1. What is big data?
Big data, IT industry terminology, refers to a collection of data that cannot be captured, managed and processed with conventional software tools within a certain time frame. It requires a new processing model to have stronger decision-making power, insight and discovery. Mass, high growth rate and diversified information assets of process optimization capabilities.
2. How to understand big data?
The first level is theory. Theory is the necessary way of cognition and the baseline for being widely recognized and disseminated. Here, we understand the industry’s overall description and characterization of big data from the definition of the characteristics of big data; from the discussion of the value of big data to in-depth analysis of the preciousness of big data; gain insight into the development trend of big data; from the special and important aspect of big data privacy The perspective of examining the long-term game between people and data.
The second level is technology, which is the means to reflect the value of big data and the cornerstone of progress. Here are the developments of cloud computing, distributed processing technology, storage technology, and perception technology to illustrate the entire process of big data collection, processing, storage, and formation of results.
The third level is practice. Practice is the ultimate value of big data. Here are four aspects of big data on the Internet, big data for government, big data for enterprises, and big data for individuals to describe the beautiful scene that big data has shown and the blueprint to be realized.
3. What problems can big data solve in enterprises?
(1) Companies that provide products or services to a large number of consumers can use big data for precision marketing;
(2) Small, medium and micro enterprises with a small and beautiful model can use big data for service transformation;
(3) Traditional enterprises that must transform under the pressure of the Internet need to advance with the times and make full use of the value of big data.
4. The development trend of big data?
Trend 1: Data resource utilization
What is resourceization means that big data has become an important strategic resource that enterprises and society pay attention to, and it has become a new focus that everyone is vying for. Therefore, companies must formulate big data marketing strategic plans in advance to seize market opportunities.
Trend 2: Deep integration with cloud computing
Big data is inseparable from cloud processing. Cloud processing provides big data with flexible and expandable basic equipment, and is one of the platforms for generating big data. Since 2013, big data technology has been closely integrated with cloud computing technology, and it is expected that the relationship between the two will be closer in the future. In addition, emerging computing forms such as the Internet of Things and mobile Internet will also help the big data revolution together, allowing big data marketing to exert greater influence.
Trend 3: Breakthrough in scientific theory
With the rapid development of big data, just like computers and the Internet, big data is likely to be a new round of technological revolution. The subsequent emergence of related technologies such as data mining, machine learning, and artificial intelligence may change many algorithms and basic theories in the data world and achieve breakthroughs in science and technology.
Trend 4: The establishment of data science and data alliance
In the future, data science will become a specialized subject and be recognized by more and more people. Major colleges and universities will set up special data science majors, and will also generate a number of new jobs related to them. At the same time, based on the basic platform of data, a cross-domain data sharing platform will also be established. Later, data sharing will extend to the enterprise level and become a core part of the future industry.
Trend 5: Data breaches are rampant
The growth rate of data breaches in the next few years may reach 100%, unless the data can be secured at its source. It can be said that in the future, every Fortune 500 company will face data attacks, regardless of whether they have taken security precautions. And all companies, regardless of size, need to re-examine today's definition of security. More than 50% of Fortune 500 companies will set up the position of Chief Information Security Officer. Enterprises need to ensure their own and customer data from a new perspective. All data needs to be secured at the beginning of its creation, not in the last link of data storage. Merely strengthening the latter’s security measures has proven to be useless.
Trend 6: Data management becomes core competitiveness
Data management has become a core competitiveness and directly affects financial performance. When the concept of “data assets are the core assets of an enterprise” became popular, enterprises have a clearer definition of data management, and regard data management as the core competitiveness of the enterprise, and continue to develop, strategically plan and use data assets, and become enterprise data The core of management. Data asset management efficiency is significantly positively correlated with the growth rate of main business income and sales revenue; in addition, for companies with Internet thinking, the competitiveness of data assets accounts for 36.8%, and the management effect of data assets will directly affect The financial performance of the business.
Trend 7: Data quality is the key to the success of BI (Business Intelligence)
Companies that use self-service business intelligence tools for big data processing will stand out. One of the challenges is that many data sources will bring a lot of low-quality data. To succeed, companies need to understand the gap between raw data and data analysis, so as to eliminate low-quality data and get better decisions through BI.
Trend 8: The degree of complexity of the data ecosystem is strengthened
The world of big data is not just a single, huge computer network, but an ecosystem composed of a large number of active components and multiple participant elements. Terminal equipment providers, infrastructure providers, network service providers, network access Enter the ecosystem constructed by a series of participants such as service providers, data service enablers, data service providers, touch point services, data service retailers, etc. Today, the basic embryonic form of such a data ecosystem has been formed, and the next development will tend to the segmentation of the internal roles of the system, that is, the segmentation of the market; the adjustment of system mechanisms, that is, the innovation of business models; the structure of the system Adjustment, that is, the adjustment of the competitive environment, etc., has gradually increased the degree of complexity of the data ecosystem.
5. How to learn big data?
I have probably learned about big data before, so how do you learn about big data? Then the dark horse must recommend the new 2021 Lunar New Year tutorial from the dark horse video library-zero-based 3 days quick start with big data!
This course is suitable for the following people:
1. No basic knowledge, computer foundation is enough.
2. At present, there are requirements for further improvement of the profession, and those who wish to engage in high-paying jobs in the big data industry.
3. Those who have continued interest in the big data industry.
The course content is as follows:
1. Introduction to Big Data and Career Planning
2. Linux server system
3. HDFS distributed file system
4. Hive Data Warehouse
5. Zeppelin framework
6. Sqoop framework
7. Superset framework
8. Didi Travel, a practical project of Hive data warehouse
Come and learn together!