Shulou(Shulou.com)05/31 Report--

This article mainly introduces the use of Apache Flink, has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, the following let the editor take you to understand it.

Apache Flink is the data processing engine of the fire in recent years. It is highly respected by major manufacturers and has been applied to actual business scenarios. Many companies will choose Apache Flink as the object of selection. So what can Apache Flink do for enterprises?

In the actual production process, there are a large number of real-time data analysis requirements, such as real-time recommendation, exception alarm, sensor signaling analysis and so on. With the continuous development of technology, there are more and more requirements for the implementation of indicators, and the demand for timeliness is getting higher and higher. Using the traditional technical scheme to wait for the data to land before analysis, then the timeliness information of the data can not be guaranteed. Therefore, when dealing with the business of some scenarios, we need a data engine that can ensure timeliness and the ability to deal with large-scale data. So Apache Flink is a very good choice.

Real-time recommendation system

With the advent of the era of data explosion, data from the traditional active retrieval to the current recommendation system. The recommendation system is also involved in all kinds of industries from the original e-commerce platform to the present. E-commerce platform, news and information platform, advertising alliance and so on will use real-time recommendation system to make more intelligent recommendations for users.

For users, based on user behavior, user information and other related data, real-time intelligent recommendation can play the role of information filtering and reduce interference. Directly give users the information they need to improve user satisfaction.

For businesses, accurate user marketing can increase sales and customer satisfaction.

Real-time report

It is believed that everyone has participated in Tmall's Singles' Day activities, and the real-time big screen is a necessary part of Singles' Day in recent years. The big screen shows the current order quantity, transaction amount and other information in real time.

Ali uses Apache Flink to analyze and calculate the data of double Eleven big screen and push the calculation results directly to the front-end big screen. The time from the landing data collection, verification, conversion, correlation analysis, calculation results to the display of data is controlled in seconds.

Now companies in all industries will also add real-time reports to the daily index analysis in order to increase the analysis of the current situation.

Real-time data warehouse and ETL

Through the association with the offline data warehouse, the data of the offline data warehouse are merged and supplemented by the ability of the real-time computing engine. In addition, based on the ETL capability of real-time computing, streaming computing directly analyzes ETL data, and based on the stateful computing characteristics of Apache Flink, it reduces the related costs of offline data analysis and scheduling. Provide the required analysis results more efficiently.

Complex event processing

Apache Flink contains a feature CEP, the full name is Complex event processing complex event handling. The so-called complex event is not a simple event. In many cases, the resulting user behavior may be caused by a combination of events associated with different contexts. For example, in industry, we define a detection anomaly in advance. Through the matching of each series of pattern events, the final trigger alarm detection and so on.

Apache Flink provides CEP event packages, provides related rule engines, and supports SQL-style transformations. Implement to define complex events in a streaming system.

Real-time Fraud and Real-time Credit Evaluation

In the financial field, there is a strong demand for fraud detection and credit evaluation. In traditional solutions, such as credit card fraud and underground industry detection, it may take hours or even days to analyze. When the test is completed, the lawbreakers may have completed the crime, causing damage to the company's property. At the same time, there are more and more scenarios such as network lending and real-time transactions, so the real-time requirements of fraud, underground industry and user credit evaluation are higher. Using Apache Flink to achieve real-time abnormal order detection, abnormal user detection and user credit evaluation can achieve millisecond anomaly analysis. Timely avoid economic losses and speed up users' credit evaluation for business growth.

Big data safety monitoring

The data security direction has higher and higher requirements for real-time, such as hacker attacks, abnormal network traffic analysis, abnormal user behavior and so on. Some big data security scenarios use public network egress traffic for analysis. For example, if the normal bandwidth is 10Mb, alarm behavior will occur if it suddenly increases to 20 or higher. Based on some conventional indicators + machine learning scheme to achieve abnormal behavior alarm and blocking and so on. If the less real-time scheme is adopted, and the analysis of the abnormal results occurs after a few hours, then the abnormal behavior may have occurred. For example, the hacker of dragging library attack has obtained the relevant data. The use of real-time computing engine can quickly detect abnormal conditions, analyze and block anomalies.

Apache Flink for real-time data processing and analysis can cover more application scenarios, maybe some scenes I have not been in contact with, just output the scenarios I have known to you. If there are more scenes, you can discuss and communicate together.

Thank you for reading this article carefully. I hope the article "what's the use of Apache Flink" shared by the editor will be helpful to everyone? at the same time, I also hope that you will support and pay attention to the industry information channel, and more related knowledge is waiting for you to learn!

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