Azure Data Engineer Training - Course in Hyderabad
                     Azure Data Engineer ETL Pipeline Explained Step by Step
Extract → Transform → Load → Analyze
How Azure services work together to build reliable data pipelines
Practical flow for modern data engineering
WWW.VISUALPATH.IN +91-7032290546
Slide 1 of 8
Identify Data Sources
Data can come from SQL databases, APIs, files, applications, and cloud storage.
Azure Data Factory connects to many different data sources.
Understand data format, size, frequency, and quality before processing.
Identify the data required for reporting, analytics, or applications.
Define a clear pipeline goal before development.
WWW.VISUALPATH.IN +91-7032290546
Slide 2 of 8
Extract Data with Azure Data Factory
Azure Data Factory (ADF) is commonly used to orchestrate ETL workflows.
ADF uses connectors to access databases, files, APIs, and other systems.
Data can be extracted on a schedule or triggered by an event.
Pipelines can perform full loads or incremental data extraction.
Monitoring helps identify failed or slow extraction activities.
WWW.VISUALPATH.IN +91-7032290546
Slide 3 of 8
Store Raw Data in Azure
Extracted data can be stored in Azure Data Lake Storage Gen2.
Raw data is often kept before major transformations are applied.
Organize data into Raw, Processed, and Curated layers.
Data Lake Storage handles large volumes of structured and unstructured data.
Raw data supports auditing, reprocessing, and troubleshooting.
WWW.VISUALPATH.IN +91-7032290546
Slide 4 of 8
Transform and Clean the Data
Transformation removes errors and converts raw data into useful information.
Common tasks include filtering, joining, sorting, deduplication, and data type 
conversion.
Azure Databricks, Synapse, and ADF Mapping Data Flows can process data.
Business rules can be applied during transformation.
Good transformations improve data quality and analytics reliability.
WWW.VISUALPATH.IN +91-7032290546
Slide 5 of 8
 Load Data into the Target
Cleaned data is loaded into a destination designed for analytics or 
applications.
Common targets include Azure Synapse Analytics, Azure SQL Database, 
and data warehouses.
Loading can be full, incremental, or based on changed records.
Partitioning and optimized loading can improve performance.
Validate record counts and key fields after loading.
WWW.VISUALPATH.IN +91-7032290546
Slide 6 of 8
 Monitor, Secure, and Optimize
Azure Data Factory provides pipeline monitoring and activity-level status.
Configure alerts and retry policies to handle temporary failures.
Use Azure Key Vault to securely manage secrets and credentials.
Apply access controls and encryption to protect sensitive data.
Optimize pipelines by reducing unnecessary data movement and processing.
WWW.VISUALPATH.IN +91-7032290546
Slide 7 of 8
Complete Azure ETL Pipeline — Key Takeaways
Extract: Collect data from databases, APIs, files, and applications.
Store: Keep raw data in Azure Data Lake Storage for reliable processing.
Transform: Clean, enrich, and apply business rules to the data.
Load: Move prepared data into Azure analytics and database platforms.
Monitor & Analyze: Track pipeline health and deliver trusted data for BI and AI.
WWW.VISUALPATH.IN +91-7032290546
Slide 8 of 8
 Contact
Azure Data Engineering
Address:- Flat no: 205, 2nd Floor, Nilgiri Block, Aditya Enclave, 
Ameerpet, Hyderabad-1 
Ph. No: +91-7032290546 
Visit: www.visualpath.in 
E-Mail: [email protected]
WWW.VISUALPATH.IN +91-7032290546
THANK YOU
                                                           Visit: www.visualpath.in
Scan me 
WWW.VISUALPATH.IN +91-7032290546