Microsoft Azure Data Factory (ADF) is Azure’s ETL tool. ETL stands for Extract, Transform, and Load. This system allows for simple, seamless data migration between physical and cloud-based servers. It’s particularly helpful for unstructured data or reorganizing data from legacy systems.
ADF also lets users construct workflows to import their data. This way, you can specify exactly which data it collects, how it’s changed, and where it ends up. Having this level of control will result in better reports and better insights.
To learn more about how you can benefit from Azure Data Factory, read on. We’ll discuss how it works and how it helps so you can easily get started.
How Does Azure Data Factory Work?
Azure Data Factory operates on the concept of pipelines. A pipeline is a logical group of activities that together perform a task. The activities in a pipeline define the actions to perform on your data. Once a user creates a pipeline, ADF follows 4 steps.
The software ingests data from the sources specified in the pipeline. ADF can gather data from multiple sources simultaneously, including a mixture of on-premise and cloud-based data. This is excellent for data consolidation purposes.
No matter where the data came from, ADF can transform it to make it more useful. How it transforms depends on your specifications in the pipeline. Some of the things it can do include:
- Cleaning the data
- Joining data from different sources
- Aggregating the data
- Ordering the data based on certain criteria
- Splitting the data into multiple streams
Once the data is transformed, ADF publishes it to your specified destination. The most common Azure data pipeline runs from one server to another. However, you may also use it to transfer data to your business intelligence platform, online analytical processing (OLAP) cubes, or data marts.
Azure Data Factory comes with built-in tools that monitor the health, performance, and progress of your data pipelines. You may also set up alerts that notify you during specific stages of the data transfer or if it fails.
Key Components of Azure Data Factory
Like any other software tool, ADF consists of several key components that a user can interact with. Here is a list of the primary ones.
As mentioned, ADF’s functionality revolves around pipelines. Your data factory can run multiple pipelines at once. Each pipeline follows your specifications on how it should interact with the data.
Every pipeline is a group of activities. Each activity represents a step in your data transfer process. For instance, you may add a “copy data” activity to your pipeline if you would like to copy something from one data store to another.
Datasets are simply your point of reference to the data that you want to use in your activities. These datasets can be used either as an input or an output.
Inputs refer to the data your activity consumes and outputs refer to the data it produces. In the previously mentioned “copy data” example, the input would be the data you want to copy and the output would be where you want to put it.
Linked services define the connection information that ADF needs to link to external sources. A linked service doesn’t hold any data itself, it simply behaves as a bridge between ADF and another source.
Information on a linked service includes:
- The type of resource connected
- Connection details for the resource (e.g. URL or account name)
- Authentication information (e.g. username/password or encryption keys)
- Triggers specify when data pipelines begin. You may set a different schedule for every pipeline depending on when data movement needs to happen. Pipelines can be triggered immediately, on a specific wall-clock time, or on a regular schedule.
Benefits of Azure Data Factory
As a serverless tool, ADF doesn’t require any hardware setups. This saves time and money on procurement and labor. This also makes it ideal for handling large volumes of data from distant servers.
Azure Data Factory supports a wide range of data sources and integration patterns. This includes SQL server data, blob storage, structured, unstructured, batch, and real-time data.
Ease of Use
Once you’re up and running, ADF is fairly easy to use. Its UI is intuitive and integrations seamless without much additional setup. While it does take some training, it is overall a very user-friendly software tool.
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Security & Compliance
ADF follows Microsoft’s security standards. This applies to data flows during migration and data at rest. Additionally, Data Factory is automatically integrated with Azure Active Directory for access management purposes. These built-in features make it ideal for companies that must follow regulatory compliance standards.
Azure Data Factory provides visual monitoring with Azure Monitor and Azure Log Analytics. As a result, it’s easy for users to quickly detect any unexpected events as they process data. This lets the user promptly address and mitigate any issues instead of letting them fester and hinder the process.
Make The Most of Azure Data Factory With Help From Experts
Data Factory is just one of many useful features that come with Microsoft Azure. Getting to know the tool will help, but expert Azure data services can make it even better.
Atmosera offers managed Azure services to business owners across industries. We’re seasoned Data Factory users, but we’re also proficient in other Azure tools. You can ask us for help with Data Lakes, Azure SQL database, your Azure portal, or anything else.