Databricks observability gives you complete visibility into how your data pipelines and systems are performing across your entire data stack. Modern Databricks data teams need data observability tools because optimizing data reliability, pipeline performance, and spending manually is nearly impossible with today’s complex, distributed environments. A solid Databricks data observability platform helps teams catch problems before they hurt business operations, optimize how resources get used, and make sure reliable data reaches decision-makers when they need it.

Unravel’s data observability platform works perfectly for data-driven enterprises looking to optimize data performance, speed up data-driven insights, and optimize cloud spending. Our data observability software is built for organizations that depend on analytics from complex data pipelines handling massive amounts of data across modern, intricate data stacks. What separates us from other Databricks data observability companies is our AI-native approach that goes beyond monitoring to provide actionable automation and optimization.

Unravel’s AI-enabled Databricks data observability platform offers hundreds of powerful capabilities that help data teams troubleshoot issues, optimize performance, migrate workloads, and control costs. Our Databricks optimization tools provide comprehensive monitoring, automated root cause analysis, intelligent cost optimization, and proactive issue prevention. Ready to see what our Databricks optimization solutions can do?

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Unravel stands out among Databricks cost optimization solutions by excelling in all three FinOps stages: inform, optimize, and operate. Beyond just providing cost information at the account or project levels like other data optimization tools, our Databricks data optimization platform integrates app-level usage data to offer detailed chargeback and trend analysis at the workspace, cluster, and user levels. For Databricks cost optimization, our data observability software uses AI to find root causes across queries, compute, and storage, delivering specific recommendations for query rewrites and configuration adjustments that guarantee actionable and effective cost savings.

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Unravel’s Databricks data observability platform works across hybrid and multi-cloud environments with full support for major platforms, including Databricks, Snowflake, Google Cloud BigQuery, Amazon EMR, and other modern data stack systems. The platform covers AWS, Google Cloud, Azure, and on-premises deployments completely, making it an excellent data observability solution for organizations with complex, distributed data architectures.

Unravel Databricks Agents are AI-powered components that extend traditional Databricks data observability tools by taking automated actions for your team. The FinOps Agent handles Databricks cost optimization and governance within the data observability platform, delivering up to 50% more workloads for the same budget. The DataOps Agent cuts firefighting time by 99% through automated troubleshooting built into our Databricks data observability software. The Data Engineering Agent automates Databricks performance optimization, code reviews, and debugging, making our data observability platform a real AI teammate for your data engineering teams. 

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Unravel brings years of experience developing a comprehensive knowledge graph alongside AI and ML techniques for Databricks cost optimization and Databricks performance optimization. Our Databricks observability platform analyzes a complete stack of host metrics and telemetry data, including query metadata, compute details (warehouses, clusters), storage metadata, and network metadata to find root causes of inefficiencies and recommend actionable improvements. Unlike other Databricks observability tools, Unravel’s proven expertise shows in its success with numerous Fortune 500 companies across different industries, delivering measurable results that distinguish us from other data observability companies.

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The main difference between many code generation products currently available is that they are typically based on static code analysis, while Unravel’s Databricks AI Insights Engine uses dynamic code analysis–meaning that its review of code and SQL is based on more than just the code itself, but also the data layout, table structure, partitioning, and other metadata. Unravel also analyzes your code’s runtime performance and efficiency to enhance recommendations.

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Unravel enables AI-driven innovation with Databricks, such as fraud detection. Unravel is committed to security and focused on keeping our clients and their data safe. Unravel’s data observability software is SOC 2-compliant and has earned a Service Organization Control (SOC) 2, Type II certification, ensuring enterprise-grade security standards that data observability companies must meet.

Organizations using Unravel’s Databricks data optimization platform typically see significant cost reductions. Our customers report cutting cloud data costs by up to 70% within six months while maintaining or optimizing Databricks performance. The FinOps Agent can automatically find Databricks cost optimization opportunities, implement governance policies, and provide detailed chargeback and showback tracking to keep costs under control. These results show why investing in the right Databricks agents delivers measurable ROI.

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At the FinOps Crawl phase, the data will be less granular. You may start at the cloud provider and service level. In the Walk phase, you’ll want additional granularity to go into the application level. If FinOps data is only available at the cluster or infrastructure level, you may face challenges with allocation. In the Run phase, you may need to get to the user, project, and job level.

