Even for organizations with strong roots in relational databases such as Oracle RDBMS, there may be valuable opportunities for leveraging additional data sources, for example to support special (search) use cases. Elastic Search (Index) is one of those data stores that can add value – for example to provide powerfur search capabilities to web applicaties, to handle metrics and logging output from live applications or to collect and analyze any data set in your landacape. The Elastic Stack also consists of Kibana (visualizations/dashboards), Log Stash & Beats (gathering data, for example through harvesting log files).
For developers with SQL at their fingerprints, after sometimes decades of relational querying, it can be a little challenging to get started with Elastic Search. Especially for these developers, I have compiled a Postman collection (the interface to Elastic Search is a REST API) and a Powerpoint Presentation. These two cover over two dozen of operations – index management (DDL) and data manipulation (DML) as well as searches – with the familiar Employees and Departments data set. The presentation lists these operations side by side: the left hand side of the slide shows the action in Elastic Search and the right hand side the more familiar Oracle SQL syntax. By showing equivalent statements in a well known language and the new to be grasped language, I hope to help Oracle SQL developers get kick-started with Elastic Search.
The searches make use of stored scripts, geo_point and geospatial operators, text searches, aggregations, highlighting, sorting, limiting, etc.
Note: the GitHub repository (https://github.com/lucasjellema/sig-elasticsearch-february-2018) also contains hands-on labs that you could make use of to get more acquainted with both Elastic Search and Kibana.