#Databricks Data Analyst

Databricks Data Analyst leverages the power of Databricks to explore, visualize, and analyze large datasets efficiently. They use SQL, Python, and visualization tools to transform raw data into actionable insights, supporting business intelligence and decision-making. With Databricks’ unified platform, Data Analysts can collaborate seamlessly and deliver faster, data-driven results.

✅ SQL Analytics in Databricks
✅ Dataframes & PySpark Queries
✅ Delta Tables for Analytics
✅ BI Integration with Power BI
✅ Real-Time Data Insights
✅ Visualization in Databricks
✅ Security & Access Roles
✅ End-to-End Analytics Project
✅ Real Time Project
✅ 1:1 Mentorship, Resume

Module 1: SQL Server TSQL (MS SQL) Queries

Ch 1: Data Analyst Job Roles

Introduction to Data
Data Analyst Job Roles
Data Analyst Challenges
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

Ch 24: Key Take-Aways from Module 1

Case Study 1: Medicare: Tasks, Solutions
Case Study 2: ECommerce: Task, Solutions
Chapter Wise Assignments: Solutions
Dailly Assignments: Review (Feedback)
Weekly Mock Interview: Feedbacks

Module 2: Python Concepts

Ch 1: Python in Data Analyst

Database Types
Role of Python in Analysis
Databricks & Data Analyst with Python

Ch 2: Python Introduction

Python Introduction
Python Versions
Python Implementations
Python Installations
Python IDE & Usage
Jupyter Notebooks

Ch 3: Python Operations

Basic Operations in Python
Python Scripts, Print()
Single, Multiline Statements
Python: Internal Architecture
Compiler Versus Interpreter

Ch 4: Data Types & Variables

Integer / Int Data Types
Float, String Data Types
Sequence Types: List, Tuple
Range, Complex & memview
Retrieving Data Type: type()

Ch 5: Python Operators

Arithmetic, Assignment Ops
Comparison Operators
Operator Precedence
If … Else Statement, Pass
Short Hand If, OR, AND
ELIF and ELSE IF Statements

Ch 6: Python Loops, Iterations

Python Loop & Realtime Use
Python While Loop Statement
Break and Continue Statement
Iterations & Conditions
Exit Conditions & For Loops
iter() and Looping Options

Ch 7: Python Functions

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

Ch 8: Python Modules

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

Ch 9: 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 10: 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 11: Pandas DataFrames 1

Installation of Pandas
Python Modules & Pandas
Pandas Codebase & Usage
import pandas.DataFrame
Pandas Series, arrays

Ch 12: Pandas DataFrames 2

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

Ch 13: Pandas Transformations

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

Ch 14: Key Take-Aways from Module 2

Case Study @ ECommerce: Task, Solutions
Chapter Wise Assignments: Solutions
Dailly Assignments: Review (Feedback)
Weekly Mock Interview: Feedbacks

Module 3: Databricks

Ch 1: Databricks Intro

Big Data
Open Source ETL
What is a Data Lakehouse?
Hadoop, MapReduce and Apache Spark

Ch 2: Databricks Architecture

Unity Catalog Volume
Clusters…
Apache Spark and Databricks
Apache Spark Ecosystem
Compute Activities

Ch 3: Databricks Workspace

Workspace Objects
Databricks Notebooks
Databricks Managed Resources
Databricks Workspace UI
UI Updates

Ch 4: Databricks Notebooks

Databricks Notebooks
Mix Languages in Notebooks
Comments and Markdown Text to Databricks Notebooks
Organizing your Workspace Objects
SparkSQL Notebooks

Ch 5: SparkSQL Notebooks – 1

Spark SQL API
Creating a Catalog, Schema
Adding New Columns
Changing Data Types
Removing Columns
Union

Ch 6: SparkSQL Notebooks – 2

Math Functions
Sort Functions
String Functions
Datetime Functions
Conditional Statements
SQL Expressions with expr()

Ch 7: SparkSQL Notebooks – 3

Volume for our Data Assets
Uploading the Countries Data Files
File Formats, Schema Inference
How to Partition your Data
Databricks File System Utilities
Creating Views with SQL
Creating Catalogs, Schemas and Volumes with SQL

Ch 8: PySpark – 1

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

Ch 9: PySpark – 2

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

Ch 10: 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 11: Unity Catalog (Dev)

Unity Catalog Managed Tables
SQL Queries with the PySpark API
Managed Tables with SQL
Creating Views with SQL
Creating Catalogs, Schemas and Volumes with SQL
Dropping Unity Catalog Objects with SQL
Temporary Views
External Tables, External Volumes

Ch 12: Unity Catalog (Admin)

