Unravel Data
The first DataOps Observability Platform built to meet the needs of modern data teams.
Overview
Unravel Data is designed to help data teams manage and optimize their modern data stacks. It uses AI to provide recommendations for improving the performance of data applications and managing cloud costs. The platform supports a wide range of technologies, including data warehouses, and is focused on enabling a proactive, full-stack approach to data observability.
✨ Key Features
- AI-powered recommendations for performance and cost optimization
- Full-stack observability (applications, pipelines, infrastructure)
- Root cause analysis for failures and performance issues
- Cost governance and chargeback
- Support for Spark, Databricks, Snowflake, and more
🎯 Key Differentiators
- Deep focus on Spark and Databricks optimization
- AI-driven, prescriptive recommendations for code and configuration changes
- Full-stack view from application code to infrastructure
Unique Value: Automates the complex process of optimizing data pipelines for cost and performance, enabling data teams to deliver reliable data products faster and more efficiently.
🎯 Use Cases (4)
✅ Best For
- Automatically identifying and providing fixes for inefficient Spark jobs.
- Right-sizing Databricks clusters or Snowflake warehouses based on workload analysis.
- Pinpointing the root cause of pipeline delays or failures.
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Teams looking for a simple data quality alerting tool
- Organizations not using Spark, Databricks, or a major cloud data warehouse
🏆 Alternatives
Goes deeper than general-purpose APM tools by understanding the specifics of data workloads (e.g., Spark stages, query plans) to provide more relevant and impactful optimization advice.
💻 Platforms
🔌 Integrations
🛟 Support Options
- ✓ Email Support
- ✓ Live Chat
- ✓ Dedicated Support (Enterprise tier)
🔒 Compliance & Security
💰 Pricing
✓ 14-day free trial
Free tier: N/A
📊 Market Info
Customers: 100-200
Total Funding: $107.0M
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