Azure Data Factory vs Azure Pipelines

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Azure Data Factory vs Azure Pipelines: What are the differences?


Azure Data Factory and Azure Pipelines are both essential tools in the Microsoft Azure ecosystem that serve different purposes. While Azure Data Factory is a data integration and orchestration service, Azure Pipelines focuses on continuous integration and continuous delivery (CI/CD) of applications. Understanding the key differences between these two services is crucial for making informed decisions when designing and implementing data workflow solutions in Azure.

  1. Data Integration vs. Application Deployment: The main difference between Azure Data Factory and Azure Pipelines lies in their primary use cases. Azure Data Factory enables seamless data integration from various data sources and transforms the data to meet business requirements. On the other hand, Azure Pipelines facilitates the building, testing, and deploying of applications across multiple platforms and environments.

  2. Batch Processing vs. Continuous Deployment: Azure Data Factory predominantly focuses on batch processing and orchestration of data pipelines. It provides a scalable and reliable infrastructure for scheduling and executing complex data workflows. In contrast, Azure Pipelines is specifically designed for continuous deployment, allowing developers to automate application deployments and efficiently iterate through development cycles.

  3. Visual Workflow Design vs. Code-Based Pipeline Configuration: Azure Data Factory offers a visual designer that enables users to create and configure data pipelines without the need for coding. It provides a low-code/no-code approach for building and managing complex data integration workflows. Conversely, Azure Pipelines relies on code-based configuration using YAML or JSON syntax, providing more flexibility and customization options for defining CI/CD pipelines.

  4. Data Transformation and ETL vs. Application Build and Test: Azure Data Factory excels in data transformation and extraction, transformation, and loading (ETL) processes. It supports a wide range of data integration capabilities such as data mapping, data cleansing, and data format conversion. In contrast, Azure Pipelines focuses on application build, test, and deployment tasks, providing features essential for building, testing, and deploying software applications across different platforms.

  5. Seamless Integration with Azure Services vs. Broad Platform Support: Azure Data Factory integrates seamlessly with various Azure services, including Azure Databricks, Azure Synapse Analytics, and Azure Machine Learning. It provides native connectors and integration capabilities for ingesting and processing data from different sources. In contrast, Azure Pipelines offers broad platform support, allowing the deployment of applications to different platforms like Azure, AWS, and Google Cloud.

  6. Data Orchestration and Scheduling vs. CI/CD Pipeline Execution: Azure Data Factory excels in orchestrating complex data workflows and provides comprehensive scheduling capabilities for batch processing. It offers time-based triggers, event-based triggers, and dependency-based triggers for initiating data integration processes. In contrast, Azure Pipelines focuses on executing CI/CD pipelines, constantly checking for changes in the source code repositories and triggering the relevant stages of the pipeline accordingly.

In summary, Azure Data Factory and Azure Pipelines differ in terms of their primary use cases, focus areas, workflow design approaches, integration capabilities, and execution patterns. While Azure Data Factory specializes in data integration and orchestration, Azure Pipelines is geared towards application deployment and CI/CD workflows.

Advice on Azure Data Factory and Azure Pipelines
Vamshi Krishna
Data Engineer at Tata Consultancy Services · | 4 upvotes · 245.8K views

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Needs advice
Azure PipelinesAzure Pipelines

We are currently using Azure Pipelines for continous integration. Our applications are developed witn .NET framework. But when we look at the online Jenkins is the most widely used tool for continous integration. Can you please give me the advice which one is best to use for my case Azure pipeline or jenkins.

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Replies (1)

If your source code is on GitHub, also take a look at Github actions.

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    What is Azure Data Factory?

    It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

    What is Azure Pipelines?

    Fast builds with parallel jobs and test execution. Use container jobs to create consistent and reliable builds with the exact tools you need. Create new containers with ease and push them to any registry.

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    What are some alternatives to Azure Data Factory and Azure Pipelines?
    Azure Databricks
    Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
    It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.
    AWS Data Pipeline
    AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.
    AWS Glue
    A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
    Apache NiFi
    An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
    See all alternatives