Summary: Azure Databricks is a fast, secure, and collaborative Apache Spark-based platform that allows data scientists, data engineers, and data analysts to collaborate efficiently and work effectively through a single interface. Here are the reasons that make Databricks an ideal solution for managing data science and big data workloads.
Azure Databricks, the Cloud-based environment built for data scientists, data analysts, and data engineering, is the result of a partnership between Apache Spark and Microsoft. Also known as Apache Spark powerhouse, it allows business analysts and data experts to perform interactively and efficiently by building models and deploying workflows using the platform.
Simply put, Azure Databricks is an excellent platform for Data Science and Big Data workloads due to the unparalleled benefits that it offers to the expert professionals in these domains. It comprises complete open-source Apache Spark capabilities and technologies.
In 2020, the volume of big data worldwide will increase to around 44 zettabytes from the existing 4.4 zettabytes. Can you ever imagine 44 zettabytes – that amounts to 44 trillion gigabytes. Isn’t it mind-blowing?
However, what we call big data only makes up 10 percent of the overall data available to the enterprises, whereas the remaining 90 percent is unstructured data. This is what makes big data analytics tools like Apache Spark essential for businesses.
These tools possess the capability to work across massive clusters of databases and servers for exploring data efficiently.
- Speed- If you have ever had a chance to work with Apache Spark, you must be familiar with the speed capabilities that make it 100 times faster than Hadoop MapReduce when running in-memory and 10 times faster on disk. Believe it; Azure Databricks is amazingly faster than Apache Spark.
- Security- Databricks integrates directly with AAD (Azure Active Directory) and that with no custom configurations. Once you create the Azure Databricks service and initialize the Databricks workspace, you can go to the workspace URL and login with AAD credentials.
- Collaboration- Collaboration is certainly an important reason to choose Databricks for data engineering and data science workloads. It provides a comprehensive platform that allows data engineers, data analysts, and data scientists to share workspace, jobs, and clusters easily using a single interface.
Azure Databricks helps enterprises innovate more efficiently and effectively on top of big data. Here are some key reasons that explain why Databricks is an ideal solution for managing your big data workloads.
Databricks Allows for Easy Big Data Collaboration & Integration
Azure Databricks offers a robust platform with native integration with helpful storage and data analysis tools via connectors on the Microsoft Cloud platform. Databricks support includes Azure Cosmos DB, Azure Blob Storage, Azure Event Hub, Azure Data Lake Storage (ADLS), Microsoft Power BI, Azure SQL Data Warehouse, Apache Kafka for HDInsight.
Integration with these services proves to be a significant advantage for data experts as they help deliver actionable insights in a format that non-data professionals such as business executives, sales staff, and marketers can easily understand.
Using Databricks, engineers can develop, clone, and modify unstructured data clusters and turn them into significant jobs. The resulted data can then be handed over to data analysts and data scientists for further review. In short, Azure Databricks broadens the possibilities for data analysis.
Data scientists can explore jobs for gaining insights as well as for running advanced analyses on the same chunk of data in one interface – while Azure Databricks auto-scales with Cloud for minimizing total computing resources in use for optimizing the performance.
Databricks is Managed by Azure & Incorporates Apache Spark’s Features
Databricks is powered with all the robust features of Apache Spark that make it even powerful at the infrastructure level. Azure fully manages the platform. It doesn’t demand maintenance as the system is pre-configured. The infrastructure is easy to scale up and scale down using a ‘drag-and-drop’ interface.
Databricks allows users to auto-terminate the cluster based on inactivity. Moreover, users can eliminate redundant Spark clusters when they don’t need them anymore. This outstanding level of control over the clusters allows organizations to save significant resources and time in the development phase.
Databricks is Powerful, Safe, and Protected with Azure
Azure Databricks is a robust platform that uses enterprise-grade security and compliance available to services on Microsoft Azure. It integrates the Azure Active Directory (ADD) security framework, which means that the administrator can handle all of the organization’s identity management, security, and role-based access. The administrator is responsible for protecting the corporate’s big-data without interrupting ongoing workflow.
Admins can use the Admin Console for adding, managing, and deleting users. Moreover, admins can invite users that are not in the same Azure Active Directory for collaboration possibilities.
Databricks is Fast, Optimized and Enhanced for Maximize Performance
Apache Spark is considered for outstanding speed, and Databricks is known for its enterprise-grade performances. It offers business efficiency gains with 8x performance in indexing, advanced querying, and caching in comparison to other significant big data analytics solutions.
Azure Databricks possess the capability to process terabytes of data volumes in just a few minutes. The entire data that the user explores, shares, and manages aggregates with Databricks is backed by Microsoft’s Cloud Service Level Agreements for assured uptime and maximum connectivity.
Implement Azure Databricks to Transform Digitally
Azure Databricks is a versatile Azure-based service that enables and empowers data experts to efficiently examine big data workloads.
As the data continues to grow rapidly in volume, enterprises are facing daunting challenges that include:
- Poor collaboration in team members across the organization
- Spending massive time maintaining infrastructure rather than data
- Cost and complexity involved in training staff about the new technology
Azure Databricks comes as a robust platform that allows data professionals to collaborate using a single platform. Moreover, the IT teams do not have to manage the platform as the entire infrastructure is managed and maintained by Azure.