Amazon Elasticsearch Service vs. Azure AI Search

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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Incredibly robust software for an enterprise organization to plug into their application. If you have a full development resource team at your disposal, this is great software and I highly recommend it. Largely, however, you won't be able to use this prior to the enterprise level. It's just too complicated and cumbersome of a product.

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• Search
• Query language
• light-weight

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• Azure Search provides a fully-managed service for loading, indexing, and querying content.
• Azure Search has an easy C# SDK that allows you to implement loading and retrieving data from the service very easy. Any developer with some Microsoft experience should feel immediate familiarity.
• Azure Search has a robust set of abilities around slicing and presenting the data during a search, such as narrowing by geospatial data and providing an auto-complete capabilities via "Suggesters".
• Azure Search has one-of-a-kind "Cognitive Search" capabilities that enable running AI algorithms over data to enrich it before it is stored into the service. For example, one could automatically do a sentiment analysis when ingesting the data and store that as one of the searchable fields on the content.

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

• Cross platform compatibility to integrate with various OS
• Optimizing latency.
• Nothing better than work on price, create more flexible options.

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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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Azure Search is a competitor against Google's own AI autosuggest a feature. We went with Azure because our network security folks found it to be more robust from a security standpoint, which is incredibly important when you have proprietary manufacturing information. Additionally, we're a Microsoft shop so it plugged into our cloud hosting package and client facing OS.

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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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• Azure Search enabled us to stand up a robust search capability with very few developer hours.
• The fully-managed service of Azure Search means we get low cost of management (EG, DevOps) going into the future, even though the cost of the service itself definitely reflects the time saved.
• Azure Search counts as a "Cognitive Service" for Microsoft Azure consumption and aligns our products with Microsoft's interests of driving an AI-first approach in the enterprise. Microsoft Partners, service and product companies alike, should be looking to align with this AI vision as it means favorable treatment from the Microsoft sales teams.

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