#Databricks Developer

A Databricks Developer is a highly promising job role focused on building scalable data pipelines, transforming raw data into meaningful insights, and enabling advanced analytics with PySpark, Delta Lake, Lakehouse, Workflows, Auto Loader, Big Data and more.. !

✅ Delta Lake, DLT & Auto Loader
✅ Spark, Spark SQL & PySpark
✅ Python, ETL & Pandas
✅ Big Data Analytics
✅ ETL/ELT
✅ WorkFlows, Widgets, Pipelines
✅ Cloud BI, Medallion, more..
✅ Real-Time Projects, Resume

Module 1: SQL Server TSQL (MS SQL) Queries

Ch 1: Databricks Job Roles

Introduction to Data
Data Analyst Job Roles
Data Analyst Job Roles

Ch 2: Database Intro & Installations

Database Types (OLTP, DWH, ..)
DBMS: Basics
SQL Server 2025 Installations
SSMS Tool Installation
Server Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

Creating Databases (GUI)
Creating Tables, Columns (GUI)
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, Add, modify, etc..
DML: Insert, Update, Delete, select into, etc..
DQL: Fetch, Insert… Select, etc..
SQL Operations: LIKE, BETWEEN, IN, etc..

Ch 5: Data Types

Integer Data Types
Character, MAX Data Types
Decimal & Money Data Types
Boolean & Binary Data Types
Date and Time Data Types
SQL_Variant Type, Variables

Ch 6: Excel Data Imports

Data Imports with Excel
SQL Native Client
Order By: Asc, Desc
Order By with WHERE
TOP & OFFSET
UNION, UNION ALL

Ch 7: Schemas & Batches

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

Ch 8: Constraints, Keys & RDBMS – Level 1

Null, Not Null Constraints
Unique Key Constraint
Primary Key Constraint
Foreign Key & References
Default Constraint & Usage
DB Diagrams & ER Models

Ch 9: Normal Forms & RDBMS – Level 2

Normal Forms: 1 NF, 2 NF
3 NF, BCNF and 4 NF
Adding PK to Tables
Adding FK to Tables
Cascading Keys
Self Referencing Keys
Database Diagrams

Ch 10: Joins & Queries

Joins: Table Comparisons
Inner Joins & Matching Data
Outer Joins: LEFT, RIGHT
Full Outer Joins & Aliases
Cross Join & Table Combination
Joining more than 2 tables

Ch 11: Views & RLS

Views: Realtime Usage
Storing SELECT in Views
DML, SELECT with Views
RLS: Row Level Security
WITH CHECK OPTION
Important System Views

Ch 12: Stored Procedures

Stored Procedures: Realtime Use
Parameters Concept with SPs
Procedures with SELECT
System Stored Procedures
Metadata Access with SPs
SP Recompilations
Stored Procedures, Tuning

Ch 13: User Defined Functions

Using Functions in MSSQL
Scalar Functions in Real-world
Inline & Multiline Functions
Parameterized Queries
Date & Time Functions
String Functions & Queries
Aggregated Functions & Usage

Ch 14: Triggers & Automations

Need for Triggers in Real-world
DDL & DML Triggers
For / After Triggers
Instead Of Triggers
Memory Tables with Triggers
Disabling DMLs & Triggers

Ch 15: Transactions & ACID

Transaction Concepts in OLTP
Auto Commit Transaction
Explicit Transactions
COMMIT, ROLLBACK
Checkpoint & Logging
Lock Hints & Query Blockin
READPAST, LOCKHINT

Ch 16: CTEs & Tuning

Common Table Expression
Creating and Using CTEs
CTEs, In-Memory Processing
Using CTEs for DML Operations
Using CTEs for Tuning
CTEs: Duplicate Row Deletion

Ch 17: Indexes Basics, Tuning

Indexes & Tuning
Clustered Index, Primary Key
Non Clustered Index & Unique
Creating Indexes Manually
Composite Keys, Query Optimizer
Composite Indexes & Usage

