#DP-700

DP – 700 is the certification for Fabric Data Engineer. Fabric Data Engineer is the latest trending job role that deals with End to End Data Warehouse design (DWH) using ETL (Extract, Transform, Load) techniques. This prominent job role also involves Big Data Analytics and Business Intelligence implementation using Spark, PySpark, Cloud Computing, TSQL and more..

Training Highlights

✅ Data Factory Pipelines (ETL)
✅ Dataflow Gen2 Transformations
✅ Lakehouse & Warehouse
✅ Mapping, Wrangling, Alerts
✅ ETL Schedules & Triggers
✅ Security, FAUM, Activator
✅ Git Integration for CI/CD
✅ DP 700 Exam Guidance
✅ Real Time Project
✅ 1:1 Mentorship, Resume

Modules We Learn

✅ Module 1: SQL Server (MSSQL), TSQL
✅ Module 2: Fabric Data Engineering

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Course Content
FAQs

DP 700 Training
Course Contents:

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Module 1: SQL Server (MSSQL), TSQL

Ch 1: SQL Database Job Roles

Database Intro
OLTP, DWH, OLAP
DBMS Basics
Data Engineer Job Roles

Ch 2: Database Intro & Installations

SQL Server Installations
Instance & Collations
SSMS Tool Installation
Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

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
DML: Insert, Update, Delete
DQL: Select, Fetch
Add, Truncate Statements
SQL Operators

Ch 5: Data Types & Variables

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

Ch 6: Data Imports From Files

Data Imports with Excel
Data Imports with CSV
Auto Detection of DataTypes
OLE-DB Connections
Order By, TOP, OFFSET

Ch 7: Sub Queries

Basic Sub Queries
Aggregations
Combining Queries
UNION, UNION ALL

Ch 8: Schemas & Batches

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

Ch 9: Constraints, Keys & RDBMS

Null, Not Null Constraints
Unique Key & Check
Primary Key Constraint
Foreign Keys, Default
DB Diagrams & ER Models

Ch 10: Normal Forms & RDBMS

Normal Forms: 1 NF, 2 NF
3 NF, BCNF and 4 NF
1:1, 1:M, M:1 Cardinality
Cascading Keys
Self Referencing Keys

Ch 11: Joins & Queries

Joins: Table Comparisons
Inner Joins & Matching Data
Outer Joins: LEFT, RIGHT
Full Outer Joins & Aliases
Self Joins & Aliases

Ch 12: Views & RLS

Views: Realtime Usage
DML, SELECT with Views
Excel Analytics with Views
Important System Views

Ch 13: Stored Procedures – 1

Stored Procedures: Realtime Use
Parameters Concept with SPs
Procedures with SELECT
System Stored Procedures
Stored Procedures, Tuning

Ch 14: Stored Procedures – 2

Merge Statement (Upsert)
Merge with OLTP & DWH
Matched and Not Matched
Merge Statement inside SPs
SP Recompilations

Ch 15: User Defined Functions – 1

Scalar Functions in Real-world
Inline & Multiline Functions
Parameterized Queries
Variables & Parameters
Function Executions

Ch 16: User Defined Functions – 2

Date & Time Functions
String Functions & Queries
Aggregated Functions & Usage
Window Functions (Rank)
Row_Number, DenseRank
Partition By & Order By

Ch 17: 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 18: Group By Queries

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

Ch 19: Joins with Group By

3 Table, 4 Table Joins
Join Queries & WHERE
Join Queries & Group By
IIF(), CASE Statement
Query Execution Order

Ch 20: Transactions & ACID

Auto Commit Transaction
Explicit Transactions
COMMIT, ROLLBACK
Checkpoint & Query Blocking
READPAST, LOCKHINT

Ch 21: Indexes Basics, Tuning

Clustered Index, Primary Key
Non Clustered Index
Query Optimizer
Tuning Join Queries
Tuning Group By Queries

Ch 22: CTEs & Tuning

Common Table Expression
CTEs for Data Retrieval
CTEs for DML Operations
CTEs for Data Cleansing
Using CTEs with Row Number

Ch 23: Cursors & Temp Tables

Cursors & Fetch
Local Temp Tables
Global Temp Tables
SELECT..INTO Statement

Ch 24: Capstone Project (HealthCare Management System)

ECommerce Database
Data Validations
Query Writing
Query Tuning
Excel Analytics

Module 2: Fabric Data Engineer

Week 1: Microsoft Fabric Fundamentals & OneLake
Learning Modules

Introduction to Microsoft Fabric
Understand OneLake Architecture
Lakehouse Fundamentals
Delta Lake Foundations
Fabric Capacity & Workspaces

Hands-on Labs

Create Fabric Trial Workspace
Create Lakehouse
Upload CSV files
Explore OneLake Explorer
Configure Workspace Roles

Learning Outcome

Understand Fabric architecture
Differentiate Lakehouse, Warehouse, Event house

Week 2: Data Ingestion & Data Factory
Learning Modules

Data Factory in Microsoft Fabric
Dataflows Gen2
Incremental Loading
Data Integration Patterns
Data validation
SCD Type 1
SCD Type 2
Data Pipelines
Copy Activities & Pipelines
Parameters & Variables
Expressions
Lookup
ForEach
If Condition
Stored Procedure
Notebook Activity
Pipeline-to-Pipeline execution
Scheduling & Triggers
Error Handling
Retry mechanisms
Logging
Incremental Loads
Metadata-driven Pipelines

