Data Analytics — Eastwind

EW DATA ANALYTICS

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

Out-of-the-Box Solution

Work with data in the familiar Jupiter Notebook interface
right on the Hadoop cluster
Create your own unique analytical models or
use templates and manage computing on the cluster
Put analytic models in production
in one click, directly from the analytics dashboard

The full picture of the data analysis before your eyes

Control the quality of machine learning
in real time
Track data availability 24/7 and respond quickly
to change
Visualization of the relations between the calculations gives you the full picture
of your data use

Make decisions based on data

Learn about the most valuable data and control
the most important model parameters
Timely reports help you improve
the overall performance of your team

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

CTO CDO CMO Data Scientist

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
Increase the number of working models
Reduce model development time
Increase the speed and accuracy
Be aware about the number of working, broken models and their schedule
Actively use reports and receive notifications
Reduce Time-to-Market for new features and parameters of customer analytics
Turn insights into actions fast
Boost sales and profits by introducing new analytic models
Be the first to respond to the challenges of a changing market
One user-friendly toolkit for all tasks
Fast model implementation
Easy-to-use modeling environment
Access to all data from different sources
Clear results and high precision without over-engagement of Data Engineers and Developers

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

Easy
to scale

Training a Data Scientist is easier than learning the entire Hadoop ecosystem. The basic course lasts 20 hours.