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Real-time monitoring and alerting with Databricks Overwatch requires a time-series database. Databricks refreshes your billable usage data about every 24 hours and AWS Cost and Usage Reports are updated once a day in comma-separated value (CSV) format. Since Cost Explorer includes usage and costs of other services, you should tag your Databricks resources and you may consider creating custom tags to get granular reporting on your Databricks cluster resource usage. Unravel simplifies this process with Cost 360 for Databricks to provide full cost observability, budgeting, forecasting, and optimization in near real time. Cost 360 includes granular details about the user, team, data workload, usage type, data job, data application, compute, and resources consumed to execute each data application. In addition, Cost 360 provides insights and recommendations to optimize clusters and jobs as well as estimated cost improvements to prioritize workload optimization.

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Many enterprises are facing this exact situation, experiencing performance and cost issues migrating on-premises workloads to Databricks. On-prem workloads are naturally limited in terms of cost due to the physical constraints of their data center, but costs can suddenly grow on the cloud. Enterprises that have successfully migrated to Databricks start by optimizing jobs before moving them to the cloud to ensure they can predict the spend and performance.

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Databricks offers general tips and settings for certain scenarios, for example, auto optimize to compact small files. Unravel provides recommendations, efficiency insights, and tuning suggestions. With a single Unravel instance, you can monitor all your clusters, across all instances, and workspaces in Databricks to ​​speed up your applications, improve your resource utilization, and identify and resolve application problems.

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Azure Databricks offers optimization suggestions and troubleshooting tips for certain scenarios, for example, auto optimization to compact small files. Unravel provides recommendations, efficiency insights, and tuning suggestions on the Applications page and the Jobs tab. With a single Unravel instance, you can monitor all your clusters, across all instances, and workspaces in Azure Databricks to ​​speed up your applications, improve your resource utilization, and identify and resolve application problems.

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Databricks collects monitoring and operational data in the form of logs, metrics, and events for your Databricks job flows. Databricks metrics can be used to detect basic conditions such as idle clusters and nodes or clusters that run out of storage. Troubleshooting slow clusters and failed jobs involves a number of steps such as gathering data and digging into log files. Data application performance tuning, root cause analysis, usage forecasting, and data quality checks require additional tools and data sources. Unravel accelerates the troubleshooting process by creating a data model using metadata from your applications, clusters, resources, users, and configuration settings, then applying predictive analytics and machine learning to provide recommendations and automatically tune your Databricks clusters.

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Azure Databricks collects monitoring and operational data in the form of logs, metrics, and monitoring for your Azure Databricks job flows. Azure Databricks metrics can be used to detect basic conditions such as idle clusters and nodes or clusters that run out of storage. Troubleshooting slow clusters and failed jobs involves a number of steps such as gathering data and digging into log files. Data application performance tuning, root cause analysis, usage forecasting, and data quality checks require additional tools and data sources. Unravel accelerates the troubleshooting process by creating a data model using metadata from your applications, clusters, resources, users, and configuration settings, then applying predictive analytics and machine learning to provide recommendations and automatically tune your Azure Databricks clusters.

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Unravel doesn’t orchestrate Databricks Workflows but uses AI to improve Databricks performance, productivity and cost efficiency of your Databricks jobs. Unravel’s AI Agents for Databricks provide insights and recommendations for optimizing Databricks jobs. Simply ask questions in plain language to identify areas for improvement and potential cost savings. You can ask specific questions about the insights, recommendations, or performance metrics of your Databricks jobs that run on job compute clusters.

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Unravel’s Databricks optimization platform gets rid of the manual work that slows down data teams. Our data observability software automates Databricks performance optimization, handles routine debugging tasks, and provides intelligent code reviews. This means data engineers spend less time on repetitive troubleshooting and more time building valuable data products. Teams report dramatically reduced time spent firefighting issues and faster resolution of data pipeline problems, showing how effective data observability tools can transform team efficiency. 

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Yes. Unravel’s CI/CD integration for Databricks is language-agnostic. It seamlessly integrates with popular programming languages such as SQL, Python, Scala, and Java.

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Yes. Unravel’s CI/CD integration for Databricks supports both Azure DevOps and GitHub. You have the flexibility to choose the platform that best suits your team’s needs.

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Yes. With Unravel’s CI/CD integration for Databricks, you gain access to comprehensive metrics and AI-powered insights about your pipelines’ performance. You can identify and quickly resolve bottlenecks, improve resource efficiency, optimize code, and make data-driven decisions.