Ch 18: Group By Queries

Group By, Distinct Keywords
GROUP BY, HAVING
Cube( ) and Rollup( )
Sub Totals & Grand Totals
Grouping( ) & Usage
Group By with UNION
Group By with UNION ALL

Ch 19: Joins with Group By

Joins with Group By
3 Table, 4 Table Joins
Join Queries with Aliases
Join Queries & WHERE
Join Queries & Group By
Joins with Sub Queries
Query Execution Order

Ch 20: Sub Queries

Sub Queries Concept
Sub Queries & Aggregations
Joins with Sub Queries
Sub Queries with Aliases
Sub Queries, Joins, Where
Correlated Queries

Ch 21: Cursors & Fetch

Cursors: Realtime Usage
Local & Global Cursors
Scroll & Forward Only Cursors
Static & Dynamic Cursors
Fetch, Absolute Cursors

Ch 22: Window Functions, CASE

IIF Function and Usage
CASE Statement Usage
Window Functions (Rank)
Row_Number( )
Rank( ), DenseRank( )
Partition By & Order By

Ch 23: Merge(Upsert) & CASE, IIF

Merge Statement
Upsert Operations with Merge
Matched and Not Matched
IIF & CASE Statements
Merge Statement inside SPs
Merge with OLTP & DWH

Ch 24: Temporary Tables

TempDB: Realtime Use
Local Temporary Tables
Global Temporary Tables
Testing & Using Temp Tables
Temp Tables across Sessions
Auto CreateTables using Select
SELECT .. INTO & Bulk Loads

Ch 25: Cursors & Fetch

Cursors: Realtime Usage
Fetching & Paginations
Identify Nth Row of a table
3rd Highest and Row Filters
Cursors Declaration
Cursors Types
Close, Deallocations

Module 2: TSQL Programming

Ch 1: Variables & Try..Catch

Variables: Declaration & Usage
Assigning Values to Variables
SELECT & SET Operations
Using Variables in SPs
Variables Versus Parameters
Try.. Catch Block with Variables
THROW Statement, Error Handling

Ch 2: Updatable Views

Using Triggers with Views
Updatable Views, DML
Views & Stored Procedures
Data Distributions in Tables
Transactions with Procedures
Conditional Commits in SPs
Rollback Options in Realtime

Ch 3: Stored Procedures & TVPs

Using TVP with Procedures
Creating User Defined Types
Big Data Copy & Transactions
Using SPs & Table Variables
Transactional Integrity with SPs
Conditional Commits, Rollbacks
Procedure Recompilations

Ch 4: SPs & Recursive CTEs

CTEs: Common Table Expression
CTEs For DML Operations
Defining Recursive CTEs
Anchor Element: Realtime Use
Termination Checks and Loops
Defining SPs with CTEs
Cautions with Recursive CTEs

Ch 5: Functions & Loops

Inline, Table Line Functions
Multi Line Table Functions
Using LOOPs in Functions
Variables & Return Values
Table Generation Logic
Date & Time Data Types
Calendar Data Generations

Ch 6: PIVOT, UNPIVOT

Reading Denormalized Data
Normalizing Table Data
PIVOT Operation with TSQL
PIVOT with Aggregates
FOR and IN Operators
UNPIVOT with TSQL
PIVOT with Functions, SPs

Ch 7: Server Architecture

Database Engine Components
Parser, Compiler & Optimizer
Protocols and Query Processing
MDAC and CLR Components
Parsing and Compilation
Memory Manager & IO Managers
SQL OS Components, MDAC

Ch 8: DB Architecture (VLDB)

Planning Large Databases
Primary, Secondary Data Files
Filegroups, Spacing and Sizing
Log File: Usage and Precautions
Creating Tables with Filegroups
Pages and Extents for Storage
VLF, MiniLSN & Checkpoint

Module 3: Python

Ch 1: Python Introduction

Python Introduction
Python Versions
Python for Professionals

Ch 2: Python Architecture

Python Architecture
PVM: Python Virtual Machine
Execution Process
Python Implementations

Ch 3: Python Installations

Python Introduction
Python Installations
Anaconda Installation
Python IDE & Usage
Jupyter Notebooks

