To know what is a big data platform for tourism, you need to be clear about what are the components of a big data platform for tourism?

Tourism is an industry attribute, which determines which indicators we need to focus on;

Big data platform is a technical level of architecture, which determines how fast you can process how much data, and finally in what way to present.

So I will elaborate on these two aspects:

I. Big Data Platform

The overall architecture of the big data platform is shown below

From the bottom up, as shown in the figure indicates that so many links:

Business Application: actually refers to data collection, what kind of way you collect the Data. The Internet is relatively simple to collect data, through the web page, the App can collect data, deeper still can collect the user's behavioral data, can be cut out of many dimensions, do a very fine analysis. However, for offline industries, data collection needs to be accomplished with the help of various business systems. Of course, you can also use some public data sources or crawlers to get some external data to make up for the lack of your own data.

Data Integration: This refers to ETL, which means that the user extracts the required data from the data source, cleanses it, and finally loads it into the data warehouse according to the pre-defined data warehouse model. Kettle here is just one of the ETL.

Data storage: refers to the construction of the data warehouse, which is relatively complex, I will not go into detail, you can look at the following chart in detail, "Data Warehouse Layer" in this section.

Data **** enjoyment layer: indicates in the data warehouse and business systems to provide data **** enjoyment services. Whether it is Web Service, or Web API , it represents a way of connecting between data.

Data Analysis Layer: this part of the analysis function we can understand, is the mathematical variety of formulas, such as cluster analysis, regression analysis and so on.

Column storage makes each Page of the disk store just the values from a single column, not the whole row. As a result, compression algorithms are more efficient because they can work on the same type of data. To put it more simply, disk I/O can be reduced, cache utilization can be improved, and therefore, disk storage will be used more efficiently.

Distributed computing, on the other hand, can take a problem that requires a very large amount of computing power to solve, break it into many smaller parts, distribute those parts among many computers, and then combine the results of those calculations to get the final result.

Overall, the efficiency of data analysis can be dramatically improved by these two techniques.

And Yonghong MPP is supposed to be the best product for column storage and distribution.

Data presentation: What kind of form is the result of the analysis to be presented, frankly speaking, is the work of data visualization. This part of the recommended use of agile BI products, different from the traditional BI, it can be generated through a simple drag and drop approach to the report, more time-saving, relatively low learning costs. Agile BI in the country, individual users recommend Tableau, enterprise-level demand recommended Yonghong BI.

Data Access: This is relatively simple, depending on what kind of way you are going to view the data, the figure in the example is because of the B/S architecture, the final visualization is accessed through the browser. Of course, there is also C/S architecture, through the client to view. Relatively speaking, B/S architecture will be more convenient, more suitable for many people nowadays with the needs of the cell phone office, open a web page will be able to see the data.

II. What indicators should the tourism industry pay attention to?

I'll take a province's tourism data as an example:

The indicators that can be analyzed are:

Provincial tourism revenue analysis (including the amount of revenue, growth rate, and revenue growth rate compared with the national)

Provincial tourism analysis (including the total number of star-rated hotels, the number of domestic tourists, the number of inbound tourists, the number of inbound overnight tourists, the level of tourist spending, the number of travel agencies, the number of tourist practitioners, etc.)

Analysis of inbound tourists (number of foreign tourists, number of compatriots from Hong Kong and Macao, number of compatriots from Taiwan, and their corresponding growth rates)

Tourism revenue analysis (merchandise sales, long-distance transportation, lodging, attraction tickets, food and beverage, postal and telecommunication services)

Hotel analysis (by the number of guest rooms can be analyzed to see the appropriate form of hotel for the development of the current stage, whether the chain hotels Or B&B is more appropriate)

Combined with the above analysis, it can be concluded that the province's next stage in tourism should go to focus on the place, to provide a basis for judgment planning.

So tourism big data platform, big data platform is the foundation, and specific indicators can determine the value of the application.