Sewa Studies

🚀 Sewa studies– Dynamic Pipeline Framework in Azure Data Factory for Loading 1000+ Tables
Hello Everyone,
Today, let's explore how enterprise projects load hundreds or even thousands of tables using a Metadata-Driven Dynamic Pipeline Framework.
Instead of creating separate pipelines for each table, we use a single reusable pipeline driven by metadata.
Why Dynamic Framework?
✅ Reduced Development Effort
✅ Scalable Architecture
✅ Reusable Pipelines
✅ Easy Maintenance
✅ Supports Hundreds or Thousands of Tables
✅ Faster Onboarding of New Tables
Metadata Control Table
TableName
SourceSchema
SourceTable
LoadType
WatermarkColumn
Customers
Sales
Customers
Incremental
ModifiedDate
Orders
Sales
Orders
Full
NA
Products
Master
Products
Incremental
LastUpdatedDate
Real-Time Architecture
Source Systems
Oracle
SQL Server
SAP
APIs
⬇️
Metadata Control Table
⬇️
Lookup Activity
⬇️
ForEach Activity
⬇️
Parameterized Dataset
⬇️
Copy Activity
⬇️
ADLS Gen2 Raw Layer
⬇️
Azure Databricks Transformation
⬇️
Silver Layer
⬇️
Gold Layer
⬇️
Power BI Dashboard
ADF Activities Used
🔹 Lookup Activity
🔹 ForEach Activity
🔹 Set Variable Activity
🔹 If Condition Activity
🔹 Copy Activity
🔹 Execute Pipeline Activity
Key Components
Parameterized Linked Services
Enables dynamic connection to multiple databases.
Parameterized Datasets
Avoids creating separate datasets for each table.
Metadata Table
Stores:
Source System
Table Name
Load Type
Watermark Column
Active Flag
Generic Pipeline
Single pipeline processes all tables dynamically.
Real-Time Example
Suppose you have:
500 Oracle Tables
300 SQL Server Tables
200 SAP Tables
Instead of creating 1000 pipelines,
✅ One Metadata Table
✅ One Generic Pipeline
✅ One Dataset
can process all 1000 tables.
Interview Question
Q: Why do enterprises prefer metadata-driven frameworks?
Answer
✔ Reusable Design
✔ Less Code Maintenance
✔ Easy Onboarding
✔ Scalable Solution
✔ Reduced Development Time
✔ Industry Best Practices

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