#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: Data Analyst Job Roles

Introduction to Data
Data Analyst Job Roles
Data Analyst Challenge
Data and Databases Intro

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..
Special Operators

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

Module 2: Databricks

Ch 1: Databricks Introduction

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

Ch 2: Databricks Architecture

Unity Catalog, Volume
Spark Clusters
Apache Spark and Databricks
Apache Spark Ecosystem
Compute Operations
Hadoop, MapReduce, Apache Spark

Ch 3: Unity Catalog

Unity Catalog Concepts
Workspace Objects
Databricks Notebooks
Databricks Workspace UI
Organizing Workspace Objects
Creating Volumes
Spark Table Creations
UI : Limitations

Ch 4: Unity Catalog Operations, Spark SQL – 1

Spark SQL Notebooks
Creating Catalog
Creating Schemas, Tables
Spark Data Types
Data Partitioning
Managed Tables
SQL Queries with the PySpark API
Union, Views in Spark
Dropping Objects
External Tables, External Volumes
Spark SQL Notebooks: Exports, Clone

Ch 5: SparkSQL Notebooks – 2

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 6: Python Concepts – 1

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

Ch 7: Python Concepts – 2

Python Data Types
Integer / Int Data Types
Float, String Data Types
Arithmetic, Assignment Ops
Comparison Operators
Operator Precedence
If … Else Statement
Short Hand If, OR, AND
ELIF and ELSE IF Statements

Ch 8: Python Concepts – 3

Python Lists
List Items, Indexes
Python Dictionaries
Tables Versus Dictionaries
Python Modules & Pandas
import pandas.DataFrame
Pandas Series, arrays
Indexes, Indexed Lists

Ch 9: PySpark – 1

Dataframes with SQL DB
Pandas Dataframes
Dataframe()
List Values, Mixed Values
spark.read.csv()
spark.read.format()
Filtering DataFrames
Grouping your DataFrame
Pivot your DataFrame

Ch 10: PySpark – 2

DataFrameReader
DataFrameWriter Methods
CSV Data into a DataFrame
Reading Single Files
Reading Multiple Files
Schema with an SQL String
Schema Programmatically

Ch 11: PySpark – 3

Writing DataFrames to CSV
Working with JSON
Working with ORC
Working with Parquet
Working with Delta Lake
Rendering your DataFrame
Creating DataFrames from Python Data Structures

Ch 12: PySpark Transformations – 1

Data Preparation
Selecting Columns
Column Transformations
Renaming Columns
Changing Data Types
select() and selectExpr()
Column Transformations
withColumn()

Ch 13: PySpark Transformations – 2

Basic Arithmetic and Math Functions
String Functions
Datetime Conversions
Date and Time Functions
Joining DataFrames
Unioning DataFrames
Joining DataFrames

Ch 14: PySpark Transformations – 3

Filtering DataFrame Records
Removing Duplicate Records
Sorting and Limiting Records
Filtering Null Values
Grouping and Aggregating
Pivoting and Unpivoting
Conditional Expressions

Ch 15: Medallion Architecture

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

Ch 16: Delta Lake – 1

Storage Layer
Delta Table API
Deleting Records
Updating Records
Merging Records
History and Time Travel

Ch 17: Delta Lake – 2 (SCD)

Schema Evolution
Delta Lake Data Files
Deleting and Updating Records
Merge Into
Table Utility Commands
Exploratory Data Analysis
Incremental Loads
Old History Retention
Delta Transaction Log

Ch 18: Widgets

Text Widgets
User Parameters
Manual Executions
Lake Bridge
Databricks BridgeOne

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

How Spark Optimizes your Code
Lazy Evaluation
Explain Plan
Inspecting Query Performance
Caching, Data Shuffling
Broadcast Joins
When to Partition
Data Skipping
Z Ordering
Liquid Clustering
Spark Configurations

Ch 21: 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 22: Spark Structured Streaming

Streaming Simulator Notebook
Micro-batch Size
Schema Inference and Evolution
Time Based Aggregations and Watermarking
Writing Streams
Trigger Intervals
Delta Table Streaming Reads and Writes

Ch 23: Auto Loader

Reading Streams with Auto Loader
Reading a Data Stream
Manually Cancel your Data Streams
Writing to a Data Stream
Workspace Modules

Ch 24: Lake Flow Declarative Pipelines

Delta LIVE Tables
Data Generator Notebook
Pipeline Clusters
Databricks CLI
Data Quality Checks
Streaming Dataset “Simulator”
Streaming Live Tables

Ch 25: Security: ACLs

Overview of ACLs
Adding a New User to our Workspace
Workspace Access Control
Cluster Access Control
Groups

Ch 26: Realtime Project @ Ecommerce / Banking / Sales

Detailed Project Requirements
Project Solutions
Project FAQs
Project Flow
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.