Azure Databricks is an analytics platform optimized for the Microsoft Azure cloud service platform. Azure Databricks offers three environments for developing data-intensive applications: Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning.
With Azure Databricks, companies gain new insights and can build analytics solutions for artificial intelligence (AI). Users set up their Apache Spark™ environment in minutes, automatically scale it, and collaborate on collaborative projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries such as TensorFlow, PyTorch, and Scikit-Learn.
Azure Databricks offers three environments for developing data-intensive applications: Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning.
Databricks SQL provides an easy-to-use platform for analysts who want to create SQL queries against their data lake, create multiple visualization types to explore query results from different perspectives, and create and share dashboards.
Databrick’s Data Science & Engineering provides an interactive workspace that enables collaboration between data engineers, data scientists, and machine learning engineers. For a big data pipeline, raw or structured data is collected in batches through Azure Data Factory in Azure or streamed in near real time using Apache Kafka, Event Hub, or IoT Hub. This data is stored in a data lake (Azure Blob Storage or Azure Data Lake Storage) for the long term.
Databrick’s Machine Learning is an integrated, end-to-end machine learning environment with managed services for experiment tracking, model training, feature development and management, and feature and model deployment.
Get started quickly with an optimized Apache Spark environment
Azure Databricks includes the latest version of Apache Spark, allowing users to seamlessly integrate with open source libraries. Spinup clusters in a fully managed Apache Spark environment with the worldwide availability of Microsoft Azure. Azure Databricks sets up, configures, and then optimizes clusters to ensure superior reliability and performance without the need for monitoring. Azure Databricks provides auto-scaling and auto-stop to reduce overall costs.
Azure Databricks: Increase productivity with a shared workspace and many languages
With Azure Databricks, users work together effectively on an open, unified platform. They can run any analytics workload, whether they are data scientists, data technical professionals, or business analysts. Azure Databricks offers the possibility to work in the user’s preferred language – whether Python, Scala, R or SQL. Easily control notebook version control with GitHub and Azure DevOps.
Azure Databricks: Powerful machine learning capabilities for big data
With Azure Databricks, users can leverage complex automated machine learning capabilities thanks to the built-in Azure Machine Learning service to quickly determine appropriate algorithms and hyperparameters. This makes it easier for organizations to manage, monitor, and update machine learning models deployed from the cloud to the edge. Azure Machine Learning also provides a central registry for experiments, machine learning pipelines, and models.
Azure Databricks supports powerful, modern data warehousing
Azure Databricks supports the optimal analytics workflows for businesses, from combining all relevant data to gaining insights using analytics dashboards and reporting. The starting point is to automate data integration with Azure Data Factory, then load the data into Azure Data Lake Storage, transform and manage with Azure Databricks, and deploy it for analysis with Azure Synapse Analytics. This is how organizations modernize their data warehouse in the cloud for unmatched performance and scalability.
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The use of areto’s Microsoft Azure reference architecture provides customers with best practices for developing and operating reliable, secure, efficient and cost-effective systems in the cloud. Areto’s Microsoft Azure architecture solutions are consistently measured against Microsoft best practices in order to deliver the highest benefit to customers.
The areto Microsoft Azure reference architecture is based on five pillars: operational excellence, security, reliability, performance efficiency, cost optimization.
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ensure security, disaster recovery, business continuity as data is mirrored in multiple redundant locations.
efficient use of computer resources, scalability to meet short-term requirement peaks, sustainability
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Gartner, Magic Quadrant for Cloud Infrastructure & Platform Services, Raj Bala, Bob Gill, Dennis Smith, Kevin Ji, David Wright, 27 July 2021. Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
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