Ch 4: Python Print Statement

Python Print Statement
print(), print()
Single Line print()
Multi Line print()
print() with single quotations

Ch 5: Python Variables

Python Variables
Purpose & Rules
Variable Value Reads
Multiple Variables & Print()

Ch 6: Python Operators

Athematic *& Multiplier Operators
Python String Literals
Single, Double Quotes
Format Strings (f string)
Comparison, Indexing Operators

Ch 7: Python Data Types

Python Data Types
Integer, Float, String Data Types
Type Casting, Type Identification
Multi Value Assignments
Python Built-In Classes (data types)

Ch 8: Python Lists

Creating Python Lists
Printing List Items
Print List Slices
Length & Type
list() method
Empty Lists, Append
Loops, List Updates

Ch 9: Python Dictionaries

Python Dictionary
Creating, Indexing Dictionaries
Edit / Overwrite Key Values
Lists inside Dictionaries
Delete & Clear

Ch 10: Python Tuples

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

Ch 11: Python IF..ELSE Condition

If..Else conditions
if..elif..else & Shorthand if
composite conditions
Indent, pass statement
in & negation operators
range conditions

Ch 12: Python Loops (For)

Python For Loop
For Loop @ Range
For Loop @ Sequence Values
Nested Loops
Loop Control Statements
Break, Continue, Paas

Ch 13: Python Loops (While)

While Loop
Termination Checks (Expressions)
Variables, Logical Conditions
Loop Conditions, Operators
Exit Conditions
iter() and Looping Options

Ch 14: Python Dataframes

Dataframes: Creation
Pandas Dataframes
Dataframes From Single List
Dataframes from Dictionary
Display Dataframes, List Items
Identify, Replace Nulls, NumPy

Ch 15: Python SQL DB Access

SQL DB Access with Python
import pandas.DataFrame
pyodbc module, sql functions
SQL DB Cursor Connections
SQL Query Executions: DDL, DML
Filters, Aggregations with SQL
Dataframe Usage with SQL

Ch 16: Dataframe Transformations – 1

Dataframe Transformations
Concat & Append
Merge Function
Join with Multiple Dataframes
Indexing Operations
Data Type Checks, Conversions
Loops with Dataframes

Ch 17: Dataframe Transformations – 2

Pandas – Cleaning Data
Replace, Transform Columns
Data Discovery & Column Fill
Identify & Remove Duplicates
dropna(), fillna() Functions
Data Plotting & matlib Lib

Ch 18: Python Functions & Lambda

Python Functions & Usage
Function Parameters
Default & List Parameters
Python Lambda Functions
Recursive Functions, Usage
Return & Print @ Lamdba

Ch 19: Python File Handling

File Handling, Activities
Loop, Write, Close Files
Appending, Overwriting
import os, path.exists
f.open, f.write
f.read, f.close

Ch 20: Python Modules

Import Python Modules
Built In Modules & dir
datetime module in Python
Date Objections Creation
strftime Method & Usage
imports & datetime.now()

Ch 21: Python User Inputs & TRY

Try Except, Exception Handling
Raise an exception method
TypeError, Scripting in Python
Python User Inputs
Python Index Numbers
input() & raw_input()

Ch 22: Python Dictionary

Dictionary Creation, Use
Hashing, Copy, Update
Deletion, Sorting
Len(), Inbuilt Functions
Cmp() List Method
Python Dictionary Str(dict)
Programming Concepts
Loops and Sets

Ch 23: Python Packages

Package in Python
Creating a package
Package Imports, Modules
Sub Packages Creation
Sub Package Imports
Popular Packages in Python
NumPy & SciPy
Libraries in Python
Seaborn, Fameworks

Ch 24: Exception Handling

Shell Script Commands
OS operations in Python
File System Shell Methods
os – math – cmd -csv – random
Numpy (numerical python)
Pandas – sys – Matplotlib
Common RunTime Errors
Exception Handlin
Try…Except…else, Try…finally

