Crest Data Systems developed Elastic Security, Observability, and Enterprise Search third-party integrations using Elastic framework and standards to ingest the data into the Elastic platform. This data includes logs/feeds for Security integrations, metrics for Observability integrations, and documents in case of Enterprise Search integrations.
Elasticsearch is a distributed, free and open search and analytics engine for all types of data, including textual, numerical, geospatial, structured, and unstructured. Elasticsearch which is built on Apache Lucene is now known as Elastic and is widely known for its scalability, extensibility, simple REST APIs, and speed. Elastic addresses three major use cases under a single hood which are Security Analytics, Observability and Monitoring, and Enterprise Search.
Elastic being a widely used analytics platform, bringing in the right amount of logs and parsing them is an absolute necessity. Given the fact that each data source whether it be Security, Observability, or Enterprise Search, will be data represented in different formats, normalizing it prior to ingestion becomes essential. To increase the challenge, as the number of new data sources to be integrated is large, there's a need for a well-defined integration development outline that standardizes the process of data collection, normalizations and building custom dashboards on top of them.
Crest Data Systems developed Elastic integrations for Security, Observability, and Enterprise Search use cases that help the user analyze and correlate their logs across multiple platforms. With this solution, the user can leverage Elastic to monitor their environment for any irregularities and perform textual searches on their documents laid over their entire organization. As part of the integrations development process, the following actions were implemented:
