Amazon Elasticsearch Service vs. Rockset

Elasticsearch is a good alternative to relational databases for setting up complex searching of data. It's inbuilt features for slicing the data [in] different ways and its ability to add weights to search results makes it easy to set up complex searching scenarios. Given that data must be pushed to this service, it may be best suited for data that is not changing very rapidly.

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1. Usage based billing - helpful in creating query lambdas and workflow around them. 2. Fast data refresh and scheduled running of queries is required. 3. Wherever real-time data is in play in terms of visibility.

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• Search
• Query language
• light-weight

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• Fast ingest and real-time indexing
• Complex SQL queries run really fast
• Lower cost in terms of deployment.

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• It is not so simple to learn.
• Documentation is often very confusing.

• User interface
• AI in queries, auto-generated queries
• Graphing tools

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It is an extremely powerful tool if the time is put in to learn it. There are basic skeletons of out of the box behavior, it involves having really dedicated people to learn how to use it to take full advantage of its capabilities. A 10 for the tool itself, minus 3 for the difficulty in learning and maintenance

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Splunk is the most flexible of the 3 where you can manipulate the data to whatever fits your specific use case. Grafana has the most powerful capabilities but the steepest learning curve. Grafana also does offer the most flexibility as you can visualize almost any data source. Elastic is a solid middle ground between the 2

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• The cost is a bit expensive if compared with other clouds but [the] performance is very good.

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• Didn't need to invest in an external billing software.
• Major customer feature ask was fulfilled - realtime usage overview

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