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In this post, we demonstrate how to build a robust real-time anomaly detection solution for streaming time series data using Amazon Managed Service for Apache Flink and other AWS managed services. It offers an AWS CloudFormation template for straightforward deployment in an AWS account.
The solution required collecting and preparing user behavior data, training an ML model using Amazon Personalize, generating personalized recommendations through the trained model, and driving marketing campaigns with the personalized recommendations. The user interactions data from various sources is persisted in their data warehouse.
Furthermore, the demand for skilled data professionals continues to rise; searches for “dataanalyst” roles have doubled in recent years as companies seek to harness the power of their data. ApacheKafka), organisations can now analyse vast amounts of data as it is generated.
Real-time Data Stream Analysis: Use Python with libraries like ApacheKafka and Apache Spark to process and analyze real-time data streams from sources like Twitter, sensors, or website logs. Implement real-time analytics to monitor trends or anomalies in the data.
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