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HCIA-Big Data | Big Data Application Fields and Big Data Computing Tasks

Latest reply: Apr 13, 2022 16:56:15 523 8 3 0 0

Hello, everyone!

This post will share with you big data application fields and big data computing tasks.

Big Data Application Fields

Big Data Era Leading the Future

Data has penetrated into every inch of the industry and business domain.

Discerning essences (services), forecasting trends, and guiding the future are the core of the big data era.

With a clear future target, seize every opportunity to harness big data to secure future success.

Application Scenarios of Enterprise - Level Big Data Platforms

Level Big Data  Platforms

This is an overview of the application scenarios of enterprise-level big data platforms. As indicated from the overview big data platforms offer services in many important fields such as telecom and finance industries governments and many other enterprises.

Big data platforms have been implemented to process data for the operation management supervision profession etc.

With strong appeals for data analytics in telecom carriers, financial institutions, and governments, the Internet has adopted new technologies to process big data of low-value density.

Marketing analysis, customer analysis, and internal operational management are the top three application scenarios of enterprise big data.

This is a bar chart showing the proportion of big data applications in many aspects.

In fact, we can tell that big data exists in almost every industry. Here show some industries use big data.

Marketing analysis

We can see from the picture that big data is mostly used in marketing analysis, customer analysis, and internal operational management. Just as we mentioned before retailing enterprises like Amazon or eBay use big data solutions to do marketing and customer analysis for improving their profits and the users benefit from big data by having a better experience.

Besides in each aspect, the proportion of 2019 is always higher than that in 2018 which means the application of big data is getting more and more attention in the industry.

Big Data Market Analytics

It is predicted that the overall scale of the big data industry will exceed CNY 1 trillion by the end of 2020, in

which industry-specific solutions and big data applications account for the largest proportion.

Overview of the big data industry in China

Overview of the big data industry in China

Market scale proportion of big data market segments

Market scale proportion of big data market segment

Moreover, we can have look at these two plots: From the left side, we can find it is predicted that the overall scale of the big data industry will exceed 1 trillion CNY by the end of 2020.

And the pie graph on the right is the market scale proportion of big data market segments. In the figure, the red part which represents the industry solution accounts for more than one quarter and after that, the big data applications the basic software computing, and analysis services also play important roles as well accounting for 15.3%, 12.5%, and 12.4% respectively.

Next, we can have a look at some cases in detail.

Big Data Application Scenarios 


This first case is about finance and here we can have a comparison of conventional finance and the new finance.

In the past both customers and financial institutions are passive. The customers need to get services in a fixed location and at a fixed time and they passively receive data as well as propagation from the market information while the financial institutions like banks cloud-only offer standard industrial services and focus on complicated processes and procedures.


But today things become much better. Now the new customers can obtain service anytime and anywhere and they become more active in acquiring meaningful information. As for the new financial institutions, they can use data mining algorithms to analyze user behavior and thus draw user portraits for accurate and personalized advertising and propagation.


Big data analytics has now been widely applied to the education field.


Big data provides technical support for basic education and higher education. It helps to analyze information including students' physical activities learning behavior exam scores and career planning throughout their whole adolescent student age.

Many of the available teaching data have been stored by the government agencies such as the National Center for Education Statistics in the United States for statistics and analysis.

The ultimate goal of big data analytics in the education field is to improve students' learning performance and provide students with personalized coaching and learning guidance.

Big data is also important in statistical analysis of academic performance for example enrollment rate dropout rate and rate of admission into higher schools can be used to promote balanced development of education.

Traffic Planning

Traffic planning: multi-dimensional analysis of crowds.

Traffic Planning

And about traffic planning, we can analyze the traffic crowed based on different ages or different ways of getting to some places or we can pay more attention to these regions where the traffic load hits a record high. And based on these results we could make road network planning or bus line planning in order to optimize the city's traffic.

Clean Energy

Clean Energy

And finally, for clean energy, Huawei has made great progress in Qinghai province in China. Qinghai is a place with strong winds throughout the whole year which means that we can use the wind to generate electricity.

But in the past several years the local government hesitated to invest money in wind power because the wind is unstable and difficult to control. Now thanks to big data technologies we can make more accurate predictions based on the historical data and have achieved to use wind power for 15 consecutive days. By doing so coal consumption is reduced by around 800000 tons and CO2 emission is reduced by 1.44 million tons consequently.

Since then we can be proud to say Intelligence + Data help to fight for clear water and blue sky.

Big Data Computing Tasks

I/O-intensive Tasks

  • I/O-intensive tasks are tasks involving network, disk, memory, and I/O.

  • Characteristics: The CPU usage is low, and most latency is caused by I/O wait(CPU and memory computing is far quicker than I/O processing).

  • More I/O-intensive tasks indicate higher CPU efficiency. However, there is a limit. Most applications are I/O-intensive, such as web applications.

  • During the execution of I/O-intensive tasks, 99% of the time is spent on I/O wait. Therefore, it is top priority is to improve the network transmission and read/write efficiency.

CPU-intensive Tasks

  • Characteristics: A large number of computing tasks are performed, including Pi calculation and decoding HD videos, which consumes CPU resources.

  • CPU-intensive tasks can be completed in parallel. However, more tasks mean a longer duration for switching tasks, and task processing on CPU will be less efficient. Hence, to put the best of CPU performance, keep the number of parallel CPU-intensive tasks equal to the number of CPU cores.

  • CPU-intensive tasks mainly consume CPU resources. Therefore, the code running efficiency is critically important.

Data-intensive Tasks

Unlike CPU-intensive applications where a single computing task occupies a large number of computing nodes, data-intensive applications have the following characteristics:

  • A large number of independent data analysis and processing tasks run on different nodes of a loosely coupled computer cluster system.

  • High I/O throughput is required by massive volumes of data.

  • Most data-intensive applications have a data-flow-driven process.

Typical applications of data-intensive computing:

  • Log analysis of Web applications.

  • Software as a service (SaaS) applications.

  • Business intelligence applications for large enterprises.

Major Computing Modes

  • Batch processing computing

    Allows you to process a large amount of data in batches. Major technologies: MapReduce and Spark

  • Stream computing.

    Allows you to calculate and process stream data in real time. Major technologies: Spark, Storm, Flink, Flume, and DStream.

  • Graph computing

    Allows you to process large volumes of graph structure data. Major technologies: GraphX, Gelly, Giraph, and PowerGraph.

  • Query and analytics computing

    Allows you to manage, query, and analyze a large amount of stored data. Major technologies: Hive, Impala, Dremel, and Cassandra.

Hadoop Big Data Ecosystem

Hadoop Big Data Ecosystem

That's all, thanks!

The post is synchronized to: HCIA-Big Data

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Admin Created Feb 21, 2022 12:27:05

HCIA for Bigdata HCIA-Big Data |  Big Data Application Fields and Big Data Computing Tasks-4698625-1
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olive.zhao Created Feb 21, 2022 12:29:43 (0) (0)
Created Feb 25, 2022 17:58:19

Thanks for sharing
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olive.zhao Created Feb 26, 2022 00:34:20 (0) (0)
Created Feb 26, 2022 03:30:20

A good overview for the topic in general.
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olive.zhao Created Feb 26, 2022 03:31:20 (0) (0)
Moderator Created Apr 13, 2022 16:56:15

thanks for sharing.
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VinceD Created Apr 13, 2022 16:56:32 (0) (0)


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