What is Flink CEP?
Flink CEP is a real-time processing framework that can process streaming data. Being founded by Data Artisans company and now developed under Apache License by Apache Flink Community, it has been marked as an actual streaming model that does not take input data as batch or micro-batches. But what does one understand from the term 'CEP'?
What is Apache Flink?
Apache Flink is a distributed processing framework and engine for stateful computations over unbounded and bounded data streams. It processes data streams on a large scale and provides real-time analytical insights into your processed data through your streaming application.
Complex Event Processing (CEP)
CEP is a library implemented on the top of Flink. It is of great use in the current era when data is considered as necessary as oil, which is constantly growing. Data is continuously streamed from intelligent devices, which is a great deal to analyze in real-time. CEP comes into play in such scenarios.
- It solves the key problem in real-time processing of pattern detection of events in data streams.
- It matches continuously incoming data against a suggested pattern. This helps in keeping the data that’s currently needed and discarding the non-relevant data. Inputs are matched immediately, and the results are emitted straight away.
- It helps you to detect patterns in a data stream, allowing you to get hold of only the important data.
CEP’s are written using Pattern API’s.
Use Cases of CEP
CEP is used in many cases and scenarios. Notably, it is used in the following:
- Financial apps to check the trend in the stock market
- Credit card fraud detection
- RFID-based tracking and monitoring systems (like detecting thefts in the warehouse)
- Detecting network intrusion by specifying patterns of suspicious behavior
CEP is of great use in today's scenario of Data Analysis. Although the data is necessary, only a handful is significant at a given time. We can quickly get this handful of data by using CEP in Apache Flink. CEP can achieve high throughput and low latency processing. It can also produce the results as soon as the input stream is available. It provides necessary real-time alerts and notifications for detecting complex event patterns.
Anatomy of CEP
Below are specific guidelines to be kept in mind while operating Flink CEP.
- Obtain an Execution Environment (Stream or Batch environments)
- Load Data from the defined source (Like Apache Kafka Security)
- Pattern Definition (To get the desired data from the incoming stream)
- Pattern Detection (Getting the desired data)
- Creating of alert
- Specify where to sink the results after computation (Kafka topics, Database, etc.)