What to study in big data major
The contents of the big data major include: 1. Java; 2. Big data basics; 3. Hadoop system; 4. Scala; 5. kafka; 6. Spark; 7. python; 8. mysql . Big data majors are divided into two types: big data development, data analysis and mining.
Big data refers to a collection of data that cannot be captured, managed and processed within a certain time range using conventional software tools. It requires new processing. Models can provide massive, high-growth and diversified information assets with stronger decision-making power, insight discovery and process optimization capabilities. At present, big data is a very popular major. Now I will talk to you about what to study in big data major?
Big data majors are divided into two types, one is big data development, and the other is data analysis and mining.
1. Big data development: Java, big data foundation, Hadoop system, Scala, kafka, Spark, etc.;
2. Data analysis and mining: Python, relational database MySQL , document database MongoDB, memory database Redis, data processing, data analysis, etc.
There are two types of content to be learned in big data majors. Big data development: Java, big data foundation, Hadoop system, Scala, kafka, Spark, etc.; data analysis and mining: Python, relational database, document database, memory database, data processing analysis, etc.
The above is the detailed content of What to study in big data major. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
PHP's big data structure processing skills
May 08, 2024 am 10:24 AM
Big data structure processing skills: Chunking: Break down the data set and process it in chunks to reduce memory consumption. Generator: Generate data items one by one without loading the entire data set, suitable for unlimited data sets. Streaming: Read files or query results line by line, suitable for large files or remote data. External storage: For very large data sets, store the data in a database or NoSQL.
C++ development experience sharing: Practical experience in C++ big data programming
Nov 22, 2023 am 09:14 AM
In the Internet era, big data has become a new resource. With the continuous improvement of big data analysis technology, the demand for big data programming has become more and more urgent. As a widely used programming language, C++’s unique advantages in big data programming have become increasingly prominent. Below I will share my practical experience in C++ big data programming. 1. Choosing the appropriate data structure Choosing the appropriate data structure is an important part of writing efficient big data programs. There are a variety of data structures in C++ that we can use, such as arrays, linked lists, trees, hash tables, etc.
Five major development trends in the AEC/O industry in 2024
Apr 19, 2024 pm 02:50 PM
AEC/O (Architecture, Engineering & Construction/Operation) refers to the comprehensive services that provide architectural design, engineering design, construction and operation in the construction industry. In 2024, the AEC/O industry faces changing challenges amid technological advancements. This year is expected to see the integration of advanced technologies, heralding a paradigm shift in design, construction and operations. In response to these changes, industries are redefining work processes, adjusting priorities, and enhancing collaboration to adapt to the needs of a rapidly changing world. The following five major trends in the AEC/O industry will become key themes in 2024, recommending it move towards a more integrated, responsive and sustainable future: integrated supply chain, smart manufacturing
Discussion on the reasons and solutions for the lack of big data framework in Go language
Mar 29, 2024 pm 12:24 PM
In today's big data era, data processing and analysis have become an important support for the development of various industries. As a programming language with high development efficiency and superior performance, Go language has gradually attracted attention in the field of big data. However, compared with other languages such as Java and Python, Go language has relatively insufficient support for big data frameworks, which has caused trouble for some developers. This article will explore the main reasons for the lack of big data framework in Go language, propose corresponding solutions, and illustrate it with specific code examples. 1. Go language
Application of algorithms in the construction of 58 portrait platform
May 09, 2024 am 09:01 AM
1. Background of the Construction of 58 Portraits Platform First of all, I would like to share with you the background of the construction of the 58 Portrait Platform. 1. The traditional thinking of the traditional profiling platform is no longer enough. Building a user profiling platform relies on data warehouse modeling capabilities to integrate data from multiple business lines to build accurate user portraits; it also requires data mining to understand user behavior, interests and needs, and provide algorithms. side capabilities; finally, it also needs to have data platform capabilities to efficiently store, query and share user profile data and provide profile services. The main difference between a self-built business profiling platform and a middle-office profiling platform is that the self-built profiling platform serves a single business line and can be customized on demand; the mid-office platform serves multiple business lines, has complex modeling, and provides more general capabilities. 2.58 User portraits of the background of Zhongtai portrait construction
AI, digital twins, visualization... Highlights of the 2023 Yizhiwei Autumn Product Launch Conference!
Nov 14, 2023 pm 05:29 PM
Yizhiwei’s 2023 autumn product launch has concluded successfully! Let us review the highlights of the conference together! 1. Intelligent inclusive openness, allowing digital twins to become productive Ning Haiyuan, co-founder of Kangaroo Cloud and CEO of Yizhiwei, said in his opening speech: At this year’s company’s strategic meeting, we positioned the main direction of product research and development as “intelligent inclusive openness” "Three core capabilities, focusing on the three core keywords of "intelligent inclusive openness", we further proposed the development goal of "making digital twins a productive force". 2. EasyTwin: Explore a new digital twin engine that is easier to use 1. From 0.1 to 1.0, continue to explore the digital twin fusion rendering engine to have better solutions with mature 3D editing mode, convenient interactive blueprints, and massive model assets
Getting Started Guide: Using Go Language to Process Big Data
Feb 25, 2024 pm 09:51 PM
As an open source programming language, Go language has gradually received widespread attention and use in recent years. It is favored by programmers for its simplicity, efficiency, and powerful concurrent processing capabilities. In the field of big data processing, the Go language also has strong potential. It can be used to process massive data, optimize performance, and can be well integrated with various big data processing tools and frameworks. In this article, we will introduce some basic concepts and techniques of big data processing in Go language, and show how to use Go language through specific code examples.
Big data processing in C++ technology: How to use in-memory databases to optimize big data performance?
May 31, 2024 pm 07:34 PM
In big data processing, using an in-memory database (such as Aerospike) can improve the performance of C++ applications because it stores data in computer memory, eliminating disk I/O bottlenecks and significantly increasing data access speeds. Practical cases show that the query speed of using an in-memory database is several orders of magnitude faster than using a hard disk database.
