Azure Data Factory

what is azure data factory?

Azure Data Factory is a Cloud data integration service that enables organizations to collect, transform and load data from various sources to destinations such as databases, data warehouses, data lakes and lakehouses. It enables users to design and manage end-to-end data workflows, including planning, orchestrating and monitoring data processing activities.

application integration versus data integration.

Before we delve deeper into the components of Azure Data Factory, it is important to understand the difference between application integration and data integration.

Application integration is connecting different applications to share data with each other. This includes real-time message exchange, data synchronization and coordination of business processes between applications.

Data integration focuses on collecting, transforming and loading data from various sources into a central environment for storage and analysis. This requires data to be extracted from various systems, transformed to the right format and loaded into the target system. Usually we are talking here about so-called ETLs & ELTs(Extract, Load, Transform).

Azure Data Factory focuses primarily on data integration and provides a range of tools and capabilities to design and execute data workflows.

components of azure data factory.

Data Pipelines and Data Flows form the basis of Azure Data Factory, which can be used to create and manage data integrations.

data pipelines.

Pipelines in Azure Data Factory enable users to orchestrate and plan data integrations, allowing complex workflows to be created and managed.

A pipeline in Azure Data Factory is a logical collection of activities used to move, transform and load data from one source to another. It provides a visual interface that allows users to build data workflows by configuring and connecting activities.

Key features and benefits of ADF pipelines include:

1. Visual (low code) interface: Azure Data Factory's low code interface lets you build pipelines by dragging and dropping activities. Then these activities can be linked and configured. This simplifies the development process and makes it easy to understand your data flows.
2. Wide range of standard connectors: Azure Data Factory has a comprehensive set of standard connectors for popular services and systems. This means you can easily exchange data with various sources and destinations, such as databases, cloud storage, SaaS applications and more. This allows you to quickly start integrating your data without worrying about complex details.
3. Triggers for automated processing: Azure Data Factory lets you set triggers to start pipelines automatically based on events, time schedules or external signals.
4. Monitoring and management: Azure Data Factory provides comprehensive monitoring and management capabilities to track the performance of your pipelines and identify problems. You can check the status of your data workflows, receive errors and alerts, and analyze detailed logs. This helps you optimize your data processing and proactively resolve any issues.
5. On-premises and cloud integration: Azure Data Factory allows you to achieve seamless integration between on-premises and cloud environments. This can be achieved by installing a self-hosted integration runtime within your local network or by using various Azure VPN services.

In short, ADF pipelines is an essential component of Azure Data Factory that allows users to design, manage and execute data integrations. It provides an intuitive design interface, data movement and transformation, orchestration and scheduling, flexibility and reusability, as well as extensive monitoring and management capabilities.

data flows.

In addition to pipelines, Azure Data Factory also offers a powerful feature called Data Flows. With Data Flows, users can design and execute data transformations at scale without the need for coding.

ADF Data Flows also uses a visual (low-code) interface. In this interface, you can add transformation, validation and aggregation steps to manipulate data. This simplifies and speeds up the process of data transformation, even for users without extensive programming knowledge.

The benefits of ADF Data Flows include:

1. Simple (low code) interface: The Data Flow Editor provides an intuitive interface that allows you to design transformations by simply adding and configuring the desired steps.
2. Scalability: ADF Data Flows is designed to work with large amounts of data. It can automatically scale to enable parallel data processing, allowing it to handle most demanding data sets quickly and efficiently.
3. Reusability and modularity: ADF Data Flows allow users to reuse transformation steps in different Pipelines, reducing development time and increasing consistency.
4. Data Validation: ADF Data Flows allows users with a Data Assert transformation to set rules that data must meet. This can ensure data quality. Not only can validations be set at the rule level, but it is also possible to check for duplicate values in a dataset. In case of errors, actions can be executed, allowing proactive monitoring of integrations.
5. Debugging and viewing data: ADF Data Flows provides built-in capabilities to live debug and view your data at every step of the process. This allows you to develop more accurately and efficiently.

azure synapse analytics & microsoft fabric.

Two other products within the Microsoft ecosystem to mention are Azure Synapse Analytics and Microsoft Fabric.

azure synapse analytics.

Azure Synapse Analytics is an integrated analytics service that brings all your data queries together in a combined product. This means that ADF is a part of Synapse, which is referred to as the Synapse Data Pipelines & Synapse Data Flows.

In addition to the features of ADF, Synapse offers the following components in an integrated solution:

• Create and manage SQL databases
• Using Apache Spark notebooks to analyze, transform and train AI models
• Creating Power BI reports

This makes Synapse a great solution for business users, data scientist and data engineers.

microsoft fabric.

Microsoft Fabric is the latest solution that Microsoft announced at the annual Microsoft Build event in 2023. This is currently in preview and is an evolution of the capabilities offered by Synapse.

In Microsoft Fabric, we again find Data Factory with many of the features as we know them.

differences between azure synapse analytics & microsoft fabric.

The main differences between Synapse & Microsoft Fabric are:

1. OneLake: In OneLake, all data from the entire organization is stored. OneLake is the OneDrive for data. This means you can easily share your data and it is stored in an open standard. The goal of OneLake is to eliminate unnecessary data movement.
2. SaaS: Microsoft Fabric is a SaaS service, like Office365 & Power Automate. This means you can get started within 5 minutes and you don't have to create (Azure) resources yourself. In addition, the cost structure is more transparent because you don't have to pay for all the separate services, but pay 1 combined price.
3. AI (GPT-4): In Fabric, you can have chat conversations with copilot, who can help you create PowerBI reports and answer questions about your data.