Elasticsearch

Greatly reduced data-in-transit and at-rest overheads

Provided us with a truly scalable solution for our data

Kibana offers a reporting platform based on our custom queries. Extremely useful for reports from automated test executions.

Data driven enabler

Capacity to scale

Unify IT operations

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

Finding talent.

Using open source versus paid.

Management if it gets too big.

It has allowed fast searching on large datasets which allow our customers to conduct business in a timely and simple manner.

Faster searches on our application have resulted in better usability and increased application use

Analytics dashboard has given our managers a better understanding of day-to-day activities

Being a backup data store, we need not touch SQL database while doing data dumps for local data science projects

Free alternative has given us incredible value at the cost of time and energy.

We spend 70-90% less time on investigations of incidents.

No initial investment required.

It's simple and the fast set-up spares you lots of money on the set-up stage.

Immediate results give back a lot without spending too much.

Improves developer velocity

Improves ability to diagnose problems

Was able to demo its background use at a conference quickly.

Was able to index 100s of documents quickly.

Easy to create queries.

Shorter outages since it is easier to figure out root causes

Better visibility over your environment

Security keeping your data at your own environment

Easy to maintain.

Simple to use.

Great monitoring out of the box for the product.

I am not in finance and I suspect even if I was this would be hard to measure. But for sure, Elasticsearch has enabled us to have the most flexible data model in the industry for our customer's data, and in doing so we have attracted many many technical customers and got much of their $$$.

One problem with Elasticsearch is that because it runs on the JVM, there can be some stop-the-world JVM garbage collections happening that can take down nodes and reduce indexing speed. The solution for that tends to be "let's just upgrade the CPU on that machine". And before you know it you are paying $$$ because this'll happen with 40+ machines.

On the other hand, I do think that ES is more efficient than other systems and so it requires fewer nodes to keep it highly tolerant and available, so we probably saved some money that way.

Investment is primarily the cost of developer time and maintenance time and with its cost being zero, Elasticsearch has a positive ROI.

Improved decision making

Better log investigations have helped early action on issues

Helped in digitizing the organization value chain for maintaining the applications

It reduced our solutions' time-to-market, as it's very easy to set up.

It greatly boosted the user experience with near-instant search results.

Elasticsearch can give you insights based on predictability, to do a Capacity Plan for infrastructure metrics.

The Visualization and Dashboards can give you a real view of business KPIs.

With OOTB anomaly detection, you can see potential issues with systems.

ElasticSearch was able to meet the high demands of our product when it mattered most.

Implementation of ElasticSearch was easy and quick, saving on the cost of implementation.

Managing ElasticSearch is very easy. With the right monitoring tools in place, it really is "set it and forget it".

Allowed us to give accurate, real-time feedback for RPA.

Able to see what works, and what doesn't work.

Allows us to identify consistent problems across 60+ different processes.

(Negative) Expense: Just Time. Early on, I had issues getting it installed in an exotic distribution, so labor/hours invested.

(Positