#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.

✅ 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
✅ 1:1 Mentorship, Resume

Databricks Data Engineer Training Contents:

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Module 1 : Microsoft SQL (TSQL)

Ch 1: Database Intro & Job Roles

Database Introduction
Database Types: OLTP, DWH
DBMS & Realtime Use
DBMS Software & Purpose
SQL : Purpose & Use
SQL Server Versions, Editions
Job Roles & Responsibilities

Ch 2: SQL Server Installations

SQL Server 2022 Installations
SQL Server 2019 Installations
SSMS Tool Installation
Server Connections, Properties
Instance & Instance Types
Authentication Types
System Databases & Purpose

Ch 3: SQL Basics V1 (Commands)

Database, Tables & Columns
SQL Basics: Purpose
DDL Statements
DML Statements
DQL Statements
Verifications @ GUI
Basic SELECT Queries

Ch 4: SQL Basics V2 (Operators)

DDL Variants in MSSQL
DML Variants in MSSQL
INSERT & INSERT INTO
SELECT & SELECT INTO
Basic Operators in SQL
Special Operators in MSSQL
ALTER, TRUNCATE, DROP

Ch 5: Excel Data Imports

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

Ch 6: Schemas & Security

Schemas: Creation, Usage
Schemas & Table Grouping
Using Default Schema
Real-world Banking Database
Table Migrations @ Schemas
2 Part, 3 Part & 4 Part Naming
Verifying Schemas in UI

Ch 7: Constraints & Keys Basics

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

Ch 8: Indexes Basics, Tuning

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

Realtime Case Study 1

Ch 9: Joins Basics

Joins: Table Comaparisons
Inner Joins & Matching Data
Outer Joins: LEFT, RIGHT
Full Outer Joins & Audits
Cross Joins & Table Combinations
Joining more than 2 tables
Joining Tables with Aliases

Ch 10: Views & RLS

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

Ch 11: 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 12: User Defined Functions

Using Functions in MSSQL
Scalar Value Functions
Inline & Multiline Functions
Parameterized Queries
Date & Time Functions
String Functions & Queries
Aggregated Functions & Usage

Ch 13: Triggers & Automations

Need for Triggers
DDL & DML Triggers
For / After Triggers
Instead Of Triggers
Memory Tables with Triggers
Data Replication, Automation
Disabling DMLs & Triggers

Ch 14: Transactions & ACID

Transaction Concepts in OLTP
Transaction Types in Realtime
Auto Commit, Explicit Transaction
COMMIT, ROLLBACK
Checkpoint & Logging
Lock Hints & Query Blocking
READPAST, LOCKHINT

Ch 15: Cursors & Fetch

Cursors: Realtime Usage
Cursor Declaration Types
Open Cursor, Close Cursor
Local & Global Cursors
Scroll & Forward Only Cursors
Static & Dynamic Cursors
Fetch, Absolute Cursors

Ch 16: CTEs & Tuning

CTE: Common Table Expression
Creating and Using CTEs
CTEs and In-Memory Processing
Using CTEs for DML Operations
Using CTEs for Data Retrieval
Using CTEs for Tuning
CTEs For Duplicate Row Deletion

Realtime Case Study 2

Ch 17: Relations, Normal Forms

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

Ch 18: Self Joins, EXISTS

Joining same table
Correlated Queries
Joining Tables, Queries
Self Joins with WHERE
Self Joins with UNION
Self Joins with Order By
Self Joins with Views

Ch 19: Remote Joins

Working with Multiple Servers
Multi Server Access from SSMS
Linked Servers Creation, Tests
4 Part Naming Convention
Remote Data Access
RPC & RPC OUT
Remote Joins & Data Analysis

Ch 20: Sub Queries

Sub Queries Concept
Sub Queries & Aggregations
Joins with Sub Queries
Sub Queries with Aliases
Sub Queries with OrderBy
Sub Queries with WHERE
Sub Queries, Joins, Where

Ch 21: 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 22: 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 23: Data Types & Conversions

Integer Data Types
Character, MAX Data Types
Decimal & Money Data Types
Boolean & Binary Data Types
Date and Time Data Types
Table, SQL_Variant Types
Cast( ) and Convert( ) Functions

Ch 24: Window Functions, CASE

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

Realtime Case Study 3

Module 2: Python Analytics

Ch 1. Python Introduction

Need for Data Analytics
Python in Data Analysis
History of Python
Python Versions
Python Implementations
Python Installations
Python IDE & Usage
Jupyter Notebooks

