Best Microsoft Fabric & Azure Data Engineering Training in Hyderabad
Eclasess is a leading IT Training Institute located in Ameerpet, Hyderabad Providing Quality Training and Placements for Students and Working Professionals who are looking to Upgrade their career. We Provide Training and Course Materials with a Free Demo for Microsoft Fabric and Azure Data Engineering Training.
We provide Microsoft Fabric and Azure Data Engineering Training in Hyderabad with 60 hours of live training and 30 hours of placement-focused practice. The course includes daily live classes, recordings, notes, DP-700 certification guidance, scenario-based tasks, Agile sprints, end-to-end projects, resume preparation and mock interviews.
This course covers Basic SQL, Basic Python, Microsoft Fabric Data Engineering Services, Azure Data Engineering Services and Microsoft Power BI. Learners work with OneLake, Lakehouse, SQL Database, Warehouse, Mirroring, Pipelines, Notebooks with PySpark, KQL Database, Activators, AI Data Agents, Data Factory, Databricks, Synapse, Power BI and deployment processes.
Introduction to Microsoft Fabric & Azure Data Engineering
Eclasess is providing Microsoft Fabric and Azure Data Engineering training designed by experienced data engineering professionals. You will become strong in Fabric, Azure Data Engineering, PySpark, Power BI and real-time project implementation after completing the course.
Microsoft Fabric & Azure Data Engineering Course
The course is designed with a 60-hour training program and a 30-hour placement program. It includes Microsoft Fabric capacity setup, OneLake, Lakehouse, Warehouse, SQL Database, Azure SQL mirroring, Snowflake mirroring, Fabric Pipelines, PySpark notebooks, Dataflow Gen2, KQL Database, Semantic Model, AI Data Agents, Power BI reports, Activator, Azure DevOps, Azure Data Factory, Azure Databricks, CI/CD deployment and two end-to-end projects.
Training Program and Placement Program
The training program includes live classes for one hour daily from Monday to Friday, daily class recordings, daily class notes and DP-700 certification guidance. The placement program includes weekend classes, Agile methodology with 10 sprints, 200+ scenario-based tasks, two end-to-end projects, 100+ interview questions and answers, resume preparation support and mock interviews.
End-to-End Projects
As part of the placement program, learners work on a Health Care Insurance Project in Microsoft Fabric and a Retail Sales Project in Azure Data Engineering. These projects help learners understand real-time data ingestion, transformation, reporting, deployment and interview-ready project explanation.
- Live Classes — Daily 1 Hour (Monday to Friday)
- Daily Class Recordings
- Daily Class Notes
- DP-700 Certification Guidance
- Weekend Classes — Saturday & Sunday
- Agile Methodology with 10 Sprints
- 200+ Scenario-Based Tasks
- Two End-to-End Projects
- Health Care Insurance Project in Microsoft Fabric
- Retail Sales Project in Azure Data Engineering
- 100+ Interview Questions & Answers
- Resume Preparation Support
- Mock Interviews
- Basic SQL
- Basic Python
- Microsoft Fabric Data Engineering Services
- OneLake
- Lakehouse
- SQL Database
- Warehouse
- Mirror SQL Database
- Mirror Snowflake Database
- Pipelines
- Notebooks with PySpark
- KQL Database
- Activators
- AI Data Agents
- Deployment Process
- Azure Data Engineering Services
- Storage Account (Blob Storage / Data Lake Gen2)
- Azure SQL Database
- Synapse Data Warehouse
- Synapse Analytics
- Azure Data Factory
- Azure Databricks
- Azure DevOps Git Repository
- CI/CD Deployment Process
- Microsoft Power BI
- Power BI Desktop
- Power Query Editor
- Semantic Model
- Power BI Reports & Dashboards
- DAX Functions & Calculations
- Basics of Python
- Variables
- Data Types
- Loops
- Functions
- Arrays
- List, Tuple, Set, Dictionary
- NumPy
- Pandas
- Basics of SQL
- DML Commands
- INSERT
- DELETE
- UPDATE
- SELECT
- DDL Commands
- CREATE
- ALTER
- DROP
- TRUNCATE