Ch 25: Python Class & Objects

Class variables, Instances
Built in Class Attributes
Objects – Constructors
Modifiers – Self Variable
Python Garbage Collections
Hierarchical Inheritance
Multilevel, Multiple, Hybrid
Overloading & OverRiding
Polymorphism– Abstraction

Ch 26: Regular Expressions

Regular Expression
Regular Expression Patterns
Literals – Repetition Cases
Groups and Grouping
w+ and ^ , \s Expressions
re.split function
Regular expression methods
re.match() in Regular Expr
re.search(), re.findall for Text

Ch 27: Multi-Threading

Python Multi-Threading
Thread Synchronization
Python Gil & Programming
Thread Control Block (TCB)
Stack Pointers & App Usage
Program Counters in Realtime
Thread State Concept
Python Exception Handling

Ch 28: Python TKinter

Tkinter GUI Program
Components & Events
Adding Controls in Tkinter
Radio & Check Buttons
Tkinter Forms in Realtime
List Boxes, Menu, ComboBox
Mainloop () & Functions

Ch 29: Python Web & IoT Intro

Python Web Frameworks
Django: Advantages
Web Framework
MVC and MVT – Django
Web Pages using python
HTML5, CSS3 usage
PYTHON Bottle & Pyramid
Falcon; smart_open in python

Module 4: Databricks

Ch 1: Databricks Introduction

Cloud ETL, DWH
Cloud Computing
Databricks Concepts
Databricks Advantages
Databricks Key Features
Big Data in Cloud
Databricks Account

Ch 2: Databricks Architecture

Unified Cloud Platform
Unity Catalog
Apache Spark
LakeHouse (Cloud)
Volumes, Files & Tables
Control Pane, Compute Pane
Deployment Modes
Cloud Providers: Azure/AWS/Google
Azure Cloud: Advantages
Databricks Runtime (DBR)
RDD & DAG Components
Databricks One: Hadoop, Map Reduce

Ch 3: Spark Cluster Architecture (Cloud Computing)

Spark Components
Apache Spark Clusters
Cloud Computing Concepts
Classic Cluster Types
Serverless Clusters
Compute Operations
Apache Spark Ecosystem
Drive Node, Worker Node
Cluster Manager & Executors

Ch 4: Unity Catalog

Unity Catalog Concepts
Region, Properties
Databricks Workspace UI
Organizing Workspace Objects
File Uploads
Spark Table Creations
Creating Volumes
UI: Limitations

Ch 5: Spark SQL – 1

Spark SQL Notebooks
Creating Schemas, Tables
Spark Data Types
Data Partitioning
Managed Tables
SQL Queries with the PySpark API
Union, Views in Spark
Dropping Objects

Ch 6: Spark SQL – 2

Spark Joins
Aggregations
Math, Sort Functions
String, DateTime Functions
Conditional Statements
SQL Expressions with expr()
Spark SQL Aggregations

Ch 7: Spark SQL – 3

Spark Time Travel
Data Recovery & Undo
Version Number
Describe
Describe Extented
TimeStamp As Of Concept

Ch 8: Python Intro & Print

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

Ch 9: Python Variables

Defining Variables
Using Variables
Printing Variables
Display Variables
Variable Types
Multi Value Variables
Multi Value Assigning
If … Else Statement

Ch 10: Python Operators

Integer Operators
String Operators
Arithmetic Operators
Assignment Operators
Comparison Operators
Formatted Strings
Indexing Operators
Short Hand If, OR, AND
ELIF and ELSE IF Statements

Ch 11: Python Data Types

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

Ch 12: Python Dataframes

Pandas Module (Python)
Dataframes from Lists
Dataframe from Dict
Pandas Dataframes
Dataframe print, display
Dataframe from Files
spark.read.csv()
spark.read.format()

Ch 13: Medallion Architecture

Understanding Medallion Concepts
Bronze, Gold and Silver
Raw Data
Data Preparation (Prepping)
Temporary Views
Aggregated Data Flow
Big Data Analytics

Ch 14: PySpark: Medallion Loads – 1

Reading from Volumes
Dataframes, Temp Views
Data Prep (Silver)
Filtering DataFrame Records
Removing Duplicate Records
Sorting and Limiting Records
Spark SQL Dataframes
Gold Layer Implementation
Testing Aggregated Loads

