There is a larger increase in the availability of streaming data, which is largely repulsed by the rise of connected real-time data sources. These data generates technical challenges as well as opportunities. As more and more data is presented, transforming this data into actionable insights in real-time has now become an operational necessity.which is connected to the external data sources enable applications to integrate data into the application flow and also helps to update an external database with processed information.Model each stream in an unsupervised manner and observe the unusual and anomalous behaviors in real-time is the fundamental capability of. Detecting this kind of abnormal patterns in data which implies a security violation has innumerable possibilities.Here are some of the benefits:● Through data visualization, provides a deeper insightVisualization of important company information can help the company to manage its key performance indicators. These data can improve sales, reduce costs and can react to the risks quickly.● Renders insight into the behavior of customersIt allows companies to know about the preferences of customers and thus makes them to rapidly respond to their needs and demands. In this way,increases revenues through up-selling and cross-selling of goods and services.● Remain competitiveStream analytics can identify the trends, develop white papers, and generate forecasts of their company and their industry as well. This helps the company to become innovative, competitive and also to strengthen their brands.In earlier times, anomaly detection was operated by using rule-based techniques employed to static data processed in batches. However, this kind of batch anomaly detection becomes difficult when there was a larger increase in the data scenario. This paves the way to real-time anomaly detection using modern data science techniques.Real-time anomaly detection is distinct from the older batch anomaly detection. The reasons that led to the shift are efficient detection and looking at multiple aspects of detection including predicting and curbing anomalies in real-time.● Hand-codingOne of the new approaches in anomaly detection is hand-coding every data from scratch. Developing a custom solution from scratch,● Platform approachThis is used for bid data anomaly detection. This approach along with building anomaly detection models for streaming data, platform approach offers unified solutions to train and enable post-production monitoring of models. For example, StreamAnalytix, a real-time anomaly detection platform, which continually update anomaly detection modes.● Spark anomaly detectionEliminating too many anomalies is a tiresome work. You require a large data set on an analytics platform that can efficiently run detection algorithms. As a parallelized big data tool, Apache Spark is a perfect one for the task of anomaly detection. Thisprovides anomaly detection using spark which implements for data quality, cybersecurity, fraud detection and for other such business use cases. The included statistical models are:○ Z Score○ Robust Z Score○ Multivariate Non-Parametric Distribution○ Univariate Non-Parametric Distribution