Ch 2. Python Basics, Architecture

Python Scripting Options
Basic Operations in Python
Python Scripts, Print()
Single, Multiline Statements
Adding Cells, Saving Notebook
Single, Multi Line Comments
Python : Internal Architecture
Compiler Versus Interpreter

Ch 3. Data Types & Variables

Integer / Int Data Types
Float & String Data Types
Boolean, Binary Types
Sequence Types: List, Tuple
Range, Complex & memview
Retrieving Data Type: type()
Multi Assignments & Casting
Unpack Collection, Outputs

Ch 4. Python Operators

Arithmetic, Assignment Ops
Comparison Operators
Logical, Identity Operators
Member, Bitwise Operators
Operator Precedence
If … Else Statement, Pass
Short Hand If, OR, AND
ELIF and ELSE IF Statements
Expressions, Ternary OPs

Ch 5: Python Loops, Iterations

Python Loop & Realtime Use
Python While Loop Statement
Break and Continue Statement
Using Print with While()
Iterations & Conditions
Exit Conditions & For Loops
Break, Continue & Range
__iter__() and __next__()
iter() and Looping Options

Ch 6: Python Collections

Python Collections (Arrays)
list() Constructor, print()
Python Tuples, Tuple Items
tuple() Constructor, Usage
Python Sets : Syntax Rules
Duplicates, Types, Ordered
Python Dictionaries: Usage
Changeable, Ordered Data
Dictionary Construct, type()

Ch 7: Python Functions

Python Functions & Usage
Function Parameters
Arguments, **kwargs
Default & List Parameters
Python Lambda Functions
Anonymous Functions
Recursive Functions, Usage
Return & Print @ Lamdba

Ch 8: Python Classes & Arrays

Python Classes & Objects
__init__() Function
__str__() Function
Self Parameters & Objects
Python Inheritance & Classes
Parent & Child Classes
__init__() & super() Function
Polymorphism in Python

Ch 9: Python Modules

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

Ch 10: : Python JSON & RegEx

JSON Concepts, Usage
Dictionary & import json
Python Objects into JSON
Formatting & Ordering
json.dumps, print options
Python Regular Expressions
RegEx Module & Function
search() & span() , Strings
Using RegEx with JSON

Ch 11: Python User Inputs & TRY

Try Except, Exception Handling
NameError Resolution
Python Finally Block, Usage
Raise an exception method
TypeError, Scripting in Python
Python User Inputs
Python Index Numbers
Named Indexes, Usage
input() & raw_input()

Ch 12: Python File Handling

File Handling, Activities
r, a, w, x modes
t, b Operations
Read Only Parts
Loop, Write, Close Files
Appending, Overwriting
import os, path.exists
f.open, f.write
f.read, f.close

Ch 13: Data Analytics – Pandas

Python Modules & Pandas
Pandas Codebase & Usage
Installation of Pandas
import pandas.DataFrame
Checking Pandas Version
Pandas Series, arrays
Labels : Creation, Use
series(), print()

Ch 14: Data Analytics – DataFrames

Indexes & Named Options
Locate Row and Load Rows
Row Index & Index Lists
Load Files Into a DataFrame
pd.read_csv() Function
pd.options.display.max_rows
df.to_string() Function
tail() & null() Function

Ch 15: Data Analytics – Pandas

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

Ch 16: SQL DB & Python – 1

SQL & Databases
Azure Data Studio Tool
sp_execute_external_script
Input Data & Result Sets
DDL & DML with Python
SQL_out, SQL_in
Variables & Parameters
Versions, Package List
WITH RESULT SETS Options

Ch 17: SQL & Python – 2

pandas.Series with SQL DBs
Indexing Methods in Realtime
Convert series to data frame
Output values into data.frame
pymssql package in SQL Server
pip list & Package Manager
Python runtime, Py Package
pymssql.connect & Usage
Cursor Variables & Usage

Ch 18: Realtime Case Study

Module 3: Databricks (ETL, DWH)

Ch 1: Databricks Intro

Azure Introduction
Azure Account & Subscription
Open Source ETL : Spark
Azure Databricks Resource
Databricks Workspace
Creating Spark Cluster

Ch 2: Spark Architecture

Spark Clusters: Types, Policies
Driver Node: Purpose, Compute
Worker Node: Purpose, Compute
Cluster Manager, Executions
Resilent Distributed Datasets
DAG: Directed Acyclic Graph

Ch 3: DBFS Operations

DBFS Concepts: File Store, Tables
DBFS File Uploads, Infer Schema
Header Row Promotion
Create Table using UI
HIVE Metastore Catalog
Spark Database & Tables

