Why is the data service API the standard configuration of the data midrange when we take the last mile of the data midrange?

Link to the original text ： Take the last kilometer of the data center station , Data services API It is the standard configuration of the data console

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One 、 Data services API Construction background

In the era of digital transformation , A substantial increase in new demand 、 The constant iteration of new technologies ,“ The Internet, 、 Digitization ” The continuous deepening of the process , More and more businesses are migrated to the Internet , Generate a large number of business interactions and external service requirements , Yes API The demand for interfaces is increasing day by day , How to quickly improve the enterprise's ability to open and share data , It is the key proposition for enterprises to face digital transformation .

Traditional methods, such as back-end developers through Java or Python And other languages to generate API Interface , The development cycle is too long , The cost of operation and maintenance is too high , Can no longer meet the needs of enterprises . Enterprises often face many difficulties in the process of digital transformation ：

In order to solve these problems more , We are open in the enterprise 、 The following objectives need to be identified in the process of sharing data ：

• Fast build API

• System stability 、 Data security

• Easy to integrate

• Authorized delivery

• Low cost operation and maintenance

Two 、 Data service platform construction methodology

Before sharing the methodology of data service platform construction , Let's first learn about the common data application architecture ：

The data service layer is in the middle of the overall application architecture of the data center , Pass the results of the data computing layer through the data API Is shared to the data application layer . The data service layer mainly includes 3 A role ：

1、 When the data has been integrated and calculated , It needs to be provided to products and applications for data consumption ;

2、 For better performance and experience , Build the data service layer , Provide external data services through interface servitization ;

3、 Meet various complex data service requirements of applications （ Simple data query service 、 Complex data query services 、 Real time data push ）

In the process of providing external services at the data service layer , Experienced from “DWSOA” To “OneService” The evolution of .

from “OneService” For the data service itself , It mainly solves the problem of heterogeneous data sources 、 building redundant project 、 Audit operation and maintenance is difficult 、 Understanding difficulties is 4 A question , adopt “OneService” service , Realize topic data service 、 Unified and diverse data services 、 Service objectives of cross source data services .

therefore , If you want to build a complete data service platform , You need to have the following 6 Elements ：

• Convenient development , Low code development capability

• Easier to manage ,API Manage operations visual queries API

• Easy to use , Have standardized document description information

• Safe and stable , Service call tracking monitoring 、 Service usage audit 、 Authentication etc.

• Easy operation and maintenance , test 、 Rectification 、 Problem rule configuration

• performance , Load balancing 、 High concurrency

3、 ... and 、 be based on OneService Building data systems

To understand the “OneService” theory , Next, let's share how to base on OneService Building data systems , Mainly follow the following steps ：

● First step ：API Definition

API The definition of includes ： Quick configuration parameters 、 Select the sort field 、API Diversity of types 、 Data preview 、 Copy fields, etc .

API Types of include generation API、 register API、 Service grouping and service arrangement 4 In terms of .

● The second step ：API Release

API The release of includes testing 、 Submit to API gateway 、 Release to API market 、 Version management .

● The third step ：API call

API Call includes data preview 、API apply 、 The examination and approval 、 Download the interface documentation 、 Formal call .

● Step four ： Call monitoring

Business ： Yes API Call statistics for in-depth analysis , And then get the key information ;

technical ： adopt API Call the statistical chart for analysis to find , Which? API The most popular ; And what almost nobody cares about , Should be eliminated ;

On the security ： Call on IP、 The number of calls is monitored , Trace the source of the caller .

● Step five ： Data security

Data security includes ： Unified Authentication 、 Transmission encryption 、 Security group 、 Role assignment 、 Line level authority 、 Call approval, etc .

The above data services API The construction process of , In fact, it is the several stacks of data services developed by kangaroo cloud EasyAPI The process of product implementation .

Data services （EasyAPI）, Efficient enterprise data service products , Configuration generation and registration through dual mode visualization API, Fast build OneService Data sharing services , Form an enterprise level API Market and API Service management platform , Improve the efficiency of data opening and sharing .

At the same time, the product has the following characteristics :

• Fast build

Configuration is development , Support 0 Code 、 Low code fast build API

• High safety

User authentication 、 monitor 、 Transmission encryption 、API Level security policy 、 Line level authority 、 Role assignment 、 Call application approval 、 Limit on the number of call cycles 、 Black and white list

• High flexibility

“ Service Orchestration “ For different API Are combined , Support integration python Data processing 、 Support “ conditional ” node , Select the branch that meets the criteria

• Flexible configuration

Horizontal expansion API gateway 、 cache

• Low cost operation and maintenance

use Serverless framework , Just focus on API Its own business logic , Little consideration is given to infrastructure such as the operating environment

Four 、API Implement landing cases

Next, let's share three actual cases of using customers , To introduce EasyAPI How to effectively help customers solve problems .

● Finance ： Application data service of a securities company

● School ： A university application data service

● retail ： Application data service of a network company

The source of the original ：VX official account “ Several stack Study Club ” Kangaroo cloud open source framework nail technology exchange group （30537511）, Students interested in big data open source projects are welcome to join us to exchange the latest technical information , Open source project library address ：https://github.com/DTStack