Ch 15: PySpark: Medallion Loads – 2

Azure SQL DB Connections
JDBC & Credentials
SQL Queries in PySpark
Data Prep (Silver)
Filtering Null Values
Grouping and Aggregating
Spark SQL Dataframes
Gold Layer Implementation
Testing Aggregated Loads

Ch 16: PySpark: Delta Tables

Delta Tables (Spark)
Parquet Versus Delta
Deleting and Updating Records
Table Utility Commands
Delta Transaction Log

Ch 17: PySpark: SCD

Slowly Changing Dimension
Parquet Versus Delta
Deleting and Updating Records
Table Utility Commands
Merge Into Statement
Incremental Loads
Merge with OLTP Data Sources
Merge Temp Views & Spark Table

Ch 18: PySpark: Widgets

Need for Widgets
Text Widget
User Parameters
Manual Executions
Parameters & JSON

Ch 19: Lake Flow Jobs

Worksflows & 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: Databricks Tuning

OPTIMIZE
VACUUM
Lazy Evaluation
Caching, Data Shuffling
Broadcast Joins
When to Partition
Data Skipping
Z Ordering
Liquid Clustering
Spark Configurations

Ch 21: Databricks Security

Databricks Security
MFA (Multi Factor Authentication)
IAM (Identity & Access Management)
ACL Concepts
Workspace Users & Groups
Workspace Security
Notebook Security
Job Security
Cluster Access Control

Ch 22: Auto Loader – 1

File Incremental Loads
Cloud Files
Cloud File Processing
Checkpoint Files
Creating Directories in Volumes
Reading Streams with Auto Loader
Workspace Modules
Testing Auto Loader (Initial Loads)

Ch 23: Auto Loader – 2

Metadata & WithColumns
Schema Evolution
addNewColumns
Rescue
FailOnNewColumns
Writing to a Data Stream
Testing Auto Loader (Incremental Loads)

Ch 24: Delta Lake & SDP (LakeFlow)

Delta Lake Concepts
Lakeflow SDP
Declarative Pipelines
Streaming Tables
CDC: Change Data Capture
Bronze Tables
Silver Tables, Timestamp
Gold Tables
Big Data Analytics
SDP (Spark Data Pipelines)
Exploratory Data Analysis

Ch 25: Version Control & GitHub

Local Development
Runtime Compatibility
Git and GitHub Pre-requisites
Git and GitHub Basics
Linking GitHub and Databricks
Databricks Git Folders
Project Code to GitHub
Adding Modules to the Project Code
Databricks Job Updates, Runs

Ch 26: Realtime Project @ Ecommerce / Banking / Sales

Detailed Project Requirements
Project Solutions
Project FAQs
Project Flow
LakeBridge
Interview Questions & Answers
Resume Guidance (1:1)

Brochure Download Request a Callback

Databricks Developer skills including Delta Lake Spark SQL PySpark and Auto Loader

#DataLake #DeltaLake #AutoLoader #DLTTables #Medallion #Workflows #Widgets #DBFS #Spark #SparkSQL #PySpark #SparkCluster #CloudComputing #DatabricksCertifications #DatabricksAssociateEngineer #DatabricksDeveloper

What is the Databricks Developer course and who is it for?

The Databricks Developer course is a complete, job-oriented training program designed for aspiring Data Engineers, Big Data professionals, Python/SQL developers, and anyone planning a career in Databricks or Lakehouse-based ETL systems.

What are the prerequisites to enroll in this course?

No prior programming experience is required. Basic SQL knowledge helps, but the course teaches SQL and Python from the fundamentals before moving into advanced Databricks concepts.

What key skills will I learn in this Databricks Developer training?

You will learn SQL Server (T-SQL), Python for ETL, Databricks Workspace, Spark SQL, PySpark, Delta Lake, Delta Live Tables, Auto Loader, Unity Catalog, Workflows, Streaming, Lakehouse Architecture, and complete real-time project pipelines.

