Excerpt: Microsoft Azure Data Factory is a form of cloud computing. It is called the future of data management because it is an emerging collection of cloud services. These days IT professionals and developers can use this platform to efficiently develop, deploy, and handle applications from anywhere in the global network of data centres. Azure Data Factory gives freedom to developers to create and deploy applications. Microsoft Azure also provides tools to practice the data management of the applications.
Table of contents:
- Why Is Azure Data Factory Important in Data Management?
- Important Data Management Activities of Azure Data Factory
- How Azure Data Factory Does the Data Management Work?
- Azure’s Key Components That Help in Data Management
- Reasons On Why Azure Data Factory Will Emerge as The Best Data Management Platform
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In technical words, Azure data factory is a data management gateway that acts as a client agent that the developer should install in his on-premises environment to copy existing data between cloud and on-premises stores. Data Factory supports these data stores and is listed under the Supported data sources section. This data management gateway can be scaled by linking multiple on-premises machines. Your data management can immensely improve with the help of Azure Data Factory.
Why Is Azure Data Factory Important in Data Management?
Azure Data Factory helps in accomplishing day-to-day operational work. For instance, a gaming company stores its log information to make decisions about it later. This log information gets stored in on-premises data storage, and the remaining information gets stored in the cloud. For analysing the stored data, we should have an intermediary job. A job that can store the information in one place. Then the consolidated data needs to be analysed by Hadoop on the cloud platform and SQL server on data storage premises. Let’s suppose that this process happens once a week.
Azure is a framework where the developer can build a workflow and get the data ingested from cloud stores and on-premises data stores. We can understand that Azure Data Factory is beneficial for developers and organizations for better data management.
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Important Data Management Activities of Azure Data Factory
- Azure is a platform based on the cloud, enabling data integration of different data stores. This data integration by Azure helps in gathering all the required information.
- It enables developers to build data-driven workflows for efficient data integration or management.
- These data-driven workflows are known as “pipelines” in Azure Data Factory.
- After collecting data or information from different data stores, the data can be transformed using processing tools such as Azure HDInsight Hadoop, Spark, and Azure Data Lake Analytics. Professionals can quickly analyse this transformed data.
So, Azure Data Factory is an EL (Extract and Load) tool that acts as Transform and Load platform. In Microsoft Azure, the data is consumed and created by time-based workflows. With the help of these parameters, the workflow can execute and do the desired work. For example, the workflow occurs on a daily or hourly basis. Settings can change it.
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How Azure Data Factory Does the Data Management Work?
Azure Data Factory manages the data using the pipeline. The data management process in Azure works in the following stages:
- Connect & Collect
Azure Data Factory connects many SaaS services and File sharing servers. After the Azure Data Factory secures the connection, it starts gathering the information or data from therein. It has on-premises sources and cloud storage as well. Azure Data Factory gathers data from all the connections and sources to make it available in one centralized place.
- Transform & Enrich
Azure Data Factory transforms all the collected data. Data transformation can be made using several methods, such as Data Lake Analytics, HDInsight Hadoop, and Machine Learning.
The transformed information is present in the local cloud space or local storage in the SQL form. This data is stored in centralized storage where analytical teams and BI can easily access and manage it.
Azure’s Key Components That Help in Data Management
Key components of the Azure Data Factory that enables seamless data management are:
- Pipelines are a group of activities that helps integrate the applications’ data. Azure Data Factory generally has many pipelines, and it is optional only to have one pipeline in one data factory.
- Activities Activities are the functions of the Azure Data Factory. The group of these activities is called pipelines in the data factory. For instance, a copy activity only copies data from one data store to another.
Azure Data Factory has two types of activities:
- Data movement activities
- Data transformation activities
Reasons On Why Azure Data Factory Will Emerge as The Best Data Management Platform
- Azure Data Factory is a cloud-based Microsoft data management solution. We know that Microsoft has a wide range of enterprise software and cloud-based platforms with many tools and resources, which can be beneficial for the developer working on Azure, which is also an offering by Microsoft.
- In recent years, Microsoft has been focussing on cloud-based frameworks like Azure Data Factory to grow its business even more.
- Organizations prefer frameworks like Azure for better data management because they also understand the significance of AI and machine learning in product management.
- Azure Data Factory offers more than two hundred different products and services on global infrastructure made of data centres in many countries.
- Azure Data Factory makes up 38% of the total operating income of Microsoft based on the latest data. It was launched in 2010 and is gradually becoming a popular and successful cloud-based platform.
- Other than Microsoft’s services, its partners also offer a wide range of services and tools as the market of Azure Data Factory is constantly growing.
- Azure Data Factory is one of the leading data management platforms in the world.
- Azure Data Factory has competitive and broad services in the analytics and data space. It gives specialized PaaS and SaaS solutions.
- Azure offers hybrid data integration as well.
- All capabilities of Azure can be automated because these are exposed with the help of APIs.
- Azure supports DataOps and DevOps scenarios where automation is very crucial.
- Azure supports the working of other data products of Microsoft, like Azure Synapse, Azure SQL Server, Azure Machine Learning, and Azure Data Lake.
- It gives more than 90 built-in connectors for developers and enterprises.
- Azure Data Factory manages everything related to ETL and ELT methods.
- It integrates data from different systems and data stores.
- Azure has many in-built capabilities that help in data processing and management.
- Azure Data Factory has many features that cause data asset discovery quite straightforwardly.
- Azure is a fully-managed service that helps analysts, data scientists, and developers register, enrich, discover, understand, and consume various data sources.
- Azure helps developers to choose tools and find the data they are looking for. A data catalogue helps to store data at the desired place and lets developers discover and work with it the way they want. Its architecture gives an intuitive user experience.
- It helps you get tips, tricks, and rules to get better service.
- It helps you find hidden or dark data and reduces your work.
- It helps in reducing your time on finding data.
- Azure gives more value or results in less time.
- Azure helps in unlocking the potential of the data.
- Azure also stores tribal knowledge to make data easier to understand.
- It allows every development team member, IT professional, and organization to give their insights and helps bridge the gap between them.
- Azure provides better connecting tools as compared to other competitors.
- It also prevents your registered data assets from being discovered by anyone else.
- It enables easy data integration with open REST APIs. The integration goes into existing processes and tools.
- It allows modern SQL coding experience. This coding makes the developer’s work easy because it has many built-in features like SQL editor, multiple tab windows, IntelliSense, code snippets, code navigation, keyword completion, and source control integration.
- The SQL editor Azure provides helps organize, edit, and browse database objects.
- Azure can create highly customized dashboards to check and troubleshoot performance bottlenecks of different databases.
- It also allows you to choose command-line tools of your preference.
- Azure Data Factory gives extensibility points for the best data management functions.
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Azure Data Factory of Microsoft is a cloud-based and cross-based platform. It is a database tool for developers and data professionals. It is best for those professionals who use on-premises and cloud-based frameworks on macOS, Linux, and Windows. It gives you advanced editing experience using IntelliSense, source control integration, code snippets, and an efficiently integrated terminal.
Hence, Azure Data Factory is a leading cloud framework in the tech industry worldwide. It reduces the operational tasks of developers and businesses by integrating and managing data from different sources. It also provides much-needed information in a unified way and one central place. It will undoubtedly become more popular and widely used worldwide as its features and components provide excellent data management.