Microsoft Azure is all about AI and analytics services. In addition, there is an excellent option for businesses who want to combine the advantages of big data and data analytics with cloud computing like azure. The Azure data factory lets businesses structure and unstructured higher volumes of data.

    Real-time analytics is one big thing about azure. It comes packed with real-time analytics and ultimately manages azure database services, machine learning and data engineering solutions, and analytics services.

    Unquestionably, Azure provides users with multiple services that assist them in setting up big data infrastructure, whether it is a database, analytics, data processing, machine learning, complex data integration, or others.

    • Databases

    The first and foremost benefit azure ensures businesses are the database option that lets them simplify their business workflows. The database option includes self-managed table storage and managed databases including Server, SQL, MySQL, PostgreSQL, and MariaDB.

    Azure comes packed with another incredible fully managed service known as Cosmos DB. It is truly a flexible and scalable service that enhances the deployment of various database engines.

    Azure ensures business the provision of SQL data warehouse for large-scale structured data, while unstructured data has an azure data lake for processing ahead.

    • Analytics

    Azure comes bundled with a wide range of analytics products and services. The most common ones are HDinsight and Azure analysis services which are discussed as under:


    HDinsight is an incredible azure service that works on open-source analytics. Moreover, it is highly compatible with platforms like Spark, Kafka, Apache, Hadoop, and others. It fully integrates with popular azure services like a data warehouse, SQL, and azure data lake. The analytics pipeline is created using the integration method of the above-mentioned services. Also, HDinsight is highly integrable with custom analysis tools and supports various languages like JavaScript, Python, .NET, and Scala.

    Analysis service

    Analysis service ensures businesses a large analytic engine that collects data from different sources and transforms it into a user-friendly BI model. It helps create unique dashboards and reports that are highly interactive. Also, the best thing about the service is that it does not require the urge to write complex codes or manage data processing.

    • Machine learning

    Azure data factory has excellent and readily available solutions for machine learning and artificial intelligence. Moreover, it lets users create custom machine-learning models efficiently.

    Also, machine learning tools are capable of automating machine learning with the help of tools like automated feature selection and algorithm selection.

    • Data Engineering

    Data engineering is further categorized into data factories and data catalogs. It exceptionally enhances and improves your business workflow.

    Data factory

    If businesses are looking for a serverless integration for local and cloud-based data repositories, then azure data factory is the right option. Azure big data lets users perform extract, transform, load (ETL) or extract, load, or transform (ELT). Azure provides more than 80 data connectors for this purpose.

    Data catalog

    Data catalog truly understands data sources. It has become incredibly easy to crowdsource metadata and annotations through a data catalog to the users while they can share their knowledge in response.

    Building big data solutions on Azure

    Microsoft suggests the primary steps that help build unique big data solutions in the azure cloud that include evaluation, architecture, and production.

    • Evaluation

    The priority for any business must be to evaluate the data before choosing a particular service. In addition, it is essential to understand better the type of data you want to make part of your plan and how to format it. You get different kinds of data from various sources. For instance, data from web scraping is entirely different from data through IoT sensors.

    When the buses reach the final stage, where they know what data they must process, they are all set to analyze it. In addition, it is unnecessary to take help from a data scientist, but you can use big data services as the most available option.

    • Architecture

    At first, you evaluate, then you need to define the architecture based on the evaluation results. Also, the architecture has a more significant relationship between legacy systems and the skills of your development and operation teams.

    • Production

    After selecting the desired services, you are ready to configure and prepare the production environment for your business. Again, your configuration heavily relies on the services you choose, hybrid or pure atmosphere, and the mixture of data sources.

    You don’t need to stop after the configuration process because you must monitor as many procedures as possible to achieve excellent performance and increased ROI.

    Final words

    Undoubtedly the Azure data factory and azure development are of great assistance to businesses. Moreover, it has many features that help companies to accomplish business processes with immense convenience.

    Azure big data lets businesses have accurate time analytics without any hassle to even out the business processes.


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