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Unravel’s Purpose-Built AI for Data Observability and FinOps

The most advanced AI built specifically for today's modern data platforms.

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Getting ahead of data avalanches by empowering and uniting data teams

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At the core of Unravel’s data observability platform is our AI-powered Insights Engine. This sophisticated Artificial Intelligence engine incorporates AI techniques, algorithms, and tools to process and analyze vast amounts of data, learn from patterns, and make predictions or decisions based on that learning.

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The decentralized nature of modern data platforms enable tremendous speed and scale but in order to harness the power of data, you need to ensure performance and efficiency across your entire data estate.

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“We have exponentially grown–almost 100x–what we used to run 5 years ago.”

LUIS CARLOS CRUZ HUERTAS DAVIS
Head of Automation, Infrastructure DBS Big Data, AI and Analytics, DBS Bank

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Our AI-powered Insights Engine was designed to mimic human intelligence and enable data teams to be even more effective. Enterprises have thousands of engineers and data scientists writing code, creating tens of thousands of data pipelines and developing thousands of machine learning models across heterogeneous environments. The more complex your data estate, the more you need AI.

No one has the time (or desire) to spend hours sifting through reams of log data, performance metrics, source code, error messages, and cloud billing data. Even if you know what you are looking for, there is still a chance that you will miss something and there simply aren’t enough data engineers.

Regardless of your level of expertise, you can leverage Unravel’s AI-powered insights to ensure you are continually making things better and more optimized. This approach enables new, self-service capabilities, and stops all the finger-pointing that goes on between FinOps teams, operations teams, and data engineering teams. Providing a common source of truth across the organization simplifies communication and accelerates results. Everyone can see what needs to be done, and they can easily take action to deliver bigger business results, faster.

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AI IS in our DNA

A powerful and platform-specific AI lies at the center of Unravel

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AI isn’t new to Unravel. We’ve had it since the beginning. Our co-founder Shivnath Babu started researching this topic at Stanford and continued to expand on that work at Duke, where he launched Starfish. That foundation enabled Unravel to quickly help large organizations achieve performance and efficiency at scale.

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AI specific to your data platform
Every data platform is unique, so you need AI that understands your specific data platform. Unravel’s ML models are trained for each specific platform—across a wide variety of workloads to provide your team accurate insights.

Unlike generic AI tools, Unravel’s AI-powered Insights Engine is purpose-built for modern data platforms such as Databricks, Snowflake, Google Cloud BigQuery, Amazon EMR, and more. No matter how your stack evolves over time, Unravel’s AI provides deep insights, tailored to your needs.

Designed specifically for modern data workloads, Unravel’s AI-powered Insights Engine goes deep into DataOps and FinOps. It runs at the scale you need to achieve your business goals with data.

Real-time insight
The engine has been built to continuously ingest and interpret the millions of ongoing data streams to provide real-time insights into application and system performance, and automatically deliver recommendations to optimize code and resource allocations for performance and financial efficiencies.

Advanced Automation
The simple fact is, you need a more automated approach with technology. In order to comprehend the interdependencies and complexity at this scale, you need to leverage the automated nature of artificial intelligence.

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“The only way to comprehend data analytics code at today’s scale is to use AI.”

DIRECTOR OF DATA INTEGRATION, AI, MACHINE LEARNING AND ANALYTICS
Global logistics company

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Our AI-powered insights provide specific guidance on how to improve performance and cost efficiency to achieve your data analytics and AI ambitions and accelerate your business.

A continuously improving dynamic AI
Unravel knows how your data pipelines and applications are supposed to run. And the AI is dynamic, constantly changing and improving as more data pipelines are processed and analyzed.

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AI is woven into our platform at all levels

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At the core of Unravel Data’s platform is its AI-powered Insights Engine, which has been trained to understand all the intricacies and complexities of modern data platforms and the supporting infrastructure.

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Embedded AI capabilities

  • Comprehensive metadata

    Unravel collects comprehensive metadata that’s specific to each data platform. So you have visibility that is very specific to your cloud data platform about who’s using what. All the way down to the jobs, service, user, application level. This deep visibility into the metadata from your specific data platform is the foundation for Unravel’s internal model to correlate all of the details in a way that is actionable and enables optimization at every level.

  • Patterns for efficiency

    That’s where Unravel’s AI and machine learning come in. Based on your data platform’s unique configuration and usage patterns, Unravel’s AI-powered Insights Engine correlates the metadata about table and partition access metrics, source code, and dependencies for each stage of your data pipelines. Unravel delivers insights for each of the components that matter to your data teams.

  • Visualize your results

    Unravel helps you visualize and understand all of the interrelationships within your data stack, such as degree of parallelism, resource contention, dependencies, and data lineage. All of this information is presented in views that are relevant to how data teams need to understand that information to operate the data platform.

  • Code-level insights for developers

    Unravel’s AI can analyze queries and code written in a wide variety of languages – SQL, Python, Java, Scala – to help uncover anti-patterns early in the data application development lifecycle.

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