Azure Databricks is a fully managed, cloud-based Big Data and Machine Learning platform, which empowers developers to accelerate AI and innovation by simplifying the process of building enterprise-grade production data applications. Built as a joint effort by the team that started Apache Spark and Microsoft, Azure Databricks provides data science and engineering teams with a single platform for Big Data processing and Machine Learning.
By combining the power of Databricks, an end-to-end, managed Apache Spark platform optimized for the cloud, with the enterprise scale and security of Microsoft's Azure platform, Azure Databricks makes it simple to run large-scale Spark workloads.
Optimized environment
To address the problems seen on other Big Data platforms, Azure Databricks was optimized from the ground up, with a focus on performance and cost-efficiency in the cloud. The Databricks Runtime adds several key capabilities to Apache Spark workloads that can increase performance and reduce costs by as much as 10-100x when running on Azure, including:
High-speed connectors to Azure storage services, such as Azure Blob Store and Azure Data Lake
Auto-scaling and auto-termination of Spark clusters to minimize costs
Caching
Indexing
Advanced query optimization
By providing an optimized, easy to provision and configure environment, Azure Databricks gives developers a performant, cost-effective platform that enables them to spend more time building applications, and less time focused on managing clusters and infrastructure.
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