In the digital age, big data has penetrated into every aspect of our lives. When it comes to big data, the first thing that comes to everyone's mind is good prospects and high salaries. However, they are still confused about what big data is. Today, I will tell you in detail.
What is big data?
If you look at it literally, big data refers to huge amounts of data. Then someone 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 developed beyond the TB level to the PB, EB, ZB, YB or even BB level.
The first person to propose the concept of "big data" was Mai Qingxi, a world-renowned consulting company. It defined big data in this way: a large-scale data that greatly exceeds the capabilities of traditional database software tools in terms of acquisition, storage, management, and analysis. The data collection has four major characteristics: massive data scale, fast data flow, diverse data types, and low value density.
Research organization Gartner defines big data this way: "Big data" requires new processing models to have stronger decision-making power, insight discovery and flow optimization capabilities to adapt to the massive, high growth rate and diversified information assets.
From a technical point of view, the strategic significance of big data does not lie in mastering huge amounts of data, but in 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 capabilities" of data and achieve the "value-added" of data through "processing".
Big data development prospects and characteristics
Judging from the school recruitment situation in recent years, the number of big data development positions has increased significantly. At present, enterprises not only need R&D talents, but also application talents. As big data begins to be fully implemented and applied, the current An industry is transitioning from platform development to application development, which is an inevitable trend.
The big data industry has developed rapidly in the past few years and has become one of the most important areas in the information technology industry. The development prospects and trends of the big data industry can be summarized as follows:
#1 Continued rapid growth
The big data industry will continue to grow rapidly. According to a report by market research company IDC, the global big data and business intelligence market reached US$189 billion in 2020 and is expected to reach a 5-year compound annual growth rate (CAGR) of 13.2%, and the market size will reach US$298 billion by 2025. .
#2 The popularity of cloud computing, AI and the Internet of Things
Cloud computing itself is closely related to big data. The development of technologies such as cloud computing, artificial intelligence (AI) and the Internet of Things (IoT) will bring more opportunities to the big data industry. Chance. These technologies can provide more data, faster calculation speeds and higher accuracy, thereby improving the efficiency and quality of big data analysis work, and expanding their own technical boundaries.
#3 The Increased Importance of Data Privacy and Security Data privacy and security
are becoming increasingly important as data breaches and information security incidents occur frequently. The big data industry will play a more important role in protecting data privacy and security. This will drive industry investment and technological innovation in data privacy and security.
#4 The popularity of data analysis
Data analysis has become an essential skill in all walks of life, and a large number of companies and organizations need professional data analysts to help them process and analyze data. The big data industry will provide more job opportunities for these data analysts.
#5 Accelerating Industrial Integration The big data industry will be more integrated with other industries.
For example, various industries such as medical care, finance, and retail will use big data technology to improve business efficiency and user experience. This will bring more development opportunities to the big data industry.
To sum up, the big data industry has a trend of sustained and rapid growth. In the future, there will be more technological innovation and industrial integration. At the same time, it is also necessary to pay attention to the issues of data privacy and security.
Is big data easy to learn?
The big data industry has experienced 10 years of development. Now the technology is very mature and involves more and more industries. It is relatively simple to transform it into learning.
Detailed introduction to Python+big data learning roadmap (all are free video tutorials)
Introduction to the first phase of big data development
Pre-study introduction: Start with traditional relational databases, master data migration tools, BI data visualization tools, and SQL to lay a solid foundation for subsequent learning.
1. Big data data development basics 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 you write SQL well, you can find a job easily. This course comprehensively explains MySQL8.0 from zero to advanced level. After studying this course, you can have the SQL level required for basic development.
The second stage of big data core foundation
Pre-study introduction: Learn Linux, Hadoop, Hive, and master the basic technologies of big data.
The 2022 version of Big Data Hadoop Introductory Tutorial
Hadoop Offline is the core and cornerstone of the big data ecosystem. It is an introduction to the entire big data development and a course that lays a solid foundation for later Spark and Flink. After mastering the three parts of the course: Linux, Hadoop, and Hive, you can independently implement visual report development for offline data analysis based on the data warehouse.
The third stage of hundreds of billions of data warehouse technology
Pre-study introduction: This stage of the course is driven by real projects and learns 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 centrally store and process scattered business data; the purpose is from demand research, design, Version control, research and development, testing to implementation, covering the complete process of the project; mining and analyzing massive user behavior data, customizing multi-dimensional data collections, and forming a data mart for use in various scene themes.
Phase 4 PB Memory Computing
Pre-study introduction: Spark has officially adopted Python as the first language on its homepage. In the update to version 3.2, it is highlighted that Pandas is built-in and bundled; the course fully complies with the trend of the technical community and recruitment needs, and is the first company in the entire network to add Python on Spark content.
1. Python from beginner to proficient (19 days complete)
Python basic learning course, starting from setting up the environment. Judgment statements, then basic data types, then learn and master functions, become familiar with file operations, initially build object-oriented programming ideas, and finally lead students into the Python programming palace with a case.
2.Advanced python programming from scratch to building a website
After studying this course, you will master Python's advanced syntax, multi-task programming and network programming.
3.spark3.2 from basics to mastery
Spark is the star product of the big data system. It is a high-performance distributed memory iterative computing framework that can process massive amounts of data. This course is developed based on Python language learning Spark3.2. The explanation of the course focuses on connecting theory with practice, is efficient and fast, and explains the profound things in simple terms, so that even beginners can master it quickly. Let experienced engineers also gain something.
4. Big data Hive+Spark offline data warehouse industrial project practice
Through the big data technology architecture, we solve 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 Hive data warehouse layering to store various business indicator data, and uses sparkSQL for data analysis. The core business involves operators, call centers, work orders, gas stations, and warehousing materials.