#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: Databricks Job Roles

Introduction to Data
Databricks Job Roles
Data Analyst Challenges

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
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 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: 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: SparkSQL – 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 Widgets
User Parameters
Manual Executions
Parameters & JSON

Ch 19: Lake Flow Jobs

Worksflows & CRON
Job Compute, Running Tasks
Python Tasks (Notebooks)
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
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: Spark Structured Streaming

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)

Module 3: 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

Module 4: 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

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

What is the Databricks Data Analyst course?

This course trains you in SQL Server TSQL, Python for analytics, Databricks Workspace, Spark SQL, Delta Lake, Auto Loader, Streaming, Unity Catalog, Medallion Architecture, Power BI with Spark, and Databricks Data Analyst exam guidance. It is 100% hands-on and job-oriented.

Who should join the Databricks Data Analyst training?

Aspiring Data Analysts, Data Engineers, BI Developers, SQL Developers, and Data Scientists who want to analyze data using Spark SQL, Databricks notebooks, Delta Lake, and build dashboards using Power BI integrations.

What are the modules included in this program?

Module 1: MSSQL – SQL Server TSQL
Module 2: Python (For Analytics)
Module 3: Databricks – Spark SQL, PySpark, Delta Lake, Architecture
Module 4: Power BI with AI – Cloud, Visualizations, Modelling, RLS
Realtime projects and assignments are included in each module.