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Software Status:Active

About Unravel

Unravel is a data observability and actionability platform from Unravel Data that enables capable insights for managing cloud workloads. It provides data observability, automated governance, and AI-driven actionability so enterprises can improve their operational efficiencies. The platform supports popular data services such as Databricks, Snowflake, Google Cloud BigQuery, and Amazon EMR, helping organizations gain visibility and control over their data environments. Unravel’s automated agents allow users to fully control their data observability processes, making it easier to identify and resolve issues quickly. Key capabilities: data observability AI-driven actionability automated governance support for multiple cloud platforms user control over AI agents Best for: enterprises that need to improve cloud workloads and automate data management processes.

Unravel Details

Vendor
Unravel Data
Year Launched
2012
Location
319 N Bernardo Avenue, Mountain View, CA 94043
Deployment
cloud
Training Options
videos
Countries Served
All Countries
Languages
English, French, German, Spanish, Portuguese, Chinese, Japanese
Users
IT ADMINISTRATORS, DATA SCIENTISTS, DEVELOPERS
Industries Served
Healthcare, Education, Finance.
Tags
Application Performance Management, Big Data

Unravel's In-App Market Place

Does Unravel have an in-app market place?

Yes

How many Mini-Apps in the marketplace?

5

Mini Apps

Databricks

Snowflake

Google Cloud BigQuery

Amazon EMR

Cloudera

Pricing Options

Free trial
Free version
Request a quote
Promo Offer

Accepted Payment Currencies

USD ($), EUR (€), GBP (£), AUD ($), CAD ($), JPY (¥), CNY (¥), INR (₹), RUB (₽)

Pros & Cons

  • Comprehensive Monitoring: Provides in-depth monitoring and visibility into big data application performance, including real-time analytics and automated insights.
  • AI-Driven Optimization: Uses AI and machine learning to proactively identify and resolve performance issues, optimizing resource utilization and minimizing downtime.
  • Seamless Integration: Integrates well with major big data platforms like Cloudera, Hortonworks, and Databricks, as well as various cloud environments, ensuring compatibility and flexibility.
  • User-Friendly Interface: Features an intuitive dashboard with customizable views, making it accessible for users to navigate and manage complex data pipelines.
  • Performance Scalability: Handles large-scale data processing efficiently, supporting scalability and ensuring reliable performance across diverse workloads.
  • Proactive Alerts and Recommendations: Provides actionable insights and recommendations for performance tuning, enhancing operational efficiency and application stability.
  • Customized Pricing: Pricing details are often tailored to enterprise-specific needs, which may require negotiation and can vary widely based on deployment size and features.
  • Complex Setup for Beginners: While user-friendly, initial setup and configuration may require technical expertise, especially for organizations new to big data analytics and management.
  • Dependency on Ecosystem Compatibility: Performance and integration capabilities depend on compatibility with existing big data frameworks and infrastructure, requiring careful assessment for seamless deployment.
  • Learning Curve: Advanced features such as AI-driven optimizations may have a learning curve for users unfamiliar with big data analytics or machine learning concepts.
  • Resource Intensive: Monitoring large-scale data operations can be resource-intensive, potentially requiring adequate hardware and infrastructure to support continuous monitoring and analysis.
  • Support for Emerging Technologies: While strong in traditional big data platforms, ongoing support for emerging technologies and newer frameworks may vary, requiring updates and adaptation over time.

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