Metastore and the Unity Catalog Object Model
Databricks Account Console
Data Discovery and Lineage
System Tables
Databricks Account and Workspace Roles
Unity Catalog Privileges and Securable Objects
Workspace Access Control Lists (ACLs)
Workspace-Catalog Binding
Workspace Compute Policies

Ch 13: PySpark Transformations – 1

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

Ch 14: PySpark Transformations – 2

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

Ch 15: 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 16: Medallion Architecture

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

Ch 17: Delta Lake – 1

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

Ch 18: Delta Lake – 2 (SCD)

Schema Evolution
Delta Lake Data Files
Deleting and Updating Records
Merge Into
Table Utility Commands
Exploratory Data Analysis

Ch 19: Implementation of SCD Type 2

Incremental Loads
Upserts Versus SCD
Ne Row Inserts
Existing Row Updates
Old History Retention
Delta Transaction Log

Ch 20: Widgets

Text Widgets
User Parameters
Manual Executions
Lake Bridge
Databricks BridgeOne

Ch 21: 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 22: 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 23: 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 24: 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 25: Auto Loader

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

Ch 26: Lake Flow Declarative Pipelines

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

Ch 27: Security: ACLs

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

Ch 28: Realtime Project @ Ecommerce / Banking / Sales

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

Ch 29: Key Take-Aways from Module 3

👉 Realtime Project: Requirement, CI CD, Solution, FAQs
👉 Chapter Wise Assignments: Solutions
👉 Dailly Assignments: Review (Feedback)
👉 Weekly Mock Interview: Feedback

Module 4: Power BI with AI

Ch 1: Power BI Intro, Installation

Power BI & Data Analysis
5 Design Tools, 3 Techniques
2 Hosting Solutions
Power BI with Co-Pilot & AI
Power BI Installation

Ch 2: Report Design Concepts

Basic Report Design (PBIX)
Get Data, Canvas (Design)
Data View, Data Models
Data Points, Spotlight
Focus Mode, PDF Exports

Ch 3: Visual Interactions, PBIT

Visual Interactions & Edits
Limitations with Visual Edits
Creating Power BI Templates
CSV Exports & PBIT Imports

Ch 4: Grouping, Hierarchies

Creating Groups : Lists
Creating Groups: Bins
List Items & Group Edits
Bin Size & Bin Count

Ch 5: Slicer & Visual Sync

Slicer Visual in Power BI
Slicer: Format Options
Single Select, Multi Select
Slicer: Select All On / Off
Visual Sync with Slicers

Ch 6: Hierarchies & Drill-Down

Hierarchies: Creation, Use
Hierarchies: Advantages
Drill Up, Drill Down
Conditional Drill Down
Filtered Drill Down, Table View

Ch 7: Filters & Drill Thru

Power BI Filters
Basic, Top & Advanced
Visual Filters, Page Filters
Report Level Filters, Clear Filter
Drill Thru Filters & Usage

Ch 8: Bookmarks, Buttons

Power BI Bookmarks
Images: Actions, Bookmarks
Buttons: Actions, Bookmarks
Page to Page NavigationsScore Cards, Master Pages

Ch 9: SQL DB Access & Big Data

SQL DB Access, Queries
Storage Modes: Direct Query
Formatting & Date Time
Storage Modes in Power BI
Azure (Big Data) Access & Formatting

Ch 10: Power BI Visualizations

Charts, Bars, Lines, Area
TreeMaps & HeatMaps
Funnel, Card, Multrow Card
PieCharts & Waterfall
Scatter Chart, Play Axis
Infographics, Classifications

Ch 11: Power Query Introduction

Power Query (Mashup)
ETL Transformations in PBI
Power Query Expressions
Table Combine Options
Merge, Union All Options
Close, Apply & Visualize

Ch 12: Power Query : Table Tfns

Table Duplicate, Header Promotion
Group By Transformation
Aggregate, Pivot Operation
Reverse Rows, Count Rows
Advanced Power Query Mode

Ch 13: Power Query: Column Tfn

Any Column Transformations
Data Type Detection, Change
Rename, Replace, Move
Fill Up, Fil Down
Step Edits & Rollbacks

Ch 14: Power Query: Text, Date

String / Text Transformations
Split, Merge, Extract, Format
Numeric and Date Time
Add Column & Expressions
Expressions and New Columns
Column From Examples

Ch 15: Power Query: Parameters

Parameters in Power Query
Static Parameters, Defaults
Dynamic Dropdowns, Lists
Linking with Table Queries
Step Edits, Type Conversions

Ch 16: Power BI Cloud: Publish

Power BI Cloud Concepts
Workspace Creation, Usage
Report Publish Cloud
Report Edits in Cloud
Semantic Models & Usage

