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This article is to share with you what the Flink application scenarios are. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

What is Apache Flink?

In the era of the surge in the amount of data, a large number of business data are generated in a variety of business scenarios. How to deal with these constantly generated data effectively has become a problem faced by most companies. Apache Flink is an open source distributed, high-performance, high-availability, accurate stream processing framework. Real-time streaming and batch processing are supported.

Flink is a distributed processing framework that can support high throughput, low latency and high performance among the technologies that have been developing in the open source community in recent years.

Application scenario

In the actual production process, a large number of data are constantly generated, such as financial transaction data, Internet order data, GPS positioning data, sensor signals, data generated by mobile terminals, communication signal data, etc., as well as our familiar network traffic monitoring, the log data generated by the server, the biggest common point of these data is generated in real time from different data sources, and then transmitted to the downstream analysis system. These data types mainly include real-time intelligent recommendation, complex event processing, fraud detection, real-time warehouse, and ETL type, stream data analysis type, real-time report type and other implementation business scenarios, and Flink has very good support for these types of scenarios.

1. Real-time intelligent recommendation

According to the purchase behavior of the user's history, intelligent recommendation will train the model through the recommendation algorithm to predict what the user may buy in the future. For individuals, the recommendation system plays the role of information filtering. For the Web/App server, the recommendation system plays a role in meeting the personalized needs of users and improving user satisfaction. The recommendation system itself is also developing rapidly. in addition to the algorithm becoming more and more perfect, the requirements for delay are becoming more and more demanding and real-time. Using Flink stream computing to help users build a more real-time intelligent recommendation system, real-time calculation of user behavior indicators, real-time update of the model, real-time prediction of user indicators, and push the predicted information to the Web/ App side to help users get the product information they want. on the other hand, it also helps enterprises to increase sales and create greater commercial value.

two。 Complex event processing

For complex event processing, it is more common to focus on the industrial field, such as on-board sensors, mechanical equipment and other real-time fault detection, these business types are usually very large amount of data, and the timeliness of data processing requirements is very high. By using the CEP provided by Flink to extract the time pattern, and using the Sql of Flink to convert the event data, the implementation rule engine is constructed in the streaming system. Once the event triggers the alarm rule, it immediately notifies the alarm result to the downstream notification system, so as to achieve the purpose of rapid early warning and detection of equipment faults, vehicle status monitoring and so on.

3. Real-time fraud detection

In the financial field, various types of fraud often occur, such as credit card fraud, credit application fraud and so on. How to ensure the capital security of users and companies is a common challenge faced by many financial companies and banks in recent years. With the continuous upgrading of fraudulent means of lawbreakers, the traditional anti-fraud methods are no longer enough to solve the current problems. In the past, it may take several hours to calculate the user's behavior index through the transaction data, and then identify the suspected fraudulent users through the rules, and then investigate and deal with the case. in this case, funds may have long been transferred by illegal elements, thus causing a lot of economic losses to enterprises and users. The use of Flink streaming computing technology can complete the calculation of fraud judgment indicators in milliseconds, and then intercept the transaction flow in real time to avoid economic losses caused by untimely processing.

4. Real-time data warehouse and ETL

Combined with the offline data warehouse, the real-time cleaning, merging and structured processing of the streaming data are carried out by making use of the advantages of stream computing and the flexible processing ability of Sql, so as to supplement and optimize the offline data warehouse. On the other hand, combined with the ETL processing ability of real-time data, using stateful flow computing technology, we can reduce the complexity of scheduling logic in the process of offline data computing as much as possible, deal with the statistical results needed by enterprises efficiently and quickly, and help enterprises to better apply the results analyzed by real-time data.

5. Stream data analysis

Real-time calculation of all kinds of data indicators, and the use of real-time results to adjust online related strategies, there are a large number of applications in all kinds of content delivery and wireless intelligent push fields. Streaming computing technology makes data analysis scenarios real-time, helping enterprises to analyze various indicators of Web or App applications in real time, including App version distribution, Crash detection and distribution, etc., while providing multi-dimensional user behavior analysis to support log independent analysis, helping developers to achieve fine operations based on big data technology, improve product quality and experience, and enhance user stickiness.

Thank you for reading! This is the end of this article on "what are the Flink application scenarios?". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, you can share it for more people to see!

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