What is Azure Data Factory
Azure data factory was known as ELT (extract, transform, and load) tool. This tool takes data from various resources, convert it into meaningful data or information and load it to a destination such as data warehouse or data lakes. The tool can perform all steps that are needed from cleaning data before using it to making it useful content. It can do most of the work own itself for the organizations.
The Azure data factor is an ELT solution that has many components such as datasets, activities, pipelines, and triggers.
This tool is used for transformations activities and serverless activities such as
- Executing pipelines
- Visual data transformation logics
- continuous integration and delivery
- Developing code-free ETL processes in the cloud
Why do you need a Data Factory
If you are working on cloud-based projects then you will need data factors to work with. The Apache Air Flow can also do the same work, but it is a bit difficult to work on. You must know how to use python and also to create pipelines with it. On the other hand, the Azure data factory is the most efficient tool in the market at this point.
When you are working with cloud-based projects you have to transfer data across different platforms or networks and services (across data lakes, blob storage, and data warehouse).
Benefits of using Azure Data Factory
The Azure data factory work widely and the user can get data globally with data available in different 25 countries. All these tools and data is secured by Azure Security Infrastructure
This tool can manage all the drivers to integrate with SQLServer, Oracle, MySQL, and other data stores. Even though the tool is by Microsoft but it can easily work with any cloud such as GCP and AWS. And also has a wide range of primary tools such as Databricks.
Like other tools, this tool can create multiple users and assign various roles to them as well. The roles can be admin, contributor, and owner. For example, the billing information can only be viewed by the owner and the edit can contribute.
4. Cost optimization
As you can see using this tool is easy, highly automated, and required minimum human resources to work. First, you will need a solution architecture to fully pan the data process and second a developer to integrate the data factory according to the plan. So, you don’t have to hire a large team to work with the Azure data factory. It saves the expenses of both human resources and finances to attract more customers.
5. Enhanced productivity
The data factory is a complex ETL that can move, transform and load data to a new resource. This tool is advanced and highly automated that can easily analyze your data efficiently. The system takes less time for setups and more time to do its work deeply.
All the other requirements such as update, security, maintenance, and management of data factory. As a result, you will always have updated tools and technology.
Skills You Should Have to Work with Data Factory
Understanding of Azure
If you look at the official documentation, you will have hundreds of pages to read. But you will still need to understand some basic and important concepts such as the vaults key and access key. As a developer, you should familiar with these terms.
Understanding of Azure data migration
When you are using Azure Data Factory, it is simple to manage and orchestrate data flow. The skilled developer must understand all the logics and tools to process the order logically. For example, the developer must know that how frequently you must transfer your data.
Online Courses to Learn Azure Data Factory
The Azure data factory courses are available on different platforms. You can learn from any of them and start using the tool. But if you are interested in the best course then here is the link.
The Azure data factor is a scalable tool to integrate cloud data at all premises easily. And the tool can be easily used with all platforms. Further, machine learning can also be integrated with it.