CDC (Change Data Capture)

 

Azure Cloud Data Engineering Training in Hyderabad – Quality Thoughts

Quality Thoughts offers one of the best Azure Cloud Data Engineering courses in Hyderabad, ideal for graduates, postgraduates, working professionals, or career switchers. The course combines hands-on learning with an internship to make you job-ready in a short time.

Our expert-led training goes beyond theory, with real-time projects guided by certified cloud professionals. Even if you’re from a non-IT background, our structured approach helps you smoothly transition into cloud roles.

The course includes labs, projects, mock interviews, and resume building to enhance placement success.

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Join us to unlock careers in cloud data engineering. Our alumni work at top companies like TCS, Infosys, Deloitte, Accenture, and Capgemini.

Note: Azure Table and Queue Storage support NoSQL and message handling for scalable cloud apps

CDC (Change Data Capture)

Change Data Capture (CDC) is a technique used to identify and capture changes (inserts, updates, deletes) made to data in a database, enabling real-time or near-real-time data integration. Instead of extracting the full dataset repeatedly, CDC tracks only the modified records, improving efficiency and reducing load on systems.

CDC can be implemented using methods like:

Database Logs: Reading transaction logs to detect changes.

Triggers: Database triggers capture modifications in tables.

Timestamp Columns: Tracking changes using modified date/time columns.

Benefits:

Real-Time Data Sync – Keeps systems (e.g., data warehouses, analytics platforms) up to date.

Performance Efficiency – Processes only changed data, reducing bandwidth and compute usage.

Supports Analytics & ETL – Ensures accurate, timely reporting.

Use Cases:

Data migration with minimal downtime.

Replicating databases across environments.

Feeding data lakes or BI tools with live updates.

In Azure, CDC can be implemented using Azure Data Factory, Azure Synapse, or SQL Server CDC feature, enabling continuous data pipelines and analytics with low latency. It’s essential for modern, real-time data architectures.

Read More

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