Version Numbers

Ch 8: Python: Introduction, Print

• Python Introduction
• Python Versions
• Python Implementations
• Python in Spark (PySpark)
• Python Print()
• Single, Multiline Statements

Ch 9: Python: Variables

• Python Variables
• Variable Declarations
• Variable Values
• Value Types
• Multi Variable Values
• Common Variable Values
• Realtime use of Variables

Ch 10: Python: Operators

• Need for Operators
• Arithmetic Operators
• Assignment Operators
• Comparison Operators
• Operator Precedence
• Operands in Python

Ch 11: Python: Control Statements

• Python Control Structures
• If … Else Statement
• Short Hand If
• ELIF & ELSE IF Statements
• OR, AND Concepts
• Python Loops

Ch 12: Python: Data Types

• Python Data Types
• Integer / Int Data Types
• Float, String Data Types
• List Data Type
• Dictionary Data Type
• Tuple Data Type

Ch 13: Python: Modules & DataFrames

• Python Modules
• Pandas
• NumPy
• DataFrame Concepts
• Handling Nulls
• Data Cleansing Concepts
• Python Functions

Ch 14: PySpark DataFrames & Transformations

• Creating PySpark DataFrames
• Reading Data Sources
• select(), filter(), withColumn()
• Handling NULL Values
• Data Type Casting
• Joins
• union() / unionByName()
• Aggregations
• Window Functions
• Writing DataFrames
• DataFrame vs Spark SQL

Ch 15: Medallion Architecture – 1

• Medallion Architecture
• Aggregated Data Loads
• Bronze, Silver and Gold
• Temp Views
• Spark Tables (Parquet)
• Work with File Sources

Ch 16: Medallion Architecture – 2

• Medallion Architecture
• Azure SQL DB Connections
• Joining Source Tables
• Data Frames, Temp Views
• Aggregated Data Loads
• Gold Data Consumption
• Raw Sources → Bronze → Silver → Gold → BI/Analytics

Ch 17: Delta Lake

• Databricks Delta Lake
• Schema Evolution
• Azure SQL DB Connections
• DataFrames, Temp Views
• Delta Table API
• Update, Delete Records
• MERGE / Upsert Operations
• Version History & Data Retention
• Delta Transaction Log

Ch 18: PySpark: Widgets

• PySpark Parameters
• Text Widgets
• User Parameters
• Manual Executions
• Automations
• UI & JSON For Widgets

Ch 19: Lake Flow Jobs

• Workflows & CRON
• Job Compute, Running Tasks
• Python Script Tasks
• Parameters into Notebook Tasks
• Parameters into Python Script Tasks
• Concurrent Executions, Dependencies
• Branching Control with the If-Else Task

Ch 20: PySpark: Auto Loader – 1

• Auto Loader Concept
• Cloud Files Architecture
• Checkpoint Configurations
• Schema Location
• Checkpoint Location
• Initial Loads

Ch 21: PySpark: Auto Loader – 2

• Reading Streams
• Manually Cancel your Data Streams
• Writing to a Data Stream
• Schema Evaluation Modes
• Workspace Modules
• Incremental File Ingestion
• Schema Evolution
• Rescued Data Column
• Trigger Options
• Exactly-once processing concepts

Ch 22: LakeFlow Declarative Pipelines

• SDP: Spark Declarative Pipelines
• Delta LIVE Tables
• Streaming Data Loads
• Bronze, Silver, Gold Data
• Materialized Views
• Pipeline Clusters
• Databricks CLI
• Data Quality Checks

Ch 23: Databricks Optimizations

• Lazy Evaluation
• Explain Plan
• Caching
• Data Shuffling
• Broadcast Joins
• Partitions
• Data Skipping
• Liquid Clustering
• VACUUM
• OPTIMIZE, Z Ordering

Ch 24: Databricks Security, AI

• Overview of ACLs
• Adding a New User to Workspace
• Workspace Access Control
• Cluster Access Control
• Groups & Lake Bridge
• Access Keys (Tokens), Genie AI

Ch 25: GitHub Concepts

• Creating GitHub Account
• GIT Project Concept
• GIT Project Creation
• GIT: Main, Branches
• GIT Credentials
• Connecting with ADF
• Connecting with Databricks

Ch 26: Databricks Data Engineer Certification Guidance

• Exam Q & A, Scenarios
• Certification Exam Objectives
• Topic-Wise Revision
• Scenario-Based Questions
• Practice Assessments
• Mock Exams
• Databricks Interview Questions
• Real-Time Scenario Discussions
• Resume Project Explanation
• Technical Mock Interview

Project Title
Retail Sales Analytics Platform using Databricks (Medallion Architecture)

Project Overview
Students will build a production-style data platform using Lakehouse Architecture
(Bronze, Silver, and Gold), implementing modern ETL practices, data quality validation,
incremental processing, orchestration, optimization, and reporting.

Business Scenario
A multinational retail company receives daily data from multiple operational systems. The
company wants to build a centralized analytics platform to:

• Consolidate data from different business domains
• Improve data quality and consistency
• Track sales performance across regions
• Analyse customer purchasing behaviour
• Monitor inventory levels
• Generate business KPIs for decision-makers
• Deliver Power BI dashboards with near real-time insights

You will learn Snowflake Cloud ETL & Data Warehousing, Databricks, Spark SQL, Python, PySpark, Delta Lake, Medallion Architecture, data pipelines, optimization and data governance.

Yes. The course includes an end-to-end Retail Sales Analytics project using Databricks Lakehouse and Bronze, Silver and Gold Medallion Architecture.

Yes. The curriculum covers Python fundamentals, Pandas, NumPy, DataFrames and PySpark transformations including joins, aggregations and window functions.

The training covers Snowflake architecture, databases, tables, Time Travel, cloning, security, Streams, Tasks, stages, Snowpipe, incremental loading and Power BI integration.

Yes. Topics include Unity Catalog, Delta Lake, Auto Loader, LakeFlow Jobs, LakeFlow Declarative Pipelines, Medallion Architecture, optimization and Genie AI.

Yes. The curriculum emphasizes hands-on practical sessions, assignments, practice labs and production-ready ETL pipelines.

The curriculum identifies roles including Snowflake Data Engineer, Snowflake Developer, Snowflake ETL Developer, DBT Developer / Analytics Engineer, Cloud Data Engineer and Data Warehouse Developer.

Yes. It includes SnowPro certification guidance, Databricks Data Engineer certification preparation, scenario-based questions, mock exams, interview questions, resume project explanation and technical mock interviews.