Ch 4: Notebooks Intro

ETL & ELT Process
Workspace Options: Notebooks
Notebooks: SQL, Python, Scala
When to use which Notebooks?
Notebook Exports, Imports
Cloning and Markdown Cells

Ch 5: Unity Catalog

Unity Catalog & Big Data Storage
Unity Catalog Connectors
Catalog Explorer, HIVE Metastore
Ubuntu VM : Azure Resource
Cluster Size & VM Size Options
Default Spark Database, Usage

Ch 6: Spark SQL Notebooks

Creating Spark Databases
Connecting to Spark Databases
Creating Spark tables
Data Inserts & DML Operations
DDL Operations on Spark Tables
SQL Notebook: Limitations

Ch 7: Python Introduction

Python Introduction
Python Usage in ETL, DDL, DML
Dataframes : Purpose
Dataframes as Spreadsheets
Spark Environment for Python
PySpark: Python inside Spark

Ch 8: Python Notebooks

DBFS File Source & DataFrames
Creating Temp View
Dataframe Loads to TempView
Data Filters in Temp View
Data Aggregations in Temp View
Creating Parquet Tables

Ch 9: Azure SQL Reads

Azure SQL DB Connections
Azure SQL Server & DB Names
Connection String & URL Format
Dataframes @ spark.read.jdbc()
Aggregated / Incremental Tfns
Data Loads into Spark Database

Ch 10: Azure SQL Writes

DBFS FileStore into Dataframes
SQL Database Connections
Filters, Aggregations with DBFS
Azure SQL DB Connections
Dataframes To write.jdbc()
Dataframes to Azure SQL DB

Ch 11: Medallion Architecture

Medallion Architecture: Scaling
Raw Data with Medallion
Transformations (ETL)
Bronze Layer : Raw Data
Silver Layer with Temp Views
Gold Layer with Spark Tables

Ch 12: PySpark Transformations

Custom DataFrames
Single List, Mixed List Options
Concat Function & Index Options
Removing Empty Rows
Replacing Null Values
Merge, Joins, Join Kind

Ch 13: Delta Tables (PySpark)

Delta Tables : Upsert Activity
Creating Delta Tables
DML Operations in Delta Tables
Upsert: Incremental Loads
Delta Tables in HIVE Metastore
MERGE INTO Statement (Spark)

Ch 14: Python Widgets (PySpark)

Widgets: Notebook Parameters
dbutils.widgets.text()
dbuitls.widget.get()
Reading Widgets into Variables
Using Variables in Notebook
Aggregated Loads with Widgets

Ch 15: Workflows (PySpark)

Python Notebook Schedules
Adding Tasks to Jobs
Job Clusters & Cluster Sizes
High Performance Cluster
Unlimited Clusters
Job Notifications, Verifications

Ch 16: Security

IAM : Creating AD Users, Groups
RBAC Concepts: IAM Roles
Databricks Resource Security
Databricks Workspace Security
Notebook Job Level Security
Job Level Security, Sharing

Ch 17: Spark Data Analytics

Access Tokens & API Access
JDBC Connections: Server Host
HTTP Path & Port: Server URL
Power BI Desktop : Get Data
Spark Cluster Connections
Data Access & Test Connection

Ch 18: Databricks Tuning

Databricks Tuning: Caching
Job Clusters & Cloud Computing
Photon Acceleration
Spot Instance & Unity Catalog
Auto Scaling & Cluster Nodes
Performance Optimizations

Ch 19: Scala Notebooks – V1

Scala Notebooks: Realtime Use
JVM and Scala Notebooks
Creating Data Frames in Scala
Creating Temp Tables in Scala
Medallion Architecture
Parquet Tables & Delta Tables

Ch 20: Scala Notebooks – V2

Working with Widgets in Scala
Variables and Parameters
Dynamic Connections
Format String (F String) Options
Using SQL DB Connections in Scala
Python Versus Scala in Realtime?

Databricks Certification Exam
Fabric Cloud Concepts
Fabric Cloud Migrations

SQL SCHOOL

24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.

Training FAQs

Who is SQL School? How far you have been in the training services ?

SQL School is a registered training institute, established in February 2008 at Hyderabad, India. We offer Real-time trainings and projects including Job Support exclusively on Microsoft SQL Server, T-SQL, SQL Server DBA and MSBI (SSIS, SSAS, SSRS) Courses. All our training services are completely practical and real-time. CREDITS of SQL School Training Center

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Real-time projects in
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