- SQL Concepts
- WHERE Clause
- Operators
- BETWEEN Condition
- UNION & UNION ALL
- GROUP BY
- HAVING Clause
- Aggregate Functions
- String Functions
- Datetime Functions
- Joins
- INNER JOIN
- LEFT JOIN
- RIGHT JOIN
- FULL OUTER JOIN
- Advanced SQL
- CTE (Common Table Expressions)
- Stored Procedures
- 1. Introduction to Fabric Azure Data Engineering RoadMap
- 2. How to Create Azure and Fabric Free Subscription
- 3. How to Create Microsoft Fabric Capacity in Azure
- How to Create workspace and assign Fabric Capacity
- What is OneLake and overview
- What is Lakehouse and how to create
- What is SQL Analytics End Points
- What is Shortcuts in Lakehouse
- Create Shortcut to Data Lake Gen2 to Lakehouse
- 4. How to Create Azure SQL Database
- How to Create SQL Database in Fabric
- How to Mirror Azure SQL Database into Fabric
- How to Mirror Snowflake Database into Fabric
- How to Create Warehouse in Fabric
- 5. How to Create Pipeline in Fabric
- How to Copy Data from Lakehouse File into Lakehouse Table
- How to Copy Data from Lakehouse File into Warehouse Table
- 6. How to Copy Table from On Prem SQL to Lakehouse File
- 7. How to Copy Multiple Tables from On Prem SQL to Lakehouse Multiple Files
- 8. How to Copy Multiple Tables from On Prem SQL to Lakehouse Single File
- 9. How to Copy Multiple Tables from On Prem SQL to Lakehouse Single Table
- 10. Incremental Loading for single Table from On Prem SQL Server
- 11. Incremental Loading for Multiple Tables from On Prem SQL Server
- 12. If file exist Copy Else Send Email Notification
- 13. Based on Type of File Load Data into specific Table
- 14. Create Pipeline to execute until file Available using Until Activity
- 15. How to Schedule Pipeline
- Introduction to PySpark in Microsoft Fabric
- 16. Basics of Python
- 17. Basic of SQL
- 18. Spark Session and Data Frame Basics
- Reading Data from Lakehouse (CSV, Parquet, JSON, Delta)
- Data Frame Select and Projection (select, alias)
- Adding and Renaming Columns (withColumn, withColumnRenamed)
- Dropping Columns (drop)
- 19. Removing Duplicates (distinct)
- Filtering Data (filter, where)
- Applying Multiple Conditions (and, or)
- Conditional Columns (when, otherwise)
- Column Expressions and Arithmetic Operations
- String Functions (concat, substring, length, trim, upper, lower)
- Date Functions (current_date, datediff, to_date)
- 20. Null Handling (coalesce, nvl)
- Handling Missing Data (fillna, dropna)
- Removing Duplicate Records (dropDuplicates)
- Data Type Casting (cast)
- Data Standardization Techniques
- GroupBy Transformations (groupBy)
- Aggregation Functions (sum, avg, count, min, max)
- Multiple Aggregations in Single Query
- Filtering Aggregated Data
- 21. Join Transformations (Inner, Left, Right, Full)
- Joining on Multiple Columns
- Handling Duplicate Columns After Join
- 22. Window Functions Overview
- Row Number, Rank, Dense Rank
- Lead and Lag Functions
- Running Totals and Cumulative Calculations
- Data Reshaping using Pivot
- Unpivot using Stack
- 23. Exploding Arrays (explode)
- Sorting Data (orderBy, sort)
- Ascending and Descending Order
- Working with Nested JSON Data
- Flattening Complex Structures
- Extracting Nested Fields
- 24. Creating User Defined Functions (UDF)
- Applying Custom Business Logic
- File Format Transformations (CSV to Parquet, JSON to Delta)
- Writing Data (overwrite, append)
- 25. Delta Lake Concepts in Fabric
- Merge (Upsert) Operations
- Update and Delete Operations
- Time Travel in Delta Tables
- Incremental Data Processing
- 26. Write T-SQL to Load Data into Warehouse
- Performance Optimization Techniques
- Partitioning Data z-ordering
- Caching and Persistence
- Repartition vs Coalesce
- Broadcast Join Optimization
- 27. Scheduling Notebooks
- 28. Introduction to Data Flow Gen2
- Remove Duplicates - handling duplicate customer records
- Fill Null Values - replacing missing sales or customer data
- Replace Values - correcting invalid or inconsistent data
- Trim & Clean Text - removing spaces and unwanted characters
