Powerful, distributed, RESTful, JSON-based Search & Analytics engine
Server-side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a “stash”.
Visualize data stored in Elastic with charts and graphs
What we achieve from ELK?
Valuable business insights - Yes ! In current era, data is growing like anything & thus becoming difficult to read and analyzed the data. For any applications, logs behaves as support systems, which kind of keeps an eye on application behavior. If an application is getting hundred thousands request per day, it would be very difficult to analyze all these request e.g. count of requests , number of successful/failed requests , peak time of requests etc.
Also reading those huge log files is a mess. Here my friends can say why to read log files, keep you audit data in DB tables - believe that’s perfect but one have to write complex SQL queries to fetch required data & believe me in most of application system, one will not get production database rights to do so. My other friend says use Business intelligence tools to achieve same- Yes absolutely true but my submission here is that via ELK stack objective is not to achieve high levels. ELK helps you achieve very simple objectives mentioned below
- Push any type of application data to Elastic & view the same via kibana
- View all access /error/application logs via Kibana. No need to access logs folders in production grade environment
- Create different dashboards for data Analytics (easy as compare to BI tool that reads databases)
Dimit Chadha ARCHITECTURES · FRAMEWORKS
architectures frameworks ELK logstash Elastic Filebeat