What is a tourism big data platform?

To understand what a tourism big data platform is, we must first understand what constitutes a tourism big data platform.

Tourism is an industry attribute, which determines which indicators we need to pay attention to;

The big data platform is a technical architecture that determines how much data you can process at what speed and how to present it in the end.

So I will elaborate from these two aspects:

I. Big Data Platform The overall architecture of the big data platform is as follows

As shown in the figure, there are several links from bottom to top:

Business application: it actually refers to data collection and how you collect data. Collecting data on the Internet is relatively simple. You can collect data through web pages and apps, and you can also collect user behavior data at a deeper level. Many dimensions can be subdivided and analyzed in detail. But for offline industries, data collection needs to be completed with the help of various business systems. Of course, you can also get some external data through some open data sources or crawlers to make up for the lack of your own data.

Data integration: actually refers to ETL, that is, users extract the required data from the data source, clean the data, and finally load the data into the data warehouse according to the predefined data warehouse model. The Kettle here is just one of ETL.

Data storage: refers to the construction of data warehouse, which is complicated here, so I won't go into details. You can take a closer look at the "data warehouse layer" in the figure below.

Data * * * sharing layer: provides data * * * sharing service between data warehouse and business system. Web service and Web API both represent a way to connect data.

Data analysis layer: the analysis function that everyone can understand is various mathematical formulas, such as cluster analysis, regression analysis and so on.

Column storage allows each page of the disk to store only the values of a single column, rather than the values of an entire row. Therefore, compression algorithms are more efficient because they can handle the same type of data. Simply put, I/O on disk can be reduced, and cache utilization can be improved, so that disk storage can be used more efficiently.

Distributed computing can divide a problem that requires huge computing power into many small parts, then distribute these parts to many computers for processing, and finally synthesize these calculation results to get the final result.

Generally speaking, the efficiency of data analysis can be greatly improved through these two technologies.

Yonghong MPP should be the best column storage and distribution product at present.

Data presentation: in what form will the analysis results be presented? To put it bluntly, it is the work of data visualization. This part recommends agile BI products, which are different from traditional BI. The report can be generated simply by dragging and dropping, which saves time and has relatively low learning cost. In domestic agile BI, individual users recommend Tableau, and enterprise-level requirements recommend Yonghong BI.

Data access: This is relatively simple, depending on how you look at the data. The example in the figure is because of the B/S architecture, and the final visualization result is accessed through the browser. Of course, there are also C/S architectures, which can be viewed through the client. Relatively speaking, B/S architecture will be more convenient and more suitable for the needs of many people who work with mobile phones. You can see the data by opening the webpage.

Second, what indicators should the tourism industry pay attention to? I take the tourism data of a province as an example:

The indicators that can be analyzed are:

Analysis of provincial tourism income (including income amount, growth rate and comparison with national income growth rate)

Analysis of the tourism situation in the whole province (including the total number of star-rated hotels, domestic tourists, inbound tourists, inbound overnight tourists, the consumption level of tourists, the number of travel agencies, tourism professionals, etc.).

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

Tourism revenue analysis (commodity sales, long-distance transportation, accommodation, scenic spot tickets, catering, post and telecommunications)

Hotel analysis (according to the number of rooms, we can analyze the hotel form suitable for development in the emerging stage, which is more suitable for chain hotels or homestays)

Based on the above analysis, it can be concluded that the next stage of tourism in this province should focus on places to provide a basis for judging the planning.

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