What language foundation do you need to master to learn big data?
1. Java foundation
More than 90% of big data frameworks use the Java development language, so if you want to learn big data technology, you must first master the basic Java syntax and the relevant knowledge of JavaEE direction.
2. MySQL database
This is one of the knowledge that must be mastered in learning big data. The language of data manipulation is SQL, so the development goal of many tools is to be able to use SQL on Hadoop.
3. Linux system
The framework of big data is installed on the Linux operating system, so proficiency in Linux-related knowledge is also the basic knowledge of big data.
The learning of big data cannot only stay at the theoretical level. The direction of big data is all-round, and the learning of basic language is only a small aspect. After programming is implemented in the end, it is the programming idea. With the guiding ideology, it is easy to learn. Can be much more convenient.
At present, the big data positions provided by enterprises can be divided into the following categories according to the job content requirements:
① Primary analysis category, including business data analysts, business data analysts, etc.
② Mining algorithms, including data mining engineers, machine learning engineers, deep learning engineers, algorithm engineers, AI engineers, data scientists, etc.
③ Development and maintenance, including big data development engineers, big data architecture engineers, big data operation and maintenance engineers, data visualization engineers, data acquisition engineers, database administrators, etc.
④ Product operation category, including data operation manager, data product manager, data project manager, big data sales, etc. The number and proportion of the four types of posts are shown in the figure below.
The demand for big data is increasing, and the country is also opening related jobs, which have increased year by year since 2018.
At this time, students and parents who apply for university are also very interested in big data and artificial intelligence. Big data has entered the top 5 for three consecutive years, and a bachelor's degree is all that is required.
In the foreseeable next few years, this is really a sunrise industry, and there is a big gap now.
The technical requirements of a big data engineer are as follows:
1. Master at least one database development technology: Oracle, Teradata, DB2, Mysql, etc., and flexibly use SQL to realize ETL processing of massive data;
2. Familiar with the regular shell processing commands of the Linux system, and flexibly use the text processing and system operations done by the shell;
3. Experience in distributed data storage and computing platform application development, familiarity with Hadoop eco-related technologies and relevant practical experience are preferred, focusing on Hdfs, Mapreduce, Hive, and Hbase;
4. Proficiency in one or more programming languages, and experience in large-scale project construction is preferred, focusing on Java, Python, and Perl;
5. Those who are familiar with the knowledge and skills in the field of data warehouse are preferred, including but not limited to: metadata management, data development and testing tools and methods, data quality, master data management;
6. Master real-time stream computing technology, experience in storm development is preferred.
Data engineers aim to look at the big picture and develop. Data engineers build automated systems and model data structures so that data can be processed efficiently. The goal of a data engineer is to create and develop tables and data pipelines to support analytics dashboards and other data customers (such as data scientists, analysts, and other engineers). Much like most engineers, there are many designs, assumptions, constraints, and developments to be able to create some sort of ultimate robust system. This system might be a data warehouse and ETL or streaming pipeline.
Analyzing different industries, we found that the demand for big data jobs is distributed in all walks of life, mainly in computer software and the Internet. It may also be determined by this recruitment software. After all, Boss direct employment is still mainly in the Internet industry.
Let's take a look at which companies are recruiting for big data-related positions. Judging from the number of more than 15, Huawei, Tencent, Ali, Byte, these big companies still have a large demand for this position.
So what skills do these jobs require? Spark, Hadoop, Data Warehouse, Python, SQL, Mapreduce, Hbase, etc.
According to the domestic development situation, the future development prospects of big data will be very good. Since enterprises have started digital transformation in 2018, first- and second-tier cities have a very strong demand for talents in the field of big data. In the next few years, the demand for talents in third- and fourth-tier cities will also increase significantly.
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
Before choosing a training institution, you can learn the basics of big data first to see if you can master it~
This set of tutorials covers everything that must be learned in big data
Hadoop, Hive, cloud platform practical projects
Let zero-based students get started in one stop
Straight-through big data core technology
This new set of big data tutorials is based on Hadoop, Hive, cloud platform and other technologies to lead you into the field of big data from shallow to deep, and experience the charm of large-scale data computing together.
Based on the content design of zero-based learning, it provides a wealth of supplementary knowledge points for zero-based students to carry out pre-learning.
As a new big data introductory course in 2023, the course content adopts a new technology stack system. Based on Hadoop3.3.4, Hive 3.1.3, Alibaba Cloud and UCloud cloud platforms, an introductory course for students to create a big data Hadoop ecosystem, but not just Hadoop.
course features
• Perfect combination of theory + practice: This set of tutorials uses the form of "theory + practice" to comprehensively introduce the relevant knowledge of big data Hadoop and Hive offline development;
• Both content and depth: the course adopts the content design of "introduction + improvement", the introductory knowledge and advanced knowledge are independent of each other, first comprehensive introduction, then comprehensive advanced, step by step so that everyone can learn something;
• Combining the current popular cloud platforms (Aliyun, UCloud) to bring you "Cloud Native Big Data Development": based on Hadoop3.3.4, Hive 3.1.3, Alibaba Cloud and UCloud cloud platforms, using a new technology stack system.
suitable for the crowd
>Basic zero: beginners to advanced level, and then to proficiency
＞Advanced: Experienced engineers consolidate and expand
＞Explorer: those interested in enjoying the charm of big data
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.