"No matter how serious the domestic or global" new crown "epidemic is, and how long it has to last, I just want to keep my job first. If my salary is reduced, I can find the next owner with better treatment in a short time."
—— "Big Data Employment Training Camp" 23 students Li Bin
has been doing big data training in 2014 for more than 5 years. It can be said that the development and change of big data technology is fast, and it is no exaggeration to say "rapid advancement" . As far as the development of the computing engine alone is concerned, before 2014, MapReduce must still be used to do offline calculations. Although the speed is slow, it is still very exciting to handle TB-level data scales. From 2014 to 2018, Spark entered the market strongly with its advantages based on in-memory computing and faster speed. Most big data personnel turned to Spark and the development of its ecosystem. Since 2017, as the demand for real-time application scenarios has expanded, Flink finally broke out in silence with its real-time computing, and people have begun to turn to the development of Flink and its ecosystem. So, what are the areas that the data man might turn to next? what is it then? A large number of previous students said this: "I don't want to only do technology development at the Hadoop, Spark, Flink level, I want to go deep into the core areas of data warehouse system construction, data asset management and so on." I am also thinking that with the increasing number of Hadoop, Spark and Flink developers, enterprises are paying more and more attention to data asset management, and the requirements for enterprise data transformation are becoming more and more urgent. Big data around data asset management Development will be destined to become a new direction, this direction will also develop more lasting, higher capacity requirements, better salary and better development prospects.
Take this opportunity, combing with the real application scenarios of the enterprise, to sort out "the 11 steps of the five major systems, and master the methodology of building a data center and data asset management platform."
Build a data asset management platform to achieve "clear home and clear data"
With the advent of the era of big data, people have realized that data is an intangible and valuable asset. For data owners and managers, through reasonable management and effective application of data, it can revitalize and fully release the huge value of data. But if they cannot effectively manage the data, the data will not be used, or even if it is not used well. In this case, the huge amount of disordered data brings high costs to the enterprise, and the data A tricky "debt". From this perspective, the ability to manage data assets has become an important factor in measuring the success of a company.
Eleven-step process of the five major systems to gradually build a data asset pool
Step 1: Business data inventory, sort out the data account
Step 2: Data access, flexible multi-source heterogeneous data access and storage
Step 3: Data exploration, accurate positioning of data quality issues
Step 4: Data preprocessing, clean dirty data
Step 5: Data standard system
Step 6: Data standardization
(1) Implementation of data standard system
(2) Comparison of data standardization effects
Step 7: Data warehouse construction, standards and specifications, data concentration, business-oriented
Step 8: Data processing, gradually close to business needs
Step 9: Metadata management, navigation map of data information
Step 10: Directory of data resources, so that the platform data is no longer a black box
Step 11: Open data sharing to achieve data capacity sharing
Global data security barrier, truly managed and controlled
