Microsoft Azure Data Engineering Course - Training In Bangalore
                     How Azure Turns Raw Data into Analytics-Ready Data
From Raw Data to Analytics-Ready Data
. Raw data comes from many business sources
. Azure connects and collects data efficiently
. Data pipelines transform scattered information
. Quality checks improve data reliability
. Analytics-ready data supports faster decisions
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Step 1 — Connect Diverse Data Sources
. Connect databases, files, APIs, and applications
. Ingest structured and semi-structured data
. Capture data from cloud and on-premises systems
. Schedule or trigger data ingestion
. Centralize data for downstream processing
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Step 2 — Store Raw Data in Azure
. Store original data without unnecessary transformation
. Use Azure Data Lake Storage for scalable storage
. Organize data using folders and logical zones
. Support multiple file and data formats
. Separate raw data from processed datasets
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Step 3 — Clean and Transform the Data
. Remove duplicates and invalid records
. Handle missing and inconsistent values
. Standardize formats, names, and data types
. Join information from multiple data sources
. Apply business transformation rules
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Step 4 — Build a Trusted Data Layer
. Create standardized and validated datasets
. Apply data quality and business rules
. Organize reusable analytical datasets
. Maintain metadata and data lineage
. Prepare data for reporting and machine learning
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Step 5 — Make Data Analytics-Ready
. Structure data for analytical workloads
. Create dimensional or optimized data models
. Aggregate frequently analyzed business metrics
. Improve query performance and accessibility
. Prepare datasets for business intelligence
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Step 6 — Connect Data to Analytics
. Connect curated data with Power BI
. Build dashboards from trusted datasets
. Enable self-service business analytics
. Monitor important business performance indicators
. Turn data into actionable business insights
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The Complete Azure Data Journey
. Sources → Ingestion → Raw Storage
. Raw Storage → Transformation → Quality Checks
. Quality Data → Curated Layer → Analytics
. Analytics → Dashboards → Business Insights
. Continuous monitoring improves the data platform
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          For More Information About 
Azure Data Engineer
Address:- Flat no: 205, 2nd Floor,
   Nilagiri Block, Aditya Enclave,  Ameerpet, 
Hyderabad-16
Ph. No: +91-7032290546
www.visualpath.in
 [email protected]
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