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Apache Spark, is an open source, unified analytics engine built for big data and machine learning. The founders of Apache Spark designed Databricks to provide simple setup, workflows and an interactive workspace to enable collaboration between business analysts, data engineers and data scientists to explore and visualize data.
If you’re interested in implementing a Databricks into your BI solution
Build a fast, simple, and scalable data warehouse that eliminates the need to invest in costly ETL pipelines and scales on- demand, revolutionizing the way data teams analyze their data sets
Databricks provides a unified analytics platform that unifies data science, engineering and business with an extensive library of machine learning algorithms, interactive notebooks to build and train
Find actionable insights by collecting and analyzing high-velocity sensor and time-series data in real-time.
A new Apache Spark environment can be launched in minutes and integrate with other Azure services
Instantly scale up or down to either reducing costs or gain extra capacity whenr equired.
Your data and business are protected with Azure Active Directory integration, allowing for role-based control and peace of mind. User permissions also enables secure access to Databricks notebooks, clusters, jobs and data.
Choose from several languages such as Python, Scala, R and SQL.
An interactive workspace enables data engineers, data scientists, and business users to collaborate and comment on shared projects as a team.
Integrate with a wide variety of data stores and services such as Azure SQL Data Warehouse, Azure Cosmos DB, Azure Data Lake Storage, Azure Event Hubs, and Azure Data Factory. Enable single sign-on with Azure AD to unlock role-based controls. Discover and share your insights quickly and easily by connecting to Azure Databricks with Power BI.
Build, train, and deploy AI models using GPU-enabled clusters. Machine learning comes preinstalled and preconfigured with deep learning frameworks and libraries such as TensorFlow, Keras, and XGBoost.
Azure Databricks supports languages such as Python, Scala, R, and SQL so you can use your existing skills to start building and leverage a comprehensive set of analytics technologies including SQL, Streaming, MLlib, and GraphX.