Does the course include real-time projects and hands-on labs?

Yes. The course is 100% hands-on and includes real-time projects in domains such as e-commerce, banking, and sales, with end-to-end ETL pipelines, CI/CD, Delta Lake, and Medallion architecture.

What roles can I apply for after completing the Databricks Developer course?

You can apply for roles like Databricks Developer, Data Engineer, PySpark Developer, ETL Engineer, Azure/AWS Data Engineer, and Lakehouse Engineer.

What are the main modules covered in the course?

The course consists of three modules:
Module 1 – SQL Server & T-SQL
Module 2 – Python for ETL
Module 3 – Databricks (Spark, Delta, Streaming, DLT, UC, Pipelines)

What Databricks technologies will I learn (Delta, DLT, Auto Loader, PySpark)?

You will learn Delta Lake, Delta Live Tables (DLT), Auto Loader, Spark SQL, PySpark, Unity Catalog, Streaming, Workflows, Lake Flow, and Performance Tuning.

How deeply is SQL and Python taught in this program?

SQL is taught from basics to advanced (Joins, Views, SPs, Triggers, CTEs, Window Functions, Merge, Indexing). Python covers data types, functions, loops, exceptions, file handling, Pandas, and ETL transformations.

Does the course include Databricks Workspace, Unity Catalog & Admin concepts?

Yes. You will learn Workspace objects, Unity Catalog Admin, metastore, system tables, securable objects, catalog-schema-volume creation, and access control lists.

Is Databricks Streaming, Auto Loader & Delta Streaming included?

Yes. Structured Streaming, Auto Loader, micro-batch, watermarking, Delta streaming reads/writes, and live streaming pipelines are included.

Is the training 100% hands-on with daily tasks?

Yes. The training includes daily assignments, weekly mock interviews, real-time projects, and end-to-end pipeline tasks.

Will I get access to Databricks Workspace, datasets & notebooks for practice?

Yes. Students receive full access to Databricks Workspace, practice notebooks, datasets, and all required implementation files.

Do I get resume preparation, interview guidance, and mock interviews?

Yes. You get 1:1 resume building, weekly mock interviews, project-based interview questions, and career support until placement.

Do you provide certification guidance for Databricks Data Engineer exams?

Yes. You will receive preparation support for Databricks Certified Data Engineer Associate and Professional certifications.

What is the demand for Databricks Developers in the job market?

Databricks is one of the fastest-growing platforms in the Data Engineering ecosystem, and companies worldwide are rapidly adopting the Lakehouse architecture, creating high demand for skilled developers.

What is the average salary of a Databricks Developer (India / Abroad)?

In India, salaries range from ₹8 LPA to ₹22 LPA. In the USA, salaries range from $95,000 to $170,000 depending on experience and cloud expertise.

Is Spark mandatory to learn Databricks?

Yes, Spark is the core engine behind Databricks. However, the course teaches Spark SQL and PySpark from scratch, so beginners can learn smoothly.

How is Databricks different from Azure Data Factory or Synapse?

Azure Data Factory is an orchestration tool, Synapse is a warehouse/analytics platform, while Databricks is a unified Lakehouse Platform for ETL, ML, BI, and high-performance data pipelines.

Is this course suitable for freshers and non-IT learners?

Yes. The program starts from zero level and gradually moves to advanced ETL and Lakehouse topics, making it suitable for freshers, working professionals, and career switchers.

How do I enroll or attend the Free Demo session?

You can join by contacting the team at +91 96664 40801 or visiting www.sqlschool.com to register for a free live demo session.

What training modes are available for the Databricks Developer course?

The course is available in multiple flexible modes including Live Online Instructor-Led Training, Classroom Training at our institute, and Self-Paced Video Learning. You can choose the mode that best fits your schedule and learning style.

What makes this Databricks training different from other institutes?

This program is taught by real-time industry experts with 20+ years of experience, includes 100% hands-on labs, daily tasks, weekly assessments, real-time case studies, resume building, mock interviews, and complete placement assistance—ensuring practical learning and job readiness.