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This article mainly introduces "how to use Hadoop to build modern telecom enterprise architecture". In daily operation, I believe many people have doubts about how to use Hadoop to build modern telecom enterprise architecture. I have consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts about "how to use Hadoop to build modern telecom enterprise architecture". Next, please follow the editor to study!

This paper summarizes the general rules of Hortworks telecom users using hadoop (mainly architecture and technology), and gives six use cases, including:

1. CDR Analysis Analyze Call Detail Records (CDRs)

2. Active device Service Service Equipment Proactively

3. Rationalize infrastructure investment Rationalize Infrastructure Investments

4. Recommended product Recommend Next Products to Buy (NPTB)

5. Real-time bandwidth allocation Allocate Bandwidth in Real-time

6. New product development Develop New Products

Because it is an article summarized by the application of the telecommunications industry, it has a lot of similarities with us, and it can also be used for reference to us.

Using Hadoop to build Modern Telecom Enterprise Architecture

Many of the world's largest telecom companies are using HDP to manage their data. By working with these companies, we learned how customers can use HDP to increase customer satisfaction, better invest in infrastructure and develop new products.

Partner Teradata recently gave some videos about how Verizon Wireless uses Teradata in conjunction with the Hortonworks data platform to keep their customer turnover below 1%.

The executive director of Rob Smith,Verizon Wireless describes how his team uses their discovery platform to improve interaction with customers:

Find a better way to communicate with customers about payment

Analyze social media to better understand customers' emotional changes in response to changes in Version's strategy

Customize marketing communication according to the independent needs of each customer

Smith describes how this new customer insight helps his IT and marketing team agree on business goals (for the benefit of Version customers)

Using Hadoop to realize Modern data Architecture

Our other telecom customers have identified their own Hadoop use cases, but all have similar Hadoop data architectures. These data architectures allow telecom companies to store new data types, retain the data for longer, and add different data sets together to gain new insights.

The following reference frame diagram describes a fusion of methods that are seen at our telecom customers.

Based on their hadoop modern data architecture, various types of telecom companies can implement the following six use cases (and more)

Analyze Call Detail Records (CDRs) CDR analysis

Telecom forensics dropped calls and poor voice quality, but call detail streams are streaming at millions of dollars per second. This high capacity makes pattern recognition and root cause analysis extremely difficult, and these often happen in real time, such as when customers are waiting to listen to music, delays can consume and hurt service profit margins.

Apache Flume can extract millions of CDR into Hadoop every second, while Apache Storm processes the data in real time and makes all kinds of distressing pattern recognition.

HDP makes it easier to keep long-term data to analyze the root causes of the problem, even years after the problem. CDR analysis can be used to continuously improve call quality, user satisfaction and service profit margins.

Service Equipment Proactively active device Services

The transmission tower and related connecting equipment constitute the backbone network of the telecommunications network. Tower failure can lead to a decline in the quality of service, and the cost of replacing equipment is usually higher than that of repair. This requires maintenance and optimization: neither too early nor too late.

Apache Hadoop stores unstructured, streaming sensor data in telecom networks. Through the comparison of real-time information and historical data, telecom operators can obtain the optimal maintenance arrangement. Machine learning algorithm can reduce the maintenance cost and service interruption caused by repairing equipment.

Rationalize Infrastructure Investments rationalizes infrastructure investment

Telecom marketing and capacity planning are related. The consumption of bandwidth and services can not be synchronized with the planned new tower stations and transmission lines. This mismatch between infrastructure investment and the actual return on investment poses a risk to income.

Web log data can help telecom companies understand the consumption of services in a particular state, county or adjacent area. With the accumulation of data over a longer period of time, they can analyze network load more rationally, which makes executives more accurate and confident in planning infrastructure investment.

Recommend Next Products to Buy (NPTB) recommends buying products

The telecom product portfolio is complex, and there are many cross-sales opportunities for stock customers. Salespeople make NPTB recommendations by face-to-face or telephone conversation, and there is generally little data to support their recommendations.

Based on the data of all users, HDP gives salespeople the ability to make NPTB recommendations with confidence. Confident NPTB recommendations enhance and improve the interaction between salespeople (or self-service) and customers.

Hadoop data Lake reduces sales resistance and creates NPTB's competitive advantage, just as Amazon has in e-commerce.

Allocate Bandwidth in Real-time real-time bandwidth allocation

Some applications greedily gobble up bandwidth and reduce the quality of service of the network to other visitors. Network managers cannot predict new super-popular applications, which can lead to spikes in network bandwidth and degrade network performance. Operators must respond quickly to the surge in bandwidth, reallocate resources and maintain SLAs.

Hadoop-based streaming data can help network operators visualize peak (bandwidth) peaks and agile throttling bandwidth in call centers. Text-based emotion analysis notes in the call center can also help (operators) understand the impact of these peaks on the customer experience. This insight helps maintain service quality and customer satisfaction, and inform strategic planning to build an intelligent network.

Develop New Products develops new products

Mobile terminals generate huge amounts of data, which shows that how, why, when, and where use these mobile devices. These data are extremely valuable to product managers, but other capacity and diversity intake, large-scale storage and analysis become difficult. Not all data is converted into business information storage, and even the stored data cannot be retained in its useful life.

Apache Hadoop can store data for longer periods of time economically. Product managers have these data with rich product value to help accelerate product innovation and capture product insights from specific regions and specific segments of customers. Quick big data feedback on product releases helps product managers to remedy deficiencies and maximize product release results.

At this point, the study on "how to use Hadoop to build a modern telecom enterprise architecture" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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