Prelert announced the release of an Elasticsearch Connector to help developers quickly and easily deploy its machine learning-based Anomaly Detective engine on their Elasticsearch ELK (Elasticsearch, Logstash, Kibana) stack.
Earlier this year, Prelert released its Engine API enabling developers and power users to leverage its advanced analytics algorithms in their operations monitoring and security architectures. By offering an Elasticsearch Connector, the company further strengthens its commitment to democratizing the use of machine learning technology, providing tools that make it even easier to identify threats and opportunities hidden within massive data sets.
Written in Python, the Prelert Elasticsearch Connector source is available on GitHub. This enables developers to apply Prelert’s advanced, machine learning-based analytics to fit the big data needs within their unique environment.
“Prelert is dedicated to making it easier for users to analyze their data and drive real, actionable value from it,” said Mark Jaffe, CEO, Prelert. “The amounts of data that companies and organizations have these days are simply massive – too massive for humans to process and analyze. The release of our Elasticsearch Connector is the latest step toward making the analysis of large data sets possible, repeatable and valuable without a team of data scientists.”
Prelert’s Anomaly Detective processes huge volumes of streaming data, automatically learns normal behavior patterns represented by the data and identifies and cross-correlates any anomalies. It routinely processes millions of data points in real-time and identifies performance, security and operational anomalies so they can be acted on before they impact business.
The Elasticsearch Connector is the first connector to be officially released by Prelert. Additional connectors to several of the most popular technologies used with big data will be released throughout the coming months.
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