what is big data
What is "big data"? Literally, big data refers to huge amounts of data. Then some people may ask, how much data is called big data? Different institutions or scholars have different understandings, and it is difficult to have a very quantitative definition. It can only be said that the measurement unit of big data has surpassed the TB level and developed to the PB, EB, ZB, YB and even BB levels.
Mai Qingxi, a world-renowned consulting company, first proposed the concept of "big data". It defines big data in this way: a kind of data whose scale is so large that it greatly exceeds the capabilities of traditional database software tools in terms of acquisition, storage, management, and analysis. The data collection has four characteristics: massive data scale, fast data flow, diverse data types and low value density.
Research firm Gartner defines big data in this way: "Big data" requires a new processing model in order to have stronger decision-making power, insight and discovery, and flow optimization capabilities to adapt to massive, high-growth, and diverse information assets.
From a technical point of view, the strategic significance of big data lies not in the mastery of huge data, but in the professional processing of these meaningful data. In other words, if big data is compared to an industry, then the profitability of this industry The key is to improve the "processing ability" of data, and realize the "value-added" of data through "processing".
The big data industry is a rapidly developing industry, and its main characteristics are the large scale, variety and complex processing of data. At present, the big data industry has become one of the hot spots in the global information technology field, attracting the attention of many enterprises and investors.
Is big data easy to learn?
Easy to learn, in fact, no matter what kind of programming technology to learn, it is the thinking of learning computer language. Since you are interested in big data, you can try it first. The most important thing now is the opportunity to try. We will not get that high salary. Low. At this time, there will be more voices saying that big data is more difficult. This is said to be difficult from around 2010. At that time, the concept of big data was rarely mentioned. In the era of crossing the river by feeling the stones, After 10 years of development, big data technology is now very mature, and more and more industries are involved, and it is relatively simple to transform into learning.
What to learn about big data
Big data technology is a comprehensive discipline involving data collection, storage, processing and analysis. The following is the main learning content of big data technology:
1. Data collection and processing
Learn how to collect different types of data, including structured, semi-structured, and unstructured data. In addition, you also need to learn how to clean, transform and preprocess the data for further analysis.
2. Big data storage
Learn how to store large amounts of data in distributed systems such as Hadoop Distributed File System (HDFS) and NoSQL databases for efficient access and management.
3. Data analysis
Learn how to use different data analysis techniques, such as data mining, machine learning, and statistical analysis, to identify patterns and trends in data and extract valuable information.
4. Data visualization
Learn how to visualize data using a variety of tools and techniques, such as charts, graphs, and interactive dashboards, to help users better understand the data and discover patterns and associations in the data.
5. Big Data Architecture
Learn how to design and implement a big data architecture, including data storage, data processing, data analysis, and data visualization components, to support high performance, scalability, and reliability of big data processing.
6. Big data security and privacy
Learn how to protect the security and privacy of big data, including techniques and practices in data encryption, access control, authentication, and auditing.
7. Big data application
Learn how to apply big data technologies to solve practical problems, such as applications in marketing, risk management, healthcare and smart manufacturing.
In general, big data technology requires comprehensive application of knowledge from multiple disciplines such as mathematics, statistics, computer science, data science, and engineering to deal with the ever-growing and changing large amounts of data.