Databricks Runtime is the core of the Databricks Unified Analytics Platform.
Built on top of a highly-optimized Spark cluster, it increases data processing performance by up to 5x.
Leverages a vertically integrated stack to optimize the I/O layer and processing layer to significantly improve performance of Spark in the cloud.
A serverless architecture that democratizes infrastructure through the auto-configuration and scaling of compute resources — enabling best-in-class performance at dramatically lower costs.
A cloud-native platform that abstracts the complexities of big data infrastructure, resulting in a highly elastic, reliable and performant platform to build innovative products.
Databricks helped us deliver a new feature to market while improving the performance of the data pipeline ten-fold. Today, it powers our entire production pipeline with multi-terabyte Spark clusters.
Foster collaboration and sharing of insights in real time within and across data engineering, data science, and the business with an interactive workspace.
A unified platform that streamlines end-to-end workflows from data ingest and ETL, to data exploration and model building, to productionizing models and data products.
Move seamlessly across various types of analytics including batch, ad hoc, machine learning, deep learning, stream processing, and graph.
Plug into a wide variety of AWS tools and data stores with built-in connectors and integrate with other data engineering services to facilitate CI/CD with comprehensive APIs.
Having an agile innovation workflow is critical for McGraw-Hill Education. Databricks Unified Analytics Platform is at the center of our ecosystem and underpins our innovation pipeline and workflows.
Benefit from best-in-class protection at rest and in motion.
Tap into comprehensive audit logs to monitor and troubleshoot issues.
Fine-grained management access to every component of the enterprise data infrastructure, including files, clusters, code, application deployments, and dashboards.
Seamless integration with enterprise identity providers via SAML 2.0 and Active Directory.
Unparalleled support by the team that started the Spark research project at UC Berkeley, which later became Apache Spark.
Innovate faster with Databricks and Spark with solution architecting and workload optimization services.
Around-the-clock coverage to ensure problems are resolved quickly, with response times as fast as one hour for production tier support.
Online library of documentation, best practices, user guides, and other technical resources.
Databricks’ quality of support and how they’ve helped our team succeed is absolutely crucial for our business.
Cloud-optimized clusters allow you to complete jobs in a shorter time, reducing cloud compute costs.
Further reduce costs by avoiding the time-consuming tasks to build, configure, and maintain complex Spark infrastructure.
Billing up to the nearest second keeps your costs down.
Lower price point for data engineering production workloads.
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Databricks is our go-to-system for anything requiring deep data processing and analysis. In just a short amount of time, we have been able to increase our data processing speeds by a factor of four without any added operational costs.
