What is big data in the IT industry now? What s the use?

Big data is divided into development and analysis, development is software engineer, commonly known as programmer, and analysis is data analyst.

The concept of big data is actually a variety of data collection and collection, which can be very diverse, and there will be many platforms for collection.

Regarding big data, the McKinsey Global Institute gives the definition as:

A data collection that is so large that it greatly exceeds the capabilities of traditional database software tools in terms of acquisition, storage, management, and analysis, and has four characteristics: massive data scale, fast data flow, diverse data types, and low value density.

Simply understood as:

Big data"It is a dataset with a particularly large volume and a large data category, and such a dataset cannot be crawled, managed, and processed by traditional database tools.

The core role of big data is data valorization, which simply means that big data makes data produce various "values", and this process of data valorization is the main thing that big data should do.

First, the processing and analysis of big data is becoming the node of the new generation of information technology integration and application.

Second, big data is a new engine for the sustained and rapid growth of the information industry.

Third, the use of big data will become a key factor in improving core competitiveness.

Fourth, the methods and means of scientific research in the era of big data will undergo major changes.

Big data is a very fashionable technical term at the moment, and at the same time, it has naturally given rise to some professions related to big data processing, which influence the business decisions of enterprises through data mining and analysis.

In China, the application of big data has just sprouted, and the talent market is not so mature, so each company has different requirements for big data work: some emphasize database programming, some highlight applied mathematics and statistical knowledge, some require relevant experience in consulting companies or investment banks, and some hope to find application-oriented talents who understand products and markets. Because of this, many companies will give this group of people who deal with big data some new titles and definitions for their business type and team division of labor:

Data mining engineers, big data experts, data researchers, user analysis experts, etc. are all titles that often appear in domestic companies, and we collectively refer to them as "big data engineers".

1. What does a big data engineer do?

Therefore, analyzing the history, the future, and optimizing the choice are the three most important tasks for big data engineers when "playing with data". Through these three directions of work, they help businesses make better business decisions.

Find out the characteristics of past events.

An important part of the big data engineer's job is to analyze the data to find out the characteristics of past events.

Something that could happen in the future.

By introducing key factors, big data engineers can ** future consumer trends.

Find out the best results.

Depending on the nature of the business of different enterprises, big data engineers can achieve different purposes through data analysis.

Second, the ability to be possessed.

Background in mathematics and statistics.

Computer coding skills.

Practical development capabilities and large-scale data processing capabilities are some of the essential elements as a big data engineer.

Knowledge of a specific application area or industry.

Experience in one or more vertical industries can build up knowledge of the industry for the candidate, which is very helpful for later becoming a big data engineer, so it is also a more convincing plus point when applying for this position.

The main contents of big data analysis in IT companies are as follows:

Different companies have different contents, but they are generally the following: first, design and implement the basic framework and functional components of the data platform, such as unified front-end and back-end basic components, configuration and permission management, data source access, task scheduling, job management, search, etc.

The second is to design and implement a technical solution for the data middle platform in the field of DevOps. Next, he is responsible for the optimization, daily operation and maintenance and monitoring of the data platform to support business stability. Finally, the system structure of the governance data platform is optimized, and the design, development, and deployment of microservices are carried out.

Introduction:

Big data analytics refers to the analysis of huge amounts of data. Big data can be summarized into five Vs: volume, velocity, variety, value, and veracity.

Big data is the hottest term in the IT industry, and the ensuing use of the commercial value of big data, such as data warehousing, data security, data analysis, and data mining, has gradually become the focus of profit sought after by industry professionals. With the advent of the era of big data, big data analysis has also come into being.

Retail: Big data can be used to analyze consumer buying habits and trends, conduct market segmentation and personalized marketing, optimize chain management, improve inventory and demand planning.

Big data can help financial institutions with fraud detection, risk management, credit evaluation, market analysis and personalized financial services, while also supporting high-frequency trading and quantitative investment strategies.

Manufacturing: Big data is applied to production process monitoring, quality control, chain management, maintenance and intelligent manufacturing to improve efficiency, reduce costs and optimize production results.

Logistics & Transportation: Big data can be used for route optimization, cargo tracking, traffic congestion**, distribution routing and logistics efficiency improvements to deliver faster, more cost-effective transportation services.

Marketing: Big data can be used for customer segmentation, market trend analysis, brand reputation management, social ** monitoring, and marketing effectiveness measurement to improve the effectiveness and return of marketing campaigns.

Telecommunications: Big data can be used to analyze user communication behavior and consumption patterns, provide personalized products and services, improve network performance and planning, and improve network failures and demand growth.

Agriculture: Big data can be used in agriculture for soil analysis, meteorology, crop growth monitoring, precision agriculture management, and water resource management to improve agricultural production efficiency and crop quality.

Internet of Things (IoT): IoT devices generate large amounts of data, and big data analytics can be used to monitor and manage IoT devices, optimize device performance and usage, and extract insights from device data.

