Microsoft Azure Databricks Standard Data Analytics
Microsoft Azure Databricks Standard provides a unified platform for data engineering, data science, and machine learning, enabling advanced analytics for businesses.
- Unified Analytics: Access a collaborative environment for data preparation, model training, and deployment.
- Scalable Performance: Process and analyze large datasets efficiently with auto-scaling compute resources.
- Integrated Tools: Utilize familiar tools and languages like SQL, Python, Scala, and R within a managed platform.
- Cost Efficiency: Pay-as-you-go licensing model based on Databricks Units (DBUs) per hour for flexible resource utilization.
Product Overview
Product Overview
Microsoft Azure Databricks Standard is a cloud-based, Apache Spark-based analytics platform designed to accelerate data engineering, data science, and machine learning workloads.
This platform is ideal for IT professionals and data analysts within SMB and mid-market companies who need to process vast amounts of data, build predictive models, and derive business intelligence from their operational data.
- Collaborative Workspace: Facilitates teamwork among data engineers, data scientists, and analysts.
- Optimized Spark Engine: Delivers high performance for complex data processing and analytics tasks.
- Managed Infrastructure: Reduces the operational burden of managing Spark clusters and infrastructure.
- Integration with Azure: Seamlessly connects with other Azure services for a comprehensive data ecosystem.
- Flexible Licensing: Subscription-based licensing with Databricks Units (DBUs) per hour allows for granular cost control.
Azure Databricks Standard empowers SMB and mid-market teams to perform sophisticated data analysis without the overhead of managing complex infrastructure.
What This Enables
Enable Advanced Data Processing and Analysis
Enable teams to ingest, transform, and analyze large volumes of structured and unstructured data using a unified platform. Streamline complex data pipelines for business intelligence and reporting.
cloud-native deployments, hybrid cloud environments, data warehousing modernization, big data analytics initiatives
Accelerate Machine Learning Model Development
Streamline the end-to-end machine learning lifecycle, from data preparation and feature engineering to model training and deployment. Automate model retraining and monitoring for continuous improvement.
predictive analytics projects, AI-driven application development, data science experimentation, operational forecasting
Facilitate Collaborative Data Science Workflows
Provide a shared workspace for data engineers, data scientists, and analysts to collaborate on projects. Improve team productivity through shared notebooks, version control, and access management.
cross-functional data teams, research and development initiatives, data-driven product development, knowledge sharing environments
Key Features
Apache Spark-based engine
Achieve high-performance data processing and analytics for complex workloads.
Collaborative notebooks
Enable seamless teamwork and knowledge sharing among data professionals.
Auto-scaling clusters
Ensure optimal performance and cost efficiency by automatically adjusting compute resources.
Managed platform
Reduce operational overhead by offloading infrastructure management to Microsoft.
Integration with Azure services
Build a comprehensive data ecosystem by connecting with other Azure data and AI services.
Industry Applications
Finance & Insurance
Enables financial institutions to perform complex risk analysis, fraud detection, and customer behavior modeling using large datasets, supporting regulatory compliance and strategic decision-making.
Healthcare & Life Sciences
Supports the analysis of patient data for improved diagnostics, personalized treatment plans, and operational efficiency, while adhering to strict data privacy regulations like HIPAA.
Retail & Hospitality
Facilitates customer segmentation, demand forecasting, and personalized marketing campaigns by analyzing sales, inventory, and customer interaction data.
Manufacturing & Industrial
Enables predictive maintenance, supply chain optimization, and quality control through the analysis of sensor data, production logs, and operational metrics.
Frequently Asked Questions
What is a Databricks Unit (DBU)?
A Databricks Unit (DBU) is a normalized unit of processing capability on the Databricks platform. The number of DBUs consumed per hour depends on the type of Databricks Runtime and the virtual machine size used for your cluster.
How does Azure Databricks licensing work?
Azure Databricks is licensed based on Databricks Units (DBUs) consumed per hour. You pay for the DBUs used by your clusters, providing a flexible, pay-as-you-go model that scales with your usage.
Can I use my existing SQL, Python, or Scala skills with Azure Databricks?
Yes, Azure Databricks is built on Apache Spark and supports SQL, Python, Scala, and R. You can leverage your existing programming skills and tools within the platform.
Deployment & Support
Deployment Complexity
Medium — IT-assisted
Fulfillment
Digital Delivery
License keys / portal provisioning
Support Model
Zent Networks Managed
Renewal, add-license, and lifecycle management included
Subscription Terms
Cancellation
Cancel anytime — no charge on next cycle
You may cancel this subscription at any time. Cancellation takes effect at the end of the current billing period. You will not be charged for the following billing cycle. Access remains active through the end of the paid term.
Returns
Subscription licenses are non-refundable
Digital software licenses and SaaS subscriptions cannot be returned once activated or provisioned. Contact a Zent Networks account manager if you have questions before purchasing.