Simply put, big data is structured traditional data plus unstructured new data. So what are traditional data and new data? Traditional data is the data in the IT business system, such as customer information, financial data, etc. These data are structured, and the amount is not particularly large, generally only terabytes. Compared with traditional data, there is also a kind of "new data", which comes from social networks, the Internet and other channels, including text, pictures, audio, video and other unstructured data. At present, more than 75% of the world is unstructured data, and it has been showing explosive growth.
Common big data application areas:
1. Understand customers and meet customer service needs
Big data applications are currently the most widely known in this field. Through big data analysis, we can better understand the hobbies and behaviors of customers and users. Enterprises like to collect user social data, browser logs, various text and sensor data through online customer service systems, so as to understand customers more comprehensively and build data models for prediction.
2. Business process optimization
Big data can help optimize business processes to dig out valuable data through social media data, business data, web search data, etc. Currently, big data is most widely used in the Internet of Things and human resources industries; for example, in the Internet of Things industry, optimization Supply chain and delivery routes track goods and delivery vehicles based on geographic location and radio frequency identification, and use real-time traffic route data to optimize delivery routes; for example, in the human resources industry, there are massive candidate information and corporate information that need to be analyzed through big data To optimize and quickly match candidates and companies, identify and screen duplicate and invalid resumes, and match people and jobs.
3. Big data improves daily life
Big data is not only applied to enterprises and governments, but also to everyone in life. We can use wearable equipment (such as smart watches, smart bracelets, smart anklets) to generate the latest data, track our health according to our heart rate, stress and work and rest data; and we can also use big data analysis to find our love , most of the time dating sites are big data application tools to help people in need match suitable objects.
4. Improve the quality of medical research and development
Computing power applied to big data analysis could allow us to decode the entire DNA in a matter of minutes. And it allows us to develop the latest treatment options. At the same time, it can better understand and predict diseases. Just like the data that can be generated by people wearing smart watches, big data can also help patients treat their diseases better. Big data technology is currently being used in hospitals to monitor premature babies and sick babies. By recording and analyzing the baby's heartbeat, doctors can help doctors better rescue babies by making predictions about the baby's possible discomfort.
5. Improve sports performance
Now athletes will apply big data analysis technology when training. For example, the IBM SlamTracker tool for ball games uses video analysis to track and analyze the performance of each player in football or baseball games, and the sensor technology in sports equipment can analyze real-time data from games to improve sports equipment and venue facilities ;Many elite sports teams also track the movement of athletes outside the competition environment - by using smart technology to track their nutritional status and sleep quality to improve meals and training methods to get athletes to the appropriate competitive state.
6. Optimize performance
Big data analysis can also make the application of machines and equipment more intelligent and autonomous. For example, big data tools are used by Google to develop Google's self-driving car. Toyota's Previa is equipped with cameras, GPS and sensors to achieve unmanned safe driving; in addition, big data tools can also be applied to optimize smart phones.
7. Ensure city safety
Big data is now widely used in the process of urban security and law enforcement. For example, health codes and itinerary cards for epidemic prevention are closely related to us at present. Enterprises use big data technology to defend against network attacks. Police use big data tools to catch criminals, and banks use big data tools to prevent fraudulent transactions.
8. Improve urban traffic
Big data is also applied to improve our daily life in cities. For example, based on urban real-time traffic information, using social network and weather data to optimize the latest traffic conditions, most first- and second-tier cities are currently conducting big data pilots.
9. Financial transactions
Big data in the financial industry is mainly applied to financial transactions. High-frequency trading (HFT) is an area where big data is widely used. Among them, big data algorithms are applied to trading decisions. Nowadays, many equity transactions are carried out using big data algorithms, and these algorithms are now increasingly considering social media and website news to decide whether to buy or sell in the next few seconds.
The times make heroes, and the same is true for individuals. Follow the trend, identify the track for your future development, and do the right thing at the right time, which is to seize your future.
The purpose of industry research is to draw future-oriented conclusions. Therefore, it is very important to understand industry trends.
In the Internet age, where are the future opportunities?
A few days ago, the Beijing Institute of Big Data, the National Engineering Laboratory for Big Data Analysis and Application Technology, and Beijing Zhishu Technology Co., Ltd. jointly released the "2022 China Big Data Industry Development Index Report".
On the basis of continuously publishing the big data industry development index in 2020 and 2021, the research team conducted in-depth research on the big data policy environment, big data industry and enterprise development status in various places, based on the data of 7,472 big data companies included in its own enterprise database and Based on the data of relevant partners, a comprehensive assessment of the development of the big data industry in 31 provincial-level administrative regions (excluding Hong Kong, Macao and Taiwan regions) and 150 key cities across the country is carried out.
Judging from the provincial scores of big data industry development, the 31 provincial-level administrative regions across the country (excluding Hong Kong, Macao and Taiwan) have significant differences in the development level and differentiation trend of big data industry.
Top 20 Cities for Big Data Industry Development
△Scatter diagram of city rankings in big data industry development index
It can be seen from the echelon and ranking changes that the development level of the city's big data industry is positively correlated with the city's comprehensive development level.
The first echelon has obvious advantages and leads the development of the big data industry.
The rankings are Beijing, Shenzhen, Shanghai, Guangzhou, and Hangzhou. These cities are strong, and the development level of the big data industry is at the top of the country, and the index ranking is firmly in the top five in the country.
The second echelon has a strong momentum of catching up, and the scale of the big data industry has expanded.
