Monitor, Troubleshoot and Optimize Apache Spark Applications Using Microsoft Azure Databricks
We are super excited to announce our support for Azure Databricks! We continue to build out the capabilities of the Unravel Data Operations platform and specifically support for the Microsoft Azure data and AI ecosystem teams. The business and technical imperative to strategically and tactically architect the journey to cloud for your organization has never been stronger. Businesses are increasingly dependent on data for decision making and by extension the services and platforms such as Azure HDI and Azure Databricks that underpin these modern data applications.
The large scale industry adoption of Spark, and Cloud services from Azure and other platforms. represent the heart of the modern data operations program for the next decade. The combination of Microsoft and Databricks and resulting Azure Databricks offering is a natural response to deliver a deployment platform for AI, machine learning, and streaming data applications.
Spark has largely eclipsed Hadoop/MapReduce as the development paradigm of choice to develop a new generation of data applications that provide new insights and user experiences. Databricks has added a rich development and operations environment for running Apache Spark applications in the cloud, while Microsoft Azure has rapidly evolved into an enterprise favorite for migrating and running these new data applications in the cloud.
It is against this backdrop that Unravel announces support for the Azure Databricks platform to provide our AI-powered data operations solution for Spark applications and data pipelines running on Azure Databricks. While Azure Databricks provides a state of the art platform for developing and running Spark apps and data pipelines, Unravel provides the relentless monitoring, interrogating, modeling, learning, and guided tuning and troubleshooting to create the optimal conditions for Spark to perform and operate at its peak potential.
Unravel is able to ask and answer questions about Azure Databricks that are essential to provide the levels of intelligence that are required to:
- Provide a unified view across all of your Azure Databricks instances and workspaces
- Understand Spark runtime behavior and how it interacts with Azure infrastructure, and adjacent technologies like Apache Kafka
- Detect and avoid costly human error in configuration, tuning, and root cause analysis
- Accurately report cluster usage patterns and be able to adjust resource usage on the fly with Unravel insights
- Set and guarantee enterprise service levels, based on correlated operational metadata
The Unravel Platform is constantly learning and our training models adapting. The intelligence you glean from Unravel today continues to extend and adapt over time as application and user behaviors themselves change and adapt to new business demands. These in-built capabilities of the Unravel platform and our extensible APIs enable us to move fast to support customer demands to support an expanding range of Data and AI services such as Azure Databricks. More importantly though it provides the insights, recommendations and automation to assure your journey to cloud is accelerated and your ongoing Cloud operations is fully optimized for cost and performance.
Take the hassle out of managing data pipelines in the cloud
Read on to learn more about today’s news from Unravel.
Unravel Data Introduces AI-powered Data Operations Solution to Monitor, Troubleshoot and Optimize Apache Spark Applications Using Microsoft Azure Databricks
New Offering Enables Azure Databricks Customers to Quickly Operationalize Spark Data Engineering Workloads with Unprecedented Visibility and Radically Simpler Remediation of Failures and Slowdowns
PALO ALTO, Calif. – Sep. 4, 2019 —Unravel Data, the only data operations platform providing full-stack visibility and AI-powered recommendations to drive more reliable performance in modern data applications, today announced Unravel for Azure Databricks, a solution to deliver comprehensive monitoring, troubleshooting, and application performance management for Azure Databricks environments. The new offering leverages AI to enable Azure Databricks customers to significantly improve performance of Spark jobs while providing unprecedented visibility into runtime behavior, resource usage, and cloud costs.
“Spark, Azure, and Azure Databricks have become foundational technologies in the modern data stack landscape, with more and more Fortune 1000 organizations using them to build their modern data pipelines,” said Kunal Agarwal, CEO, Unravel Data. “Unravel is uniquely positioned to empower Azure Databricks customers to maximize the performance, reliability and return on investment of their Spark workloads.”
Unravel for Azure Databricks helps operationalize Spark apps on the platform: Azure Databricks customers will shorten the cycle of getting Spark applications into production by relying on the visibility, operational intelligence, and data driven insights and recommendations that only Unravel can provide. Users will enjoy greater productivity by eliminating the time spent on tedious, low value tasks such as log data collection, root cause analysis and application tuning.
“Unravel’s full-stack DataOps platform has already helped Azure customers get the most out of their cloud-based big data deployments. We’re excited to extend that relationship to Azure Databricks,” said Yatharth Gupta, principal group manager, Azure Data at Microsoft. “Unravel adds tremendous value by delivering an AI-powered solution for Azure Databricks customers that are looking to troubleshoot challenging operational issues and optimize cost and performance of their Azure Databricks workloads.”
Key features of Unravel for Azure Databricks include:
- Application Performance Management for Azure Databricks – Unravel delivers visibility and understanding of Spark applications, clusters, workflows, and the underlying software stack
- Automated root cause analysis of Spark apps – Unravel can automatically identify, diagnose, and remediate Spark jobs and the full Spark stack, achieving simpler and faster resolution of issues for Spark applications on Azure Databricks clusters
- Comprehensive reporting, alerting, and dashboards – Azure Databricks users can now enjoy detailed insights, plain-language recommendations, and a host of new dashboards, alerts, and reporting on chargeback accounting, cluster resource usage, Spark runtime behavior and much more.
Azure Databricks is a Spark-based analytics platform optimized for Microsoft Azure. Azure Databricks provides one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.
An early access release of Unravel for Azure Databricks available now.
About Unravel Data
Unravel radically simplifies the way businesses understand and optimize the performance of their modern data applications – and the complex pipelines that power those applications. Providing a unified view across the entire stack, Unravel’s data operations platform leverages AI, machine learning, and advanced analytics to offer actionable recommendations and automation for tuning, troubleshooting, and improving performance – both today and tomorrow. By operationalizing how you do data, Unravel’s solutions support modern big data leaders, including Kaiser Permanente, Adobe, Deutsche Bank, Wayfair, and Neustar. The company is headquartered in Palo Alto, California, and is backed by Menlo Ventures, GGV Capital, M12, Point72 Ventures, Harmony Partners, Data Elite Ventures, and Jyoti Bansal. To learn more, visit unraveldata.com.
The name Unravel Data is a trademark of Unravel Data™. Other trade names used in this document are the properties of their respective owners.
Jordan Tewell, 10Fold