Datagrok for Data Science

Data scientists should focus on science, not infrastructure

Datagrok's integrated machine learning lets you easily connect to data, visualize and explore it, build predictive models, and operationalize them.

Understand your data by using built-in interactive tools such as multivariate analysis, clustering, or dimensionality reduction.

Seamlessly integrate your existing code in R or Python, and scale computations as needed.

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Why Datagrok?

Why Datagrok?

Datagrok was built for data scientists, by data scientists. We understand science, engineering, and we do our best to build the perfect data science platform. Our proprietary in-memory database that runs in the browser lets us process data orders of magnitudes faster than other products do.

Don't take our word for this – run the platform right now and see it yourself!

„Grok” means to understand so thoroughly that the observer becomes a part of the observed

Robert A. Heinlein, Stranger in a Strange Land (1961)

Prepare

80% of data science time is spent retrieving and cleaning the data. By natively integrating with the data retrieval mechanisms and having built-in collaboration features, we drastically reduce that time.

  • Aggregate, join, filter and edit data right in the browser
  • Record and apply macros
  • Use 500+ available functions, or write your own in R, Python, or JavaScript
  • Visually edit pipelines and query transformations
  • Reproduce and trace results back to raw data
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Prepare

Explore

Our unique technology lets you explore datasets faster and more efficiently than ever, allowing to find patterns that were previously impossible to spot, resulting in the acceleration of data-driven decisions.

  • Proprietary in-memory database technology allows to handle tens of millions of rows in the browser
  • 25+ high-performance interactive viewers
  • Powerful integration with any visualizations available in R, Python, or Julia languages
  • Statistical Hypothesis Testing
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Explore

Analyze

A number of commonly used data mining techniques are exposed as interactive applications

  • Multivariate analysis
  • Statistical hypothesis testing
  • Similarity and diversity analyses
  • Jupyter notebooks
  • Domain-specific analyses
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Analyze

Train models

Use either the built-in UI, or train the model yourself using your tool of choice

  • Different backends
  • Locally or in the cloud
  • AutoML
  • Assess and compare
  • Model repository
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Train models

Deploy models

Deploy trained models to users in minutes

  • Integrate into workflows
  • Push via data augmentation
  • Automated suggestions of applicable models
  • Analyze usage data
  • Expose as REST API
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Deploy models

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