Pipelines and Activities

  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.

Why Choose Us?

     1. Live Instructor-Led Training

     2. Real-Time Internship Projects

     3.Resume & Interview Prep

    4 .Placement Assistance

    5.Career Transition Support

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

Pipelines and Activities 

🔹 In Azure Data Factory (ADF), Pipelines and Activities are core building blocks used to create powerful and flexible data workflows.

📦 Pipeline:

A Pipeline is a logical container for a group of activities. It defines the sequence of tasks required to move, transform, or process data. You can think of a pipeline as a flowchart that describes the end-to-end data workflow.

You can run pipelines on-demand, on a schedule, or triggered by events. Pipelines can be parameterized, allowing dynamic and reusable workflows.⚙️ Activities:

An Activity represents a single step or task within a pipeline. There are different types of activities based on their function:

Data Movement: Copy Activity (moves data from source to destination)

Data Transformation: Mapping Data Flows, Data Lake Analytics, HDInsight

Control Flow: If Condition, ForEach, Until, Execute Pipeline

External Processing: Stored Procedure, Databricks Notebook, Azure Functions

✅ Key Benefits:

Modular design: separate steps for better debugging

Reusability with parameters and variables

Error handling and retry mechanisms

Integration with monitoring and alerts

📌 Example: A pipeline might extract data from an SQL Server, transform it using Mapping Data Flow, and load it into Azure Synapse Analytics.

Read More

Azure Data Factory Overview

 Data Integration & Orchestration

Dedicated SQL Pools

Azure Database for PostgreSQL

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Quality Thought Institute in Hyderabad




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