The accumulation of vast volumes of data has become pretty natural in the modern digital landscape, especially for large-scale organizations. However, several of these companies face troubles in deriving business value from these expansive sets of unstructured data. Fortunately, with technological advancements and innovation, the solution to this problem has also come up. There are analytics tools available today that make inferring intelligence, analytics and insights from big data a lot more streamlined and smoother than ever. Azure Databricks is one of the most powerful analytics tools among them. It is a highly competent analytics platform available on the Azure Cloud, and can be considered to be a version of Apache Spark that is cloud-optimized.
What is Azure Databricks?
Azure Databricks essentially is an analytics platform based on Apache Spark, which is built on top of Microsoft Azure. This platform can be used to process hefty data workloads, while also enabling collaboration between data engineers, data scientists and business analysts. These professionals work towards deriving actionable insights with the help of its interactive workspace, one-click setup and varied range of functionalities.
Apache Spark has always been quite fast. As Azure Databricks is based on an open-source unified analytics engine, it also is optimized for maximum productivity and performance. Being entirely managed by Azure, the Databricks system is predesigned. Therefore, companies need not spend any effort or resources on its maintenance. Its ‘drag and drop’ interface additionally makes scaling the system up and down very simple. Azure Databricks also makes big data integration and collaboration easier with useful data analysis and native integration. It is one of the safest platforms to make use of enterprise-grade compliance.
Key features of Azure Databricks:
- Optimized Apache Spark environment: Azure Databricks comes with a dependable and secure production environment that is supported and managed by Spark experts. This system allows users to effectively integrate with open source libraries by offering up-to-date versions of Apache Spark. Through Databricks, one can always explore a zero-management cloud platform that comes equipped with wholly-managed Spark clusters. It also provides the users an innovative platform for powering up their requited Spark-based applications, alongside an interactive workspace for visualization and exploration.
- Interactive workspace: The notebook and interactive workspace experience of Azure Databricks can be highly effective in helping the users to collaborate competently and level up their productivity quotient. This interactive workspace especially helps data engineers, business analysts and data scientists to perform their tasks more capably. The process of exploring and prototyping data, while running applications in Spark that are data driven, is accelerated with the integrated and collaborative environment of Azure Databricks.
- Databricks Runtime: Azure Databricks is developed for the Azure Cloud natively. This server-less option aids data scientists to iterate in a swift manner as a team, subsequent to eradicating the need of specialized expertise for setting up and configuring data infrastructure, as well as entirely removing infrastructure complexities.
- Machine Learning Integration: With rich integration with Power BI, Azure Databricks enables people to discover and share valuable insights easily. By leveraging the integrated Azure Machine Learning, one can access advanced automated ML capabilities, and determine hyper-parameters and algorithms effectively. This platform also provides a central registry for ML pipelines, models and experiments.
Clusters are set up, configured and fine-tuned in the Azure Databricks to provide assurance of high reliability without needing any monitoring. It also helps the users to take advantage of auto-termination and auto-scaling to improve the total cost of ownership (TCO). Databricks breaks down silos between data scientists and engineers, allowing each of them to work on the code at the same time across all components of ML, ETL and more.