Empower Business with Data Science and AI

DataCanvas is a one-stop platform for data science teams to analyze and process data in real time, which incorporates data preparation, algorithm implementation, machine learning, model development and model productionisation to help businesses quickly build their data analytic applications and get running

Product Features

➢ One-stop Service

Integrated with visualization and code-based environment, the platform can complete all the procedures for data science applications, ranging from design to production in one stop, which meets the fast-changeable development requirements of data applications.

➢ Simplify Data Preparation

Support a variety of data connectors that can be used to access data from various data sources, including local data, external data, and data from databases and data warehouses.

➢ Reduce Complexity in Processing Big Data

DataCanvas allows workflows to automatically be converted to Hadoop or Spark tasks and submit to execution, and masks complexities of components for big data, which entitles analysts the ability to process big data.

➢ Create Optimized Models in a Fast and Convenient Way

With the container technique and an intuitive drag-and-drop UI, and supportive of multiple language programing, stable and accurate models can be built quickly.

➢ Put Optimized Models into Service Quickly

Data analytics can be done automatically and interactively in batch mode or real time, in local or cluster environment, enormously reducing the time needed.

➢ Scalable and Reusable Model Library

The accumulated model library improves efficiency in model development; performance can be extended vertically, cutting costs and enhancing practicability.

➢ Real-time

Support business in real time and get feedback in millisecond.

➢ High Performance

With a high-performance advantage, it can deal with hundreds of millions of messages on a daily basis.

➢ Ease of Maintenance

Complete requirement configuration by using generators, rule engines, parameter configuration, interface operation to get the information needed, lowering costs of system maintenance.

Product Advantages

Supportive of Multiple Roles

-     Professional data scientists

Develop algorithms and complete advanced analytics in customized working environment.

-     Data analysts

Build models with visualizations, and complete analytics and model building using built-in and customized algorithm modules.

-     Coders

For those who are familiar with R, Python and Scala, they can cooperate with each other on this platform, jointly creating business analytic models.

Customized Algorithms

-     Open data science

Support R, Python and Scala etc., and allows developers to upload or use external libraries.

-     Machine learning

Integrate multiple machine learning engines and is supportive of teamwork.

Support Big Data Analysis

-     Full amount data processing

Access data from Hadoop clusters using Apache Spark and Apache Impala, instead of only processing sampled data that is compressed.

-     High computing load

Support calling distributed computing engines such as Spark and MapReduce when the analytic flow is running, which can improve efficacy in data processing.

-     Coders

For those who are familiar with R, Python and Scala, they can cooperate with each other on this platform, jointly creating business analytic models.

Teamwork

-     Joint development

Develop together among team members, improving efficiency in development.

-     Knowledge sharing

Support model sharing, avoiding repeated work.

Engineering Capability

-     Version control

Track and monitor historical revisions; flexible switch between versions and grey release extremely improve flexibility in data analysis.

-     Support DevOps

Model developing, debugging, testing, and running in production environment can be completed in one stop, allowing uninterrupted integration and delivery.

Automated Ops

-     Schedule and monitor

Automated scheduling can be executed as the time set or in a period cycle; globalized monitoring allows you to keep up with the execution of scheduling.

-     Flexible deployment

The platform can be deployed in a machine room or on cloud, and the cluster size can be adjusted accordingly.

Model Productionisation

-     Loading management

Model loading management for metric rules, machine learning and deep learning.

-     Lightning decision

Streaming data combined with model computing delivers a fast processing speed, helping to make a lightning fast decision.

-     Agility

Flexible hot configuration, highly reliable design, and offline system docking.

Typical Application Scenarios in Financial Market

Analysis on Customers’ Footprint of E-banking

Analysis on customers’ footprint is the basic condition to predict customers’ consuming behaviors. After analyzing expenditures of customers in different sites, the consuming preference and behavior change of customers can be structured. Meanwhile, you can get clues about whether the scope of consuming is widening or narrowing. Finally, a dynamic and changeable map of customer consumption is obtained.

Big Data Analysis on Customer Services

Conduct statistical analysis on on-line and off-line customer services according to real-time data to obtain different characteristics within service personnel, and find the factors that affect customer services most, thus to improve service quality based on the results. In addition, the service quality in different branches can be collected to obtain the insight of service discrepancy, and help the branches improve their services.

Real-time Warning on Risks

Risk control has always been the key point of financial institutions. Therefore, it is quite important to conduct risk management to help enterprises progress smoothly. With real-time analysis, exceptional transactions can be tracked quickly, and relevant staff can take measures more quickly to respond to these transactions. Meanwhile, early warning can be sent out more quickly with real time analysis on risks, which greatly prevents risks from being formed.

Founded in 2013, Beijing ZetYun Tech Co., Ltd is a global leading data science platform provider, which is committed to develop core technologies in big data, provides end-to-end big data solutions for enterprises, and helps enterprises cultivate ability to analyze big data. Headquartered in Beijing, ZetYun has set up branches in Shanghai, Jinan and Seattle. Our company boasts a best-in-class R&D team, with core members working over ten years in big data analysis in the USA, while other developers from development and consulting positions at Fortune 500 companies with rich experience in big data analysis, machine learning and data modeling.