Building resilient, scalable cloud data lake by bridging "data silos"



Big data has become the rule of the game in most industries in the past few years. Industry leaders, academics and other well-known stakeholders agree on this. As big data continues to penetrate into our daily lives, the hype surrounding big data is growing and real value of data in actual use.


Most companies adopt big data to solve BIG problems. The main goal of the company is to enhance the customer experience, but other goals include reducing costs, more targeted marketing, and making existing processes more efficient.

So, how to develop a big data application architecture for an industry ?


Before formulating a big data application architecture, it is necessary to clarify the problems faced by the enterprise, business demand scenarios, and user needs.


Under the current situation of digitization of enterprises, although business systems, ERP, and supply chain systems have been launched, the group management and business layers still face the following problems:


1. The data is scattered and stored in multiple business systems, forming "data islands" one by one. There is no way to open up these data and conduct in-depth data analysis from multiple angles.


2. Each business department eagerly hopes to solve some management and business development problems through data, but the existing report form cannot meet the analysis needs.


3. Relying on the cooperation method of IT access, the transmission efficiency is low, it is easy to cause duplication of labor, and the timeliness of the data cannot be guaranteed. It often takes more than a week from the data generation to the flow into the business department, and the risk cannot be exposed in time.


4. With the development of the company, data security and confidentiality have become increasingly important, especially some company financial information, customer information, etc., which require authorized management to protect and manage data.

Then through the big data analysis platform, what valuable information you want to get, what data needs to be accessed, and clarify the basic functions of the big data platform based on the business needs of the scene, to determine the big data used in the platform construction process. Data processing tools and frameworks.

The overall modern data architecture of the big data platform can be composed of the following parts:


1. Business application: In fact, it refers to data collection. How do you collect data? It is relatively simple to collect data on the Internet. Data can be collected through web pages and apps. For example, many banks now have their own apps. A deeper level can also collect user behavior data, which can be divided into many dimensions for detailed analysis. But for offline industries, data collection needs to be done with the help of various business systems.


2. Data integration: It actually refers to Extraction, Loading and Transformation, which refers to the user extracting the required data from the data source, after data cleaning, and finally strong the data on Elastic Object Storage.


3. Data storage: refers to the construction of a data lake, which can be storing of data divided into catalogs by subject area in simple terms.

4. Data sharing layer: It means to provide data sharing semantic data layer / services between the data lake and the business system. Virtual Datasets and API represent a connection method between data, and there are some other connection methods, which can be determined according to your own situation.


5. Data analysis layer: The analysis function is relatively easy to understand, that is, various mathematical functions, such as K-means analysis, clustering, RMF model and so on.


Column storage allows each Page in the disk to store only the value of a single column, not the value of the entire row. This compression algorithm will be more efficient. Furthermore, this can reduce disk I/O and improve cache utilization. Therefore, disk storage will be used more efficiently. Distributed computing can divide a problem that requires a lot of computing power into many small parts, and then give these parts to many systems to process at the same time, and then combine these intermediate results to get the final result. Combining these two technologies can greatly improve the efficiency of the analysis process.


6. Data presentation: In what form the results are presented is actually data visualization. The processed data can be connected to mainstream BI systems to visualize the results for decision analysis; or return to online to support online Business development.


7. Data access: This is relatively simple. It depends on how you view the data. The example in the figure is because of the B/S architecture. But formulating a big data application architecture is not a simple matter. It is a complex task in itself. There are many factors that need to be considered in the process, such as:


Stability: Multiple machines can be used to back up data and program operation, but the quality of the server and the budgeted cost will correspondingly limit the stability of the platform;


Scalability: The big data platform is deployed on multiple machines, how to expand new machines on its basis is a problem often encountered in practical applications;


Governance and Security: Ensuring data security is an issue that cannot be ignored when building a big data application architecture. In the process of massive data processing, how to prevent data loss and leakage has always been a research focus in the field of big data security.


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