Search begets Search - Navigating your data progressively
Overall Satisfaction with Elasticsearch
We use Elasticsearch to Index and make available for Search and Navigation our proprietary data on the M&A landscape. It drives dashboards and alerts to allow users to monitor trends and the latest events that occur in our dataset. It aligns our research group with our bankers. We marry it to Couchbase and MS SQL-Server.
Pros
- Indexing text data
- Aggregations allow users to progressively add search criteria to refine their searches
- Find trends in our data with Aggregations
- Integrate text Search our taxonomy Search
Cons
- Joining data requires duplicate de-normalized documents that make parent child relationships. It is hard and requires a lot of synchronizations
- Tracking errors in the data in the logs can be hard, and sometimes recurring errors blow up the error logs
- Schema changes require complete reindexing of an index
- Text Search (Natural Language Processing)
- Integration with Couchbase
- Integration with multiple platforms including dot net and nodejs
- Aggregations and search done together
- Most of our investment is in programming hours which is expensive
- Easy to set up nodes
- Free version has a lot of the great basic features
Elasticsearch and Solr are both based on Lucene, but the user community for Elasticsearch is much stronger, and setting up a cluster is easier. Splunk is very well suited for Log indexing and searching but is not nearly as flexible as Elasticsearch. Couchbase is a great NoSQL database and is super fast as key value store, but it's indexing abilities are much weaker than Elasticsearch and can not do aggregates and listings in a single query
Do you think Elasticsearch delivers good value for the price?
Yes
Are you happy with Elasticsearch's feature set?
Yes
Did Elasticsearch live up to sales and marketing promises?
Yes
Did implementation of Elasticsearch go as expected?
Yes
Would you buy Elasticsearch again?
Yes
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