Education: Big data can be used to analyze students' learning behaviors, personalize teaching recommendations, evaluate student performance, manage school resources, and formulate educational policies to improve the quality and effectiveness of education.

Social**: Big data analytics can be used to analyze user behavior, recommend content, target ads, and social trends on social platforms to improve user experience and advertising effectiveness.

Urban planning: Big data can help urban planners analyze data such as population movements, transportation patterns, energy consumption, and environmental indicators to optimize urban planning and infrastructure construction and improve sustainable urban development.

Human resource management: Big data can be used for recruitment analysis, employee performance evaluation, training needs analysis, employee benefits management, and employee attrition** to support more effective HR management decisions.

Crime prevention and security: Big data analytics can be used to analyze crime patterns, security incidents**, surveillance system optimization, and border security management to improve crime prevention and public safety.

These examples demonstrate the wide range of applications of big data across multiple industries. As technology continues to evolve and data continues to grow, big data will continue to play an important role in various fields, bringing more opportunities for innovation and improvement.

Big data can do the following:

1. Understanding of information. Every piece of information, every news, every advertisement you send is information, and your understanding of this information is an important field of big data.

2. User's understanding. The basic characteristics of each person, your potential characteristics, the habits of each user on the Internet, etc., are all understandings of users.

3. Guan Husen section system. Relationship is the core of us, the relationship between information and information, the relationship between one microblog and another, the relationship between one advertisement and another. The relationship between a Weibo and a ** is relatively simple when we look at it with the naked eye.

Big Data Terminology:

1. Apache Software Association (ASF).

There are many open source projects for big data, and there are currently more than 350 projects. is a non-profit organization dedicated to supporting open source software projects. The software products distributed in the Apache projects and subprojects it supports are licensed under the Apache License.

2、apachemahout

Mahout provides a library of pre-made algorithms for machine learning and data mining, and is also a well-known environment for creating more algorithms. In other words, a heavenly environment for machine learning.

3、apacheoozie

In any programming environment, there is a need for some workflow system to schedule and run work in a predefined manner and with defined dependencies. The big data work provided by Oozie is written in languages such as Apachepig, MapReduce, and Hive.

Big data technology is an interdisciplinary discipline: statistics, mathematics, and computer science are the three supporting disciplines; Biology, medicine, environmental science, economics, social science, and management are applied and expansive disciplines. In addition, it is also necessary to learn data collection, analysis, processing software, mathematical modeling software and computer programming languages, etc., and the knowledge structure is a cross-border talent (with professional knowledge and data thinking).

First, with the development of the Internet of Things and cloud computing, data valorization is an inevitable trend, and big data is the inevitable result of this trend. At the same time, the Internet of Things, cloud computing, and big data are the representative technologies of the contemporary information society.

Second, the development of big data is in its infancy. At present, big data is in the process of transforming from concept to industry, and the industrial chain of big data is also improving, so with the continuous development of big data, big data will create more development opportunities and jobs.

Third, big data is becoming an important force driving the development of science and technology. The development of big data has greatly promoted the development of the field of artificial intelligence, and many of the current research in the field of artificial intelligence are based on big data, including the "Internet brain" developed by many technology companies, which take big data as an important component. It is believed that with the continuous development of artificial intelligence, big data will play a more positive role.

At present, with the gradual implementation of big data applications, a large number of enterprises need professional big data people to complete the design and deployment of big data solutions, and at the same time, the scenario-based application of big data will release a large number of jobs, so big data will absorb a large number of professionals in the future. As a big data professional, the future of development will be huge.

Big data is widely used in many industries, including finance, e-commerce, healthcare, and logistics.

1.Finance: Big data plays an important role in the financial industry.

By analyzing a large amount of financial data, it can help banks and financial institutions assess risks, formulate investment strategies, conduct markets**, etc. For example, banks can use big data technology to assess borrowers' creditworthiness and improve the accuracy and efficiency of lending decisions.

By analyzing user behavior data, transaction data, and product data, e-commerce companies can better understand consumer needs, customize personalized recommendation services, and increase product sales and user satisfaction.

3.Healthcare: Big data has great potential in healthcare.

By analyzing large amounts of clinical, biological, and medical record data, doctors and researchers can discover disease patterns and trends, improve diagnostic accuracy, and provide patients with better solutions. At the same time, big data can also be used to monitor the spread of diseases and epidemic trends, and help public health authorities develop corresponding control strategies.

4.Logistics industry: The logistics field is also one of the industries where big data is widely used. By monitoring and analyzing transportation data, warehousing data, and order data in real time, logistics companies can optimize routing, reduce transportation costs, and improve the efficiency and on-time performance of goods distribution.

In addition, big data is also widely used in industries such as energy, agriculture, transportation, and manufacturing. In short, big data technology has brought more business opportunities and development space to all walks of life, and the application of big data with the goal of improving efficiency, innovating services and optimizing decision-making has broad prospects.