Amazon CloudSearch vs. Amazon DynamoDB vs. Amazon Elasticsearch Service

Amazon Cloudsearch can be suitable for some queries that require fast data. For example, in our case, we used CloudSearch, in a tool called Global Search. That will search everything like names, emails and a lot of stuff in our application. If you want fast data and you have a simple query, Global Search isn't appropriate for you.

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It’s great for server less and real-time applications. It would be great for gaming and mobile apps. However, if you need relational database and have fixed budget, do not use it. While budget can be managed, you need to be careful. Also this is not a tool for storing big data, there are other wide-column database types you could use for it ins the ad

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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• Really fast queries
• Good Reporting
• Reduce the cost of the server

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• To manage varying workloads, it enables users to increase capacity as necessary and decrease it as needed.
• Users can take advantage of its auto-scaling, in-memory caching, and backup without paying for the services of a database administrator.
• We can use it for low scale operations.

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• Search
• Query language
• light-weight

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• Can take some time to implement
• The initial configuration can be tricky
• Takes some time to update the values

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• Cost model may not be easy to control and may lead to higher costs if not carefully planned
• Indexing may be a cost culprit when not planned, because it's not included on the data costs
• The Query Language may not fulfill everybody's expectations, as it has less features than those of competitors.

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

It's core to our business, we couldn't survive without it. We use it to drive everything from FTP logins to processing stories and delivering them to clients. It's reliable and easy to query from all of our pipeline services. Integration with things like AWS Lambda makes it easy to trigger events and run code whenever something changes in the database.

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Functionally, DynamoDB has the features needed to use it. The interface is not as easy to use, which impacts its usability. Being familiar with AWS in general is helpful in understanding the interface, however it would be better if the interface more closely aligned with traditional tools for managing datastores.

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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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It works very well across all the regions and response time is also very quick due to AWS's internal data transfer. Plus if your product requires HIPPA or some other regulations needs to be followed, you can easily replicate the DB into multiple regions and they manage all by it's own.

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I have not had to contact support for this service, however I have had to contact AWS for other services and their support has been good.

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I didn't investigate the best alternatives to CloudSearch, but did help with implementing this feature in our application. But from what i tested and used - Cloudsearch is very fast to get queries. Some negative points can be the time to implement this and some configurations that can be tricky.

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The only thing that can be compared to DynamoDB from the selected services can be Aurora. It is just that we use Aurora for High-Performance requirements as it can be 6 times faster than normal RDS DB. Both of them have served as well in the required scenario and we are very happy with most of the AWS services.

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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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I have taken one point away due to its size limits. In case the application requires queries, it becomes really complicated to read and write data. When it comes to extremely large data sets such as the case in my company, a third-party logistics company, where huge amount of data is generated on a daily basis, even though the scalability is good, it becomes difficult to manage all the data due to limits.

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• Positive Point - Reduced the server load
• Negative point - Not suitable for all queries
• Negative Point - Time to implement this feature

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• Some developers see DynamoDB and try to fit problems to it, instead of picking the best solution for a given problem. This is true of any newer tool that people are trying to adopt.
• It has allowed us to add more scalability to some of our systems.
• As with any new technology there was a ramp up/rework phase as we learned best practices.

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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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