Hands-on Labs
Lab 1: Pipeline Development

Create Pipeline
Copy from Azure SQL Database
Copy from ADLS Gen2
Schedule execution

Lab 2: Dataflows Gen2

Import CSV
Apply transformations
Load to Lakehouse

Lab 3: Incremental Processing

Watermark columns
Historical + Delta Load

Learning Outcome

Pipeline orchestration
Incremental ingestion strategies

Week 3: Spark & Notebook Engineering
Learning Modules

Apache Spark Fundamentals
Spark SQL
PySpark Transformations
Delta Tables
Medallion Architecture
PySpark
DataFrames
Schema management
Joins
Aggregations
Window Functions
Null handling
Deduplication
JSON processing
Explode
UDFs
Date/time transformations
Delta Lake
MERGE
UPDATE/DELETE
Schema evolution
Time Travel
OPTIMIZE
VACUUM
Partitioning
Performance
Lazy evaluation
Shuffle
Partitioning
Repartition vs Coalesce
Cache/Persist
Spark execution plan basics

Labs

Create Notebook
Bronze Layer
Raw ingestion
Silver Layer
Cleansing
Deduplication
Gold Layer
Business aggregations

Advanced Labs

Delta Merge
Upsert operations
Performance Optimizations
Partitioning

Learning Outcome

PySpark development
Delta Lake engineering

Week 4: Data Warehouse Engineering
Learning Modules

Fabric Warehouse
T-SQL Development
Dimensional Modeling
Fact & Dimension Design
Performance Optimization
Star Schema
Surrogate Keys
Fact table grain
Conformed Dimensions
Date Dimension
Incremental Fact Loading

Labs

Create Warehouse
Star Schema

Build:

FactSales
DimCustomer
DimDate
DimProduct
Warehouse Objects
Views
Stored Procedures
Functions

Learning Outcome

 Modern Data Warehousing
 Analytical Modeling

Week 5: Real-Time Analytics & Streaming
Learning Modules

 Eventstream
 Real-Time Analytics
 Eventhouse
 KQL Fundamentals
 Streaming Solutions

Labs

 Eventstream
 Simulated IoT Device
 Ingest streaming data
 Route data to Lakehouse
 Eventhouse
 Create KQL Tables
 Build near real-time reporting

Learning Outcome

Streaming analytic
Eventhouse & KQL

Week 6: Enterprise Fabric – Security, Governance, DevOps & Monitoring
Learning Modules

Security in Fabric
Governance
Git Integration
Deployment Pipelines
Monitoring & Troubleshooting
Workspace architecture
Dev/Test/Prod strategy
Git branching strategy
Environment configuration
Secrets/credentials concepts
Lineage
Impact analysis
Capacity monitoring
Pipeline monitoring
Spark monitoring
Cost optimization

Labs

Security
Workspace Roles
Object Level Security
Row Level Security
Git Integration
Commit Changes
Monitoring Hub
Capacity Metrics
Refresh Tracking

Learning Outcome

Enterprise Deployment
Deployment – Dev → Test → Prod
Branch Management
Connect Fabric Workspace

Week 7: Real-Time Project
Project Activities

Ingest data from Azure SQL, ADLS, CSV & APIs
Build Fabric Data Factory Pipelines
Implement Full & Incremental Loads
Create Bronze, Silver & Gold Layers
Perform Data Cleansing & Transformations using PySpark
Build Delta Tables
Implement MERGE & Upsert Operations
Handle Duplicate & Missing Data
Build Fact & Dimension Tables
Implement Star Schema
Create Fabric Warehouse
Automate End-to-End Pipeline Execution
Implement Error Handling & Logging
Monitor Pipeline & Spark Jobs
Optimize Lakehouse & Warehouse Performance

Enterprise Deployment

Configure Dev, Test & Production Workspaces
Integrate Fabric with Git
Configure Deployment Pipelines
Deploy Dev → Test → Production
Configure Workspace & Item-Level Security
Monitor Fabric Workloads
Capacity & Performance Optimization

DP-700 Training FAQ's

What is DP-700 Job Role?

A DP-700 certified professional is an Azure Data Analyst Associate who is responsible for designing and implementing data solutions using Azure Synapse Analytics, Azure SQL, Data Lake, and Power BI. The role involves building and optimizing data models, ingesting and transforming data, implementing security, and creating enterprise-level reports and dashboards. DP-700 professionals help organizations turn raw data into actionable business insights on Azure.

What are the Job Roles of a DP-700 Azure Data Analyst?

💼 Top Job Roles:

1️⃣ Design and build modern analytics solutions using Azure services
2️⃣ Create and optimize data models for analytics and reporting
3️⃣ Develop ETL/ELT pipelines using Synapse and Data Factory
4️⃣ Visualize data and create dashboards using Power BI
5️⃣ Implement data governance, security, and compliance
6️⃣ Collaborate with data engineers, architects, and business users and more..!