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#Databricks Data Engineer

Databricks Data Engineer is the backbone of today’s data-driven world — uniting data, analytics, and AI on one collaborative platform. Built on Apache Spark, it enables seamless data integration, scalable ETL pipelines, and real-time analytics.
From finance to healthcare, Databricks Data Engineers power intelligent insights, optimize data workflows, and drive business transformation across industries.

Training Highlights

✅ Databricks Architecture
✅ PySpark & Spark SQL
✅ Delta Lake & Delta Tables
✅ Databricks Notebooks
✅ Python, PySpark, SQL
✅ Event Hub, Kafka
✅ Job Workflows & Automation
✅ CI-CD Integrations (DevOps)
✅ Real Time Projects

Modules We Learn:

✅ Module 1: Databricks
✅ Module 2: Real Time Project (E-commerce)
✅ Module 3: Databricks Data Engineer Certification Guidance

Course Duration: 7 Weeks

Databricks Data Engineer

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Module 1: MSSQL & TSQL Queries, Tuning

Ch 1: SQL Database Job Roles

Database Intro
OLTP, DWH, OLAP
DBMS Basics
Data Engineer Job Roles

Ch 2: Database Intro & Installations

SQL Server Installations
Instance & Collations
SSMS Tool Installation
Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

SQL Basics (DDL, DML, etc..)
Creating Databases, Tables
Data Inserts (GUI, SQL)
Basic SELECT Queries

Ch 4: SQL Basics V2 (Commands, Operators)

DDL: Create, Alter, Drop
DML: Insert, Update, Delete
DQL: Select, Fetch
SQL Operators

Ch 5: Data Types & Variables

Integer, Character Data Types
Decimal & Boolean Data Types
Date and Time Data Types
SQL_Variant Type

Ch 6: Data Imports

Data Imports with Excel
Data Types Detection
OLE-DB Connections
ORDER BY, TOP, OFFSET

Ch 7: Schemas & Batches

Schemas, Table Grouping
Real-world Banking Database
2 Part, 3 Part, 4 Part Naming
Batch & “Go” Command

Ch 8: Constraints, Keys & RDBMS

Null, Not Null Constraints
Unique Key & Check
Primary Key & Default
Foreign Keys, DB Diagrams

Ch 9: Normal Forms & RDBMS

Normal Forms: 1 NF, 2 NF
3 NF, BCNF and 4 NF
1:1, 1:M, M:1 Cardinality
Self Referencing Keys, ER Models

Ch 10: Sub Queries

Sub Queries
Nested Queries
Correlated Sub Queries
Union, Union All

Ch 11: Group By Queries

Group By, Having
Cube( ) & Rollup( )
Grouping( )
IsNull(), Coalesce()

Ch 12: Joins & Queries

Joins & TSQL Queries
Inner Joins, Matching Data
Outer Joins: Left, Right, Full
Self Joins, Conditions

Ch 13: Joins with Group By, Sub Queries

Join, Group By, Sub Queries
IIF(), CASE Statement
EXISTS, NOT EXISTS
Query Execution Order

Ch 14: Views & RLS

Views: Realtime Usage
DML, SELECT with Views
WITH CHECK OPTION
RLS, Important System Views

Ch 15: Functions & Queries

Scalar, Table Value Functions
Variables & Parameters
Date Time, Conversion Functions
String, Aggregated Functions

Ch 16: Advanced SQL, Window Functions

Window Functions (Rank)
Row_Number, DenseRank
Partition By, Lead & Lag
Pivot, UnPivot, Running Totals

Ch 17: Stored Procedures – 1

Stored Procedures, Parameters
SELECT & Conditional DML
IF Statement, SP Recompilation
System Stored Procedures

Ch 18: Stored Procedures – 2

Merge Statement (Upsert)
Merge with OLTP & DWH
Matched and Not Matched
Merge & SP Recompilations

Ch 19: SQL Query Performance & Indexing

Clustered, NonClustered Indexes
Execution Plans, Tuning Queries
SARGable Queries, NULLIF
Query Optimization Examples

Ch 20: CTEs & Tuning

Common Table Expression
Data Cleansing Techniques
Duplicate Detection/Removal
Cursor Basics – When to Use & Avoid

Module 2: Python For Data Engineers

Ch 1: Python Introduction

Python Introduction
Python Versions
Python for Data Engineers
Python Architecture

Ch 2: Python Installations

Python Common Line
Anaconda Web
Anaconda Installations
Jupyter Notebooks, IDE

Ch 3: Python Basics

print()
display()
Multi Line display
Debug with AI

Ch 4: Python Variables

Variable Declarations
Variable Rules
Multi Value Variables
Common Value Variables

Ch 5: Python Operators

Athematic, String Operators
Bit, Comparison Operators
Binary Operators
Indexing Operators

Ch 6: Python Data Types

Integer, Float
String Data Types
Type Casting
Type Identification

Ch 7: Python Lists

Python Lists
List Items, Slices
Length & Type
Empty Lists, Append

Ch 8: Python Dictionaries

Indexing Dictionaries
Edit Key Values
Lists inside Dictionaries
Delete & Clear

Ch 9: Python Tuples

Python Tuples
Defining, Indexing
Length(), Type()
Mixed Values in Tuples

Ch 10: Python Control Statements

If..Else conditions
if..elif..else, range
Shorthand if
in & negation operators

Ch 11: Python Loops

For Loop, Range
While Loop
Exit Conditions
Break, Continue, Paas

Ch 12: Python SQL DB Access

SQL DB Access with Python
pyodbc module, sql functions
SQL DB Cursor Connections
Filters, Aggregations with SQL

