Amazon Elasticsearch Service Reviews & Insights

We use AWS Elastic to compile metadata options for products that have a lot of options on them, things like rugs with multiple sizes, colors, patterns, shapes, and so on. This helps us to create one source of truth that we can then call through the API to get the data back we need through a lightweight webservice call.

Elasticsearch is well suited for somebody that does not have any search capabilities or observability already in place. The ECS schema is a good one to adopt to structure your data in a usable and serviceable way.

Elasticsearch like most SaaS observability offerings, can be difficult to learn, and can have extra charges based on storage, replication, and ingest needs.

1. With Amazon Elasticsearch Service, we analyze logs of our microservices-based application to fix bugs. 2. With Amazon Elasticsearch Service, we accumulate data across the microservices of our product for visualization & other business purposes. 3. The benefit of elastic search is that, since data is in JSON format, all our microservices can easily change the data structure.

Supports geographical data for location-based search where Amazon Elasticsearch Service is suited best. Amazon Elasticsearch Service provides various text analysis functionality for my application's data. For financial applications storing transactional records, since concurrency is very important in that case, Amazon Elasticsearch Service is not very well suited for those cases. Traditional ways to store data like Oracle SQL Database used to handle this problem much better.

AWS container is very fast, imagine previously I [spent] at least 30 minutes [setting] up one VM in my local server infrastructure [and] I [spent] around 150k to set up local server infrastructure with SAN, and [paid] a lot [for] electricity, internet, and firewall. But when I moved to ECS, I [found] it changed my work a lot. I deploy the server for developer and production use [in] only one minute. Fast provisioning vm and easy to scale my vm to bigger ram, cpu, hdd.

Previously we started using vmware, but it's very difficult [and] takes a lot of time to create one VM, but since we started using [Amazon Elasticsearch Service] it's great, fast, [and] it saves our IT [a lot of] time, especially for development.

Our team is using [Amazon] Elasticsearch to help us perform user lookups of data in our system. Instead of spending a lot of time and energy creating optimized queries in SQL Server, we were able to use Elasticsearch to quickly set up a system that searched through multiple data points and provide weighted results. One of the things we like the best is how easy it has been to set up the search, to do partial search matches for some data points, exact search matches for other data points, and then weight the results so that the best results are displayed first. This has allowed us to create a single "Google-like" search with minimal development effort.

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.

We use Elasticsearch to offer modern reporting to our users. They can quickly sort thru large datasets and see the information that matters to them.

Amazon offers a simple to use and turnkey implementation of Elasticsearch. All the dev-ops side of implementing Elasticsearch is handled with a simple and intuitive UI. I would recommend Amazon Elasticsearch Service to others.