Ch 17: Power BI Cloud Dashboards

Power BI Dashboards
Dashboard Creation, Usage
Pin Visuals, Pin LIVE Pages
Add Image, Video Tiles
Q&A & Pin Tiles

Ch 18: Power BI Cloud Operations

Report Shares, Alerts
Subscriptions, Exploration
Downloads & Edits
Report Cloning in Cloud
QR Codes, Web Publish
Lineage & Metrics

Ch 19: Power BI Cloud Gateways

Data Gateways, Data Refresh
Install, Configure Gateways
Data Sources Configurations
Data Refresh & Scheduling
Gateway Optimizations

Ch 20: Power BI Cloud Apps

Power BI Apps: Creation
App Sections & Content
Audience Options
App Security & Sharing
App Updates, Favourites
App URL, End User Access

Ch 21: Power BI Report Server

SQL Server 2025 (Mandatory Installations)
Power BI Report Server
Report Server Vs Cloud
Installation, Configuration
RS Config Tool Options
Report Database, TempDB
Web Service & Server URL

Ch 22: Paginated Reports

Report Builder Tool
Paginated Report (RDL)
Report Expressions (RDL)
Tablix, Chart Wizards
Fields & Drill-Down
RDL Report Publish

Ch 23: DAX Concepts (Basics)

DAX Concepts: Intro & Realtime Need
DAX Columns: Creation, Use
DAX Measures: Creation, Use
DAX Functions: IIF, ISBLANK
SUM, CALCULATE Functions
DAX Cheat Sheet

Ch 24: DAX Quick Measures

Quick Measures in Power BI
Average & Filters
Running Totals
Star Rating Calculations
DAX Measures in Data View
DAX in Visuals
DAX in Cloud Reports

Ch 25: Data Modelling, DAX

Dimensions Tables
Fact Tables & DAX Measures
Data Models & Relations
DAX Expressions
Star & Snowflake Schemas
DAX Joins & Expressions

Ch 26: DAX Joins, Variables

CALCULATEX & Variables
COUNT, COUNTA, etc..
SUM, SUMX, etc..
SELECTED MEMEBER
Filter Context, RETRUN
Dynamic Report with DAX

Ch 27: DAX Time Intelligence

Need for Time Intelligence
Date Table Generation
Time Intelligence with DAX
PARALLELPERIOD, DATE
CALENDAR, Total Functions
YTD, QTD, MTD with DAX

Ch 28: DAX – Row Level Security

RLS: Row Level Security
Data Modelling & Roles
Verify Roles (Testing)
Add Cloud Users to Roles
Dynamic Row Level Security
Testing RLS in Power BI

Ch 29: Analytical Reports

Analytical Report Concepts
Excel Data Analytics
Excel with Power BI Cloud
SQL, AVRO, JSON Sources
Analyse in Excel (Cloud)
Excel Reports to Cloud

Ch 30: PL 300 Exam Guidance, CoPilot

PL 300 Exam (Microsoft Certified Data Analyst) Guidance
PL 300 Exam Mocks
AI Components in Power BI
CoPilot Practical Uses
CoPilot with Desktop
CoPilot with Cloud
Need for AI Analytics (Fabric)

Ch 31: Key Take-Aways from Module 2

Case Study 1: Medicare: Tasks, Solutions
Case Study 2: ECommerce: Task, Solutions
Chapter Wise Assignments: Solutions
Dailly Assignments: Review (Feedback)
Weekly Mock Interview: Feedbacks

Brochure Download Request a Callback

Trainer Mr. Sai Phanindra Tholeti – 20 Years of Real-Time Experience | Microsoft, Deloitte Alumnus | Data Analyst and AI Expert

Trainer Sai Phanindra Tholetti – 20+ Years Expert in Power BI and Data Analytics | SQL School Training Institute

Fabric Data Analyst Training FAQ's

What is Fabric Data Analyst Job Role?

A Fabric Data Analyst is responsible for designing, building, and delivering data analytics solutions using Microsoft Fabric. The role involves working with lakehouses, warehouses, dataflows, pipelines, and Power BI to ingest, model, and visualize data. Fabric Data Analysts turn raw enterprise data into actionable business insights through modern analytics solutions that are secure, scalable, and governed.

What are the Job Roles of a Fabric Data Analyst?

💼 Top Job Roles:

1️⃣ Ingest and transform data using Fabric Data Pipelines and Dataflows
2️⃣ Build Lakehouse and Warehouse data models for analytics
3️⃣ Develop semantic models for enterprise reporting
4️⃣ Design and publish interactive dashboards using Power BI
5️⃣ Apply data security, governance, and compliance in analytics solutions
6️⃣ Collaborate with engineers, architects, and business users and more..!