Ch 13: Dataframe Transformations – 1

Concat & Append
Merge Dataframes
ON and KIND Options
Loops with Dataframes

Ch 14: Dataframe Transformations – 2

Pandas – Cleaning Data
Replace, Transform Columns
Identify & Remove Duplicates
dropna(), fillna() Functions

Ch 15: Dataframe Transformations – 3

date, time Functions
Now() Function in Python
date, time Functions
Calendar Data Generation

Module 3: Databricks, PySpark, Spark, Unity Catalog

Ch 1: Databricks Introduction

Cloud ETL, DWH
Databricks Concepts
ETL, Lakehouse & Big Data
Databricks Workspace UI

Ch 2: Databricks Architecture

Two Plane Architecture
Databricks Runtime (DBR)
Spark Compute Concepts
Databricks APIs

Ch 3: Unity Catalog

Unity Catalog Concepts
Managed & External
Lineage, Data Governance
Genie AI with Unity Catalog

Ch 4: Databricks Operations (GUI)

Catalog & Schema Creations
Volumes, Files & Tables
Catalog Explorer
Need for ETL

Ch 5: Spark SQL

Spark SQL Notebooks
Catalog, Schemas
Tables, DDL Operations
Spark Data Types & Clones

Ch 6: Spark Queries

Data Inserts, Sequences
Spark Joins
Aggregations
Data Analytics

Ch 7: Spark SQL: Functions

Aggregate Functions
Math, Sort Functions
String, DateTime Functions
Window & Null Functions

Ch 8: Spark SQL: Time Travel

Time Travel Concepts
HISTORY, EXTENDED
Versions & Time Stamp
Time Travel Settings

Ch 9: Python Concepts in Databricks

Python Implementations
Print(), display()
Variables, Operators
Dataframes

Ch 10: PySpark Concepts

Python Vs PySpark
PySpark DataFrames
select(), filter()
withColumn()

Ch 11: Medallion Architecture – 1

File Data Sources
Bronze, Silver and Gold
Temp Views, Aggregations
Spark Tables (Parquet)

Ch 12: Medallion Architecture – 2

Azure SQL DB Connections
Joins & Aggregations
Gold Data Consumption
Bronze → Silver → Gold → BI/Analytics

Ch 13: Databricks Delta Lake

Schema Evolution
Delta Table API
MERGE / Upsert Operations
Delta Transaction Log

Ch 14: PySpark: Widgets

Parameters, Text Widgets
dbutils() & get() Functions
Widget Prompts, Edits
UI & JSON for Widgets

Ch 15: Lake Flow Jobs

Workflows & CRON
Job Compute, Running Tasks
Python Script Tasks, Dependencies
Concurrency, Branch Control

Ch 16: PySpark: Auto Loader

Cloud Files & Checkpoints
Data Stream, Schemas
Schema Evaluation Modes
File Ingestions, Rescued Column

Ch 17: LakeFlow Declarative Pipelines

SDP: Spark Declarative Pipelines
Delta LIVE Tables, Data Streams
Bronze, Silver, Gold Data
Data Quality, Materialized Views

Ch 18: Databricks Optimizations

Lazy Evaluation, Data Skew
Data Shuffling, Partitions
Broadcast Joins, Vacuum
Liquid Clusters, Optimize, Z-Order

Ch 19: Databricks Security, AI

ACL: Access Control List
Workspace & Catalog Security
Schema, Volume Security
Table & Column Security

Ch 20: Databricks with Power BI

Access Keys (Tokens)
Server Host, HTTP Path
Spark Connectors
Databricks Connectors

Ch 21: Genie AI

Unity Catalog & Genie AI
AI Components in Databricks
Debugging Controls
Using AI for Notebook Design

Ch 22: GitHub & Asset Bundle

GitHub & GIT
Branches, Asset Bundle
Integrating GIT
Process Integration

Module 4: Realtime Projects for Data Engineering

Real-Time Project 1: HealthCare Domain

Project Requirement:
Solve 20+ Real-World Healthcare Business Requirements.

Project Workflow:
Database Design → SQL → Data Analysis → Performance Optimization

Project Operational Flow:
Healthcare DB → Patients → Doctors → Appointments → Treatments → Billing → Insurance → SQL
Analysis → Stored Procedures → Performance Tuning → Business Reports

Business Requirements:
Monthly Hospital Revenue, Top Doctors By Patients Treated, Repeat Patients, Department Performance,
Average Treatment Cost, Unpaid Bills, Insurance Claim Analysis, Patient Trends, Month-Over-Month
Revenue And Top Procedures…

Real-Time Project 2: Online Travel Data Engineering Platform
Project Objective:
Build an end-to-end Azure Data Engineering solution to process, transform, and analyze Online Travel
business data from multiple sources.

Skills Gained:

👉 Data Ingestion & ETL Development
👉 Azure Data Factory Pipelines
👉 Data Orchestration (End to End)
👉 Data Lake Architecture, RBAC
👉 Real-Time Industry Experience
👉 Azure IoT, Stream Analytics
👉 Azure Databricks with Data Factory

Components For Project:

👉 ADF Pipelines
👉 Databricks Notebooks
👉 Apache Spark Schemas
👉 Monitoring & Alerts
👉 CI/CD Deployment
👉 End to End Integrations
👉 IAM & Managed Identity