The rankings are Nanjing, Tianjin, Chengdu, Suzhou, Hefei, Chongqing, and Wuhan.
The big data industry development index of these cities is relatively concentrated, the rankings change greatly, and the market competition is fierce. Among them, the rankings of Chongqing, Tianjin, and Chengdu have risen rapidly, while the rankings of cities such as Hefei, Suzhou, and Wuhan have declined.
The development trend of the third echelon is good, but there is still a lot of room for improvement. The rankings
are Wuxi, Xiamen, Qingdao, Xi'an, Zhuhai, Zhengzhou, Fuzhou, and Jinan. These cities have a good overall development trend of big data industry and have great development potential and Market space needs to speed up the pace of catching up.
Situation of top companies
* The above screenshots are all from the "2022 China Big Data Industry Development Index Report", such as intrusion and deletion
Judging from the market value of big data listed companies in various places, Beijing topped the list with 1.356 trillion yuan, becoming the only city in the country with a value exceeding one trillion yuan, followed by Hangzhou, Shanghai, and Shenzhen, with a market value of over 500 billion yuan. Hangzhou is 0.744 trillion yuan, Shanghai is 0.578 trillion yuan, and Shenzhen is 0.569 trillion yuan;
From the perspective of net profits of listed companies, the net profits of Beijing, Hangzhou, Shenzhen, Tianjin, Shanghai, and Qingdao exceeded 10 billion yuan. Among them, Beijing, Tianjin, and Shanghai rose strongly, and Beijing surpassed Hangzhou and Shenzhen to reach 37.98 billion yuan. Leading other cities, both Tianjin and Shanghai are among the ten billion net profit enterprise clubs.
Big data blooms everywhere,
how to seize learning opportunities?
From the "2022 China Big Data Industry Development Index Report", we can see that now big data-related industries have developed in various cities, the scale of the industry is also expanding, and the demand for talents in related industries is also increasing !
According to the "New Occupation—Analysis Report on the Employment Prosperity of Big Data Engineering and Technical Personnel", it is expected that the demand for big data talents will maintain a growth rate of 30%-40% before 2025, and the demand for talents in the industry will reach 2.5 million.
Not only is there a lot of recruitment demand, but the employment salary of big data development talents in major cities is also very impressive.
△ Data source staff collection, such as intrusion and deletion
High salaries and large gaps naturally become the "salary" choice for professionals in the workplace!
Any learning process requires a scientific and reasonable learning route in order to be able to complete our learning goals in an orderly manner. The content required to learn Python+big data is complex and difficult. We have compiled a comprehensive Python+big data learning roadmap for you to help you clarify your thinking and overcome difficulties!
Detailed introduction to Python+big data learning roadmap (all free video tutorials)
Getting Started with Big Data Development in Phase 1
Pre-study guide: Start with traditional relational databases, master data migration tools, BI data visualization tools, and SQL, and lay a solid foundation for subsequent learning.
1. Big data data development foundation MySQL8.0 from entry to proficiency
MySQL is the entire IT basic course, and SQL runs through the entire IT life. As the saying goes, if SQL is well written, you can find a job easily. This course fully explains MySQL8.0 from zero to advanced level. After studying this course, you can have the SQL level required for basic development.
The core foundation of big data in the second stage
Pre-study guide: learn Linux, Hadoop, Hive, and master the basic technology of big data.
2022 Big Data Hadoop Introductory Tutorial
Hadoop offline is the core and cornerstone of the big data ecosystem, an introduction to the entire big data development, and a course that lays a solid foundation for the later Spark and Flink. After mastering the three parts of the course: Linux, Hadoop, and Hive, you can independently realize the development of visual reports for offline data analysis based on the data warehouse.
The third stage of hundreds of billions of data warehouse technology
Pre-study guide: The course at this stage is driven by real projects, learning offline data warehouse technology.
Data offline data warehouse, enterprise-level online education project practice (complete process of Hive data warehouse project)
This course will establish a group data warehouse, unify the group data center, and centralize the storage and processing of scattered business data; the purpose is from demand research, design, Version control, R&D, testing, and launch, covering the complete process of the project; digging and analyzing massive user behavior data, customizing multi-dimensional data sets, and forming a data mart for use in various scene themes.
The fourth stage PB memory computing
Pre-study guide: Spark has officially adopted Python as the first language on its homepage. In the update of version 3.2, it highlights the built-in bundled Pandas; Spark content.
1. From entry to mastery of python (19 days)
Python basic learning courses, from building the environment. Judgment statements, and then to basic data types, and then learn and master functions, familiarize yourself with file operations, initially build object-oriented programming ideas, and finally lead students into the palace of python programming with a case.
2. Python programming advanced from zero to website building
After completing this course, you will master advanced Python syntax, multi-tasking programming, and network programming.
3.spark3.2 from basic to proficient
Spark is the star product of the big data system. It is a high-performance distributed memory iterative computing framework that can handle massive amounts of data. This course is developed based on Python language learning Spark3.2. The explanation of the course focuses on integrating theory with practice, which is efficient, fast, and easy to understand, so that beginners can quickly master it. Let experienced engineers also gain something.
4. Big data Hive+Spark offline data warehouse industrial project actual combat
Through the big data technology architecture, it solves the data storage and analysis, visualization, and personalized recommendation problems in the industrial Internet of Things manufacturing industry. The one-stop manufacturing project is mainly based on the Hive data warehouse layer to store the data of various business indicators, and based on sparkSQL for data analysis. The core business involves operators, call centers, work orders, gas stations, and warehousing materials.