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Yes. Unravel allows you to tailor the PR review process according to your specific requirements. You can define custom rules, set thresholds for performance metrics, and establish guidelines to ensure code quality.

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Yes. Unravel’s CI/CD integration for Databricks seamlessly integrates with popular automated testing frameworks. You can incorporate unit tests, integration tests, and regression tests into your pipeline to ensure the reliability of your code.

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Data teams spend most of their time preparing data—data aggregation, cleansing, deduplication, synchronizing and standardizing data, ensuring data quality, timeliness, and accuracy, etc.—rather than actually delivering insights from analytics. Everybody needs to be working off a “single source of truth” to break down silos, enable collaboration, eliminate finger-pointing, and empower more self-service. Although the goal is to prevent data quality issues, assessing and improving data quality typically begins with monitoring and observability, detecting anomalies, and analyzing root causes of those anomalies.

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No. Databricks Units (DBUs) are reference units of Databricks Lakehouse Platform capacity used to price and compare data workloads. DBU consumption depends on the underlying compute resources and the data volume processed. Cloud resources such as compute instances and cloud storage are priced separately. Databricks pricing is available for Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). You can estimate costs online for Databricks on AWS, Azure Databricks, and Databricks on Google Cloud, then add estimated cloud compute and storage costs with the AWS Pricing Calculator, the Azure Pricing Calculator, and the Google Cloud Pricing Calculator.

Cost 360 for Databricks provides trends and chargeback by app, user, department, project, business unit, queue, cluster, or instance. You can see a cost breakdown for Databricks clusters in real time, including related services such as DBUs and VMs for each configured Databricks account on the Databricks Cost Chargeback details tab. In addition, you get a holistic view of your cluster, including resource utilization, chargeback, and instance health, with automated AI-based cluster cost-saving recommendations and suggestions.

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No, it is not mandatory, but very useful if possible. Azure bill integration unlocks the full potential of Unravel’s cost analysis insights and reports. This integration ensures that the insights and reports obtained are as accurate and comprehensive as possible.

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Unravel’s Databricks observability platform offers flexible deployment options to meet your organization’s requirements and security preferences. You can deploy our data observability software as a fully managed SaaS solution for rapid implementation, through a cloud marketplace for streamlined procurement and billing, or as an on-premises deployment within your own VPC for maximum control and data residency requirements. This flexibility makes Unravel’s Databricks observability solutions adaptable to any enterprise architecture or compliance framework.

Virtual Private Cloud (VPC) peering enables you to create a network connection between Databricks clusters and your AWS resources, even across regions, enabling you to route traffic between them using private IP addresses. For example, if you are running both an Unravel EC2 instance and a Databricks cluster in the us-east-1 region but configured with different VPC and subnet, there is no network access between the Unravel EC2 instance and Databricks cluster by default. To enable network access, you can set up VPC peering to connect Databricks to your EC2 Unravel instance.

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Virtual network (VNET) peering enables you to create a network connection between Azure Databricks clusters and your Azure resources, even across regions, enabling you to route traffic between them using private IP addresses. For example, if you are running both an Unravel VM and Azure Databricks cluster in the East US region but configured with different VNET and subnet, there is no network access between the Unravel VM and Databricks cluster by default. To enable network access, you can set up VNET peering between your Azure Databricks master node and your Unravel VM.

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Implementation time for Unravel’s data observability platform varies by deployment method and your organization’s security review process. SaaS deployments can be up and running in minutes to hours once security approvals are in place, providing the fastest time to value for our data observability tools. On-premises or VPC deployments generally require 1-2 weeks, though your infosec process may extend the overall timeline, for complete implementation, plus additional time for security reviews and compliance validation, depending on your organization’s requirements. Most organizations begin seeing insights and value from our data observability software within the first few days after completing their internal approval processes.

Unravel provides granular Insights, recommendations, and automation for before, during and after your Spark, Hadoop, and data migration to Databricks.

Get granular chargeback and cost optimization for your Databricks workloads. Unravel for Databricks is a complete data observability platform to help you tune, troubleshoot, cost-optimize, and ensure data quality on Databricks. Unravel provides AI-powered recommendations and automated actions to enable intelligent optimization of big data pipelines and applications.

Learn more about Unravel for Cloud Migration.