- Change Data Type - fixing schema issues from source files
- Filter Rows - selecting only active customers or valid transactions
- Remove Rows - excluding cancelled or invalid records
- Custom Column - calculating profit (Sales - Cost)
- Conditional Column - categorizing sales (High / Medium / Low)
- Group By - summarizing sales by region or product
- 29. Aggregations (Sum, Avg, Count) - generating KPI metrics
- Merge Queries (Join) - combining customer and orders data
- Expand Columns - extracting required fields after join
- Append Queries - combining monthly or yearly files
- Pivot Column - converting row data into columns for reporting
- Unpivot Columns - normalizing wide tables
- Expand Record - flattening nested JSON objects
- Expand List - handling array data from APIs
- Sort Rows - ordering data for reporting
- Add Index Column - generating sequence numbers
- 30. Split Column - separating full name into first/last name
- Merge Columns - combining address fields
- Uppercase / Lowercase - standardizing text data
- Format Text - applying consistent data formats
- Date Transformations - extracting year, month, day
- Date Difference - calculating duration (order to delivery)
- Select Columns - choosing required fields for final dataset
- Rename Columns - aligning with business naming standards
- Load to Lakehouse - storing transformed data
- Load to Data Warehouse - preparing for reporting
- 32. KQL Database
- Store telemetry/log data - application and system logs
- Query large datasets - fast analytics using KQL
- Time-series analysis - analyze data over time
- Filter data - retrieve specific records using conditions
- 33. Aggregation - summarize large datasets (count, avg, sum)
- Join datasets - combine multiple tables
- Real-time analytics - query streaming ingested data
- Data retention policies - manage lifecycle of data
- 34. Semantic Model
- Create data model - building relationships between tables
- Define relationships - connecting fact and dimension tables
- Create measures - business calculations using DAX
- Create calculated columns - derived fields at row level
- Star schema design - optimizing analytics queries
- Define hierarchies - drill-down (Year to Month to Day)
- Apply row-level security - restrict data access
- 35. AI Data Agents
- Natural language querying - ask questions on data
- Generate insights - auto summaries from datasets
- Data exploration - AI-driven analysis
- Intelligent recommendations - suggest visuals or KPIs
- 36. Power BI Reports & Dashboards
- Build interactive reports - multiple visuals and pages
- Drill-down / drill-through - detailed analysis
- Apply filters & slicers - dynamic reporting
- 37. Create dashboards - pin visuals for summary view
- Publish reports - share across organization
- 38. Activator
- Integration with Power BI - monitor report data
- No-code automation - business user friendly
- 39. Azure DevOps Git - Deployment Pipelines
- Version control - manage code using Git
- Deployment pipelines - Dev to Test to Prod movement
- 40. Azure Storage Account Blob & Data Lake Gen2 Advanced Topics
- How to Create Synapse Dedicated SQL Pool DWH in Azure
- 41. Introduction to Azure Data Factory
- 42. How to copy Data from on prem SQL to Data Lake Gen2
- 43. How to Copy Multiple Tables Incremental Loading from On Prem SQL to Data Lake Gen2
- 44. Transformations in Mapping Data Flows in Data Factory
- 45. Scheduling Data Factory Pipelines using Triggers
- 46. Introduction to Azure Databricks
- What is Spark architecture
- 47. How to create Azure Databricks Cluster and Notebooks
- What is Unity Catalog-Schema-Volume-Tables
- 48. How to connect to Data Lake Gen2 from Databricks
- 49. Delta Lake in Azure Databricks
- 50. Workflows in Azure Databricks
- Scheduling and Deployment of Azure Data Factory and Databricks
To Speak With an Expert
+91 7989781302
- Duration 90 Hours
- Trained Students 9921
- Days Mon-Fri + Weekends
- Resume Preparation Yes
- Interview Guidance Yes
