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The ultimate Data Scientist tool to work efficiently with Big Data in Hadoop ecosystem

Comfortable Environment
For Data Analysis


No more analytics vs. development. 

Your Data Scientists can do computing on a Hadoop cluster on their local machine – fast end easy.

The familiar Python and Jupiter Notebook for data analysis and model designing.
Python models automatically transform into industrial code
running on a cluster. 

Launching production analytic models without Data Analytics

Loss of data

Data Scientists cannot directly access data on a Hadoop cluster – they don’t understand how data is stored,
and they don’t know about the types of data available
Loss of accuracy

To put an analytical model in commercial operation, you need to convert it from Python to Java/Scala. This affects the accuracy of the algorithms. The actual KPIs of a production model on a Hadoop cluster will be significantly lower than the KPIs of algorithms created in Python on a local machine.
Schedule overrun

Many iterations to adjust the required precision of the industrial model.
Tensions between data scientists and data engineers as they use different technology stacks.

Data Analytics Makes Work With Industrial Models Easy!

  • Import and Export
  • Analytics and Modeling
  • Monitoring
  • Reporting
Squeeze the maximum out of data – any data

Manage the inbound data streams of your cluster
Aggregate data from various sources
Customize the import schedule in a few clicks
Easy to integrate with existing systems in the company
Export model results to
any external applications

Analytics UI

Create, save and edit projects. Separate sample data for machine learning and model control.
Use SQL queries to calculate parameters.  Put models in production directly from the analytics UI.

Data Import UI

Support SQL queries to import data from databases into Hadoop.
Manage DBMS connections.
Start or restart import and processing.
Preview the import result to avoid errors.

Data Export UI

Export data to external databases or files

Use data encryption

Preview the export result to avoid errors

Model Performance Monitoring

Visual history of launches

Notification of errors in model operation

Tracking related calculations

Monitoring Key Metrics of Models

Dashboards with current metric values

Color indication of metric deviations

Interactive retrospective graphics for each metric

One click to start model tuning

Improve Efficiency of Your Team

  • Implement big data analytics painlessly
    Integrate with any analytical systems
    Improve awareness about the work of your employees
    Optimize your resources and focus your efforts

Easy to deploy Big Data and
Machine Learning into business processes

New insights
in 1-2 weeks
Instead of 1.5-2 months
Efficient employment
of staff 1 = 3
A Data Scientist is enough to analyze big data
to scale
Training a Data Scientist is easier than learning the entire Hadoop ecosystem. The basic course lasts 20 hours.

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