Elastic, the company behind Elasticsearch, and the Elastic Stack, the most widely used collection of open source products for solving mission-critical use cases like search, logging, and analytics, has acquired Prelert, a leading provider of behavioral analytics technology. Elastic will integrate the Prelert technology into the Elastic Stack, and will offer it as part of its subscription packages in 2017, giving Elastic customers more capabilities to solve complex use cases such as cybersecurity, fraud detection, and IT operations analytics, among others. Prelert was founded in 2008 to create technology that automates the discovery of anomalies in large, complex datasets, predicts actions and outcomes, and provides enterprises and their end users with a consumable application that doesn't require them to perform data science. Using unsupervised machine learning techniques applied to a customer's historical and real-time continuous data, Prelert's predictive models perform behavioral analytics to understand the probability of failures and events occurring with built-in alerting and notifications for end users to explain 'why' something has happened and 'what' to do with that information. More companies, from startups to large enterprises, are storing large amounts of structured and unstructured data in Elasticsearch. With 'search' becoming the foundation for many of these companies to address their most complex use cases, Prelert built an Elastic Stack integration to provide Elasticsearch users with an automated way to understand the 'why' in their data and take action on 'difficult to see' insights. Combining Elastic's Kibana user interface framework and Prelert's behavioral analytics technology, customers are able to solve common problems in their continuous and ever-growing data, including:

• detecting advanced security threat activities and anomalies in log data,
• discovering hidden fraud patterns in highly sensitive data,
• identifying anomalous systems or metrics and their root causes across IT systems,
• linking together complex series of events in data to expose early warning signals,
• automatically pinpointing where and why critical system outages are occurring,
• detecting unexpected drops in transactional activity, and much more.