Azure Databricks is a unified data analytics cloud service. It is a managed service that provides users with both infrastructure and tools for data analytics.
Designers based Azure Databricks on Apache Spark. It allows users to spin up clusters and build analytics in a fully managed Apache Spark environment.
Azure Databricks users don’t have to worry about provisioning the underlying infrastructure. When using it, you don’t have to manage upgrades, libraries, clusters, or any other tasks unrelated to driving insights from data.
Also Read: How to Monitor Azure Cloud Services?
How Is Azure Databricks Offered?
Azure Databricks provides the following environments for developing data-intensive applications:
Databricks SQL is an intuitive platform that allows users to operate a multi-cloud lake house architecture. Users can run SQL queries on their data lake and create various visualizations to explore different types of query results.
Databricks Data Science & Engineering
This environment provides an intuitive workspace with a unique collaboration between engineers and data scientists.
Azure Databricks reads data from storage locations such as Azure Data Lake and Azure Blob storage. Databricks then uses Spark to turn data from multiple sources into breakthrough insights.
Databricks Machine Learning
Databricks is an integrated ML environment that incorporates a range of managed services for model training, experiment tracking, and development.
Benefits of Azure Databricks
Azure Databricks is available for all organizations on Azure. It helps them leverage the power of data by combining it with machine learning and ELT processes. Here are some of the benefits of using Azure Databricks:
Azure fully manages Azure Databricks. Because the designers preconfigured Azure Databricks, you don’t have to maintain anything. It comes with a drag and drop interface that allows users to scale up or down.
Azure Databricks gives you a unique level of control. You can add or remove redundant Spark clusters whenever needed. Unlike using on-premise Apache Spark, this level of control allows you to save lots of money in the development process.
Azure Databricks uses enterprise-grade security and compliance with all Azure services, which makes it a highly secure data analytics platform. With Azure Databricks, you can run advanced data analytics workloads with a high-security posture.
Azure Databricks uses Azure Active Directory’s security framework. You can use your existing Azure AD credentials and existing security settings. The integration with Azure Active Directory means you can handle all identity management and role-based access within the same system. It helps protect crucial data without interrupting workflows.
Fast and Optimized for Performance
When performing data analytics, you need a high-performance platform that provides all practicability you need. Azure Databricks provides speed compared to other SQL analytics platforms. It offers substantial performance gains, up to eight times faster in querying, indexing, and catching.
With Azure Databricks, you can process terabytes of data in minutes. Besides, it provides almost 100% availability.
Seamless With Azure Stack
Azure Databricks integrates seamlessly with other Azure services to provide a well-coordinated analytics environment. It has native integration with storage and data analysis tools on Azure via connectors. Some of the apps it integrates with include:
- Microsoft Power BI
- Azure Blob Storage
- Azure Event Hub
- Azure Cosmos DB
- Azure SQL Data Warehouse
- Azure Data Lake Storage
- Apache Kafka for HDInsight
For instance, Azure Databricks integrates with Power BI to deliver excellent business intelligence visualizations and reports.
These integrations provide various advantages for advanced data analytics. Azure Databricks provides actionable insights on data analytics that non-technical staff can process easily. They also make Azure a highly desirable platform for mission-critical analytics workloads.
Suitable for Small Workloads
While business owners use Azure Databricks for large workloads and large amounts of data, you can also use it for smaller workloads. In addition, you can use it for small-scale workloads and other test/dev projects.
Use of Familiar Languages and Environment
Designers base Azure Databricks on Spark. It allows the use of common programming languages such as SQL, R, and Python. Users don’t have to learn complex, unfamiliar languages such as Scala. Besides, it also supports data science frameworks such as PyTorch, Scikit-Learn, and TensorFlow.
To understand any cloud service, you need to be familiar with its documentation. Since Databricks is a rather recent addition to Azure, well-crafted documentation comes in handy.
Azure Databricks has extensive documentation and support. There are two pieces of documentation that you can rely upon to understand the service and troubleshoot any errors.
The Bottom Line
Azure Databricks is a high-performance tool for all data analytics workloads. It is extremely powerful and easy to get started on and use. Given the integrations and the enterprise-grade security it provides, Azure is by far the best destination for Databricks workloads.
At nOps, we help Azure users with advanced visibility into their cloud environments. Designers based nOps on the Azure Well-Architected Framework to help users build secure, reliable, and high performance while keeping costs down.
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