Consulting

Big Data Consulting

The consulting team with a number of data scientists and analysts of OSBD provide big data industry consulting service , help customers quickly, efficiently and conveniently obtain the value of big data ,and achieve data decision making capability and data value innovation capability by integrating open source technology with industry business.

OSBD can help enterprises to apply big data for top-level design and provide data acquisition, storage, management, analysis, mining, application and visualization and the whole industry chain integrated services of big data.

Consulting Content

• Big Data Governance Strategy Consulting Service

According to customer's strategic goal and vision, OSBD combined with customer service requirements to help enterprises to determine data streams and data sources, build data processing architecture, access to big data value by the top design for big data applications.

• Consulting Service of Big Data System Construction

OSBD meet customer's demand for data service of production management system and external environment according to customer's business objectives, design and development and delivering customized solutions to customers.

• Big Data Analysis Technology Consulting Service

OSBD help clients complete the description and characterization of existing business problems from data visualization to Management Visualization,and use advanced data analysis techniques and innovative data analysis methods to explore new business opportunities and value.

Talent Training

Talent Training

OSBD has a strong team of experts resources ,it helps customers improve data insight and data decision-making power and helps enterprises to achieve data driven business transformation through various forms of big data basic theory, analysis methods and application training. The training is divided into the following four aspects.

Content

• Big Data Basic Theory

The content of training revolves around architecture and algorithm and involves technical foundation and basic concepts of big data generation, the theoretical basis of big data analysis method,the principle of MapReduce distributed computing and HDFS distributed file storage ,etc.

• Big Data Engineering Method

Big data engineering methods include data quality and data management,principles and methods of visual analysis,common algorithms of data mining and applicable business scenarios, principles and methods of predictive analysis,text analysis and semantic engine technology.

• Big Data Release Technology

The content of big data release technology for storage and computation covers many open source technologies,includes the following points: big data storage based on HDFS;distributed computing based on MapReduce and YARN;non relational database based on HBase and cooperate with Sqoop; big data warehouse based on Hive; real time calculation of big data based on Storm and Kafka; large scale data processing based on Spark computing framework ,etc.

• Big Data Application

Big Data Application is the core of enterprise application, Involves analysis of enterprise market data, enterprise production process monitoring and fault diagnosis analysis, enterprise logistics chain, supply chain management and life cycle monitoring of enterprise products,etc.

Product Development

Product Development

Shanghai open source Institute of big data products based on a number of open source projects, after two times of development and transformation to meet the needs of special industries, especially with a large number of sensor data in the industrial field. At present, there are several sets of products which are not used in the environment to meet the needs of the research field, mainly virtualization, big data storage, high performance computing based on the three most core.

Content

• Big data infrastructure

Because the traditional data infrastructure in the price, there are many problems of scalability, usability and so on, now the rapid development of science and technology, both hardware and software have a great leap, the emergence of new technology makes some traditional architectures and techniques are not suitable for the trend, so we need a new to realize the innovation of the application data system. According to the characteristics of the data in the field of Research Institute for analysis, extraction, fusion later build a line with the characteristics of big data system architecture has high real-time, confidentiality, high adaptability and high scalability, including distributed storage, distributed computing, virtualization, contribute to the application of the exploratory development and operation.

• Data visualization

Data visualization is becoming more and more important in the era of big data. The past data want to do custom analysis, engineers need to rewrite the code to achieve an analysis, making the work difficult to carry out, the distance between people and data is also far away. In the era of big data, the importance of data has been improved, while the demand for visualization has become indispensable. Research Institute combines computer graphics and image processing technology, comprehensive data representation, data processing, decision analysis and a series of technologies, the knowledge into visual data presentation. By visualizing, understanding and understanding data, transforming data into easier and rapidly explored forms, perceptual images are formed to enhance cognitive understanding, help to understand the information contained in the data, and improve the efficiency of specific tasks.

• Functional application special

According to the special needs of different industries and different fields, both in R & D and design are different from the realization of traditional applications. In the process of realization, we should fully analyze each processing link, and finally come up with the matching architecture and method. Relying on the rich experience and technology in the industrial field, the research institute takes the real application as the starting point, and focuses on the system architecture, data storage, calculation analysis and other parts of the research and guidance. To complete the goal, assist the enterprise to complete the technical innovation quickly and conveniently, and realize the original enterprise goal.