Now, driven by digital transformation, more and more companies realize the charm of big data and continue to invest in this field. Talents related to Python+ big data development are also favored!
According to the "New Occupation—Analysis Report on the Employment Prosperity of Big Data Engineering and Technical Personnel", the demand for big data talents will reach 2.5 million in 2025!
Under this gap, the salaries of big data talents have been soaring. Not only can high-paying jobs be found in first-tier cities, but employment in new first-tier cities and provincial capital cities is also very good!
Now the development of big data is in full swing, and many small partners are more interested in what is big data, so the more official definition of big data refers to the collection of data that cannot be captured, managed and processed by conventional software tools within a certain period of time , is a massive, high-growth and diverse information asset that requires a new processing model to have stronger decision-making power, insight and discovery power, and process optimization capabilities.
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 has been applied in hospitals to monitor the conditions of 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 symptoms.
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.
Possibilities that can be obtained by learning big data:
1. Big data development engineer
Develop, build, test and maintain the architecture, responsible for the development and maintenance of the company's big data platform, and responsible for the architecture design and product development of the continuous integration of the big data platform related tool platforms.
2. Data Analyst
Collect, process and perform statistical data analysis; use tools to extract, analyze and present data, and realize the commercial significance of data, which requires business understanding and tool application ability.
3. Data Mining Engineer
Data modeling, machine learning and algorithm implementation; business intelligence, user experience analysis, prediction of churn users, etc.; in addition to strong mathematical and statistical skills, there are also high requirements for algorithm code implementation.
4. Data architect
Requirements analysis, platform selection, technical architecture design, application design and development, testing and deployment; advanced algorithm design and optimization; data-related system design and optimization require platform-level development and architecture design capabilities.
5. Database development
Design, develop and implement database systems based on customer needs, connect databases and database tools through ideal interfaces, optimize performance and efficiency of database systems, etc.
6. Database management
Database design, data migration, database performance management, data security management, troubleshooting issues, data backup, data recovery, etc.
7. Data scientists
Data mining architecture, model standards, data reporting, and data analysis methods; use algorithms and models to improve data processing efficiency, mine data value, and realize the conversion from data to knowledge.
8. Data product manager
Combine data and business to make data products; the platform line provides basic platforms and general data tools, and the business line provides analysis frameworks and data applications that are closer to business.
With the increase in the demand for big data talents, the development space and treatment of the big data industry are getting better and better. Many want to switch to big data. The three main employment directions of big data are big data system R&D talents and big data application development. Talents and big data analysis talents. Regardless of the size of the enterprise, data analysis talents have become a rigid demand, and the advantages of majors are obvious, but related majors and non-graduates can also help their careers by learning and mastering data analysis knowledge while practicing in the workplace.
What does big data development do?
There are two types of big data development, writing Hadoop and Spark applications and developing the big data processing system itself. The big data development engineer is mainly responsible for the development and maintenance of the company's big data platform, architecture design and product development of related tool platforms, network log big data analysis, real-time computing and streaming computing, data visualization and other technology research and development, and network security business theme construction. model work.
Skills required for big data development:
The languages currently engaged in the development of big data applications include Java, Python, Scala, R, etc. It is necessary to be familiar with the principles and usage methods of Hadoop, HBbase, hive, spark, Flink, ES, Presto, Flume, and Kafka ecology, and master data development and data mining of various processes.
Big data learning route and resources:
Getting Started: Getting Started with Linux → MySQL Database
Core Foundation: Hadoop
Data Warehouse Technology: Hive Data Warehouse Project
PB Memory Computing: Getting Started with Python → Advanced Python → pyspark Framework → Hive+Spark Project
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 the basic data types, and then learn and master the functions, familiarize yourself with file operations, initially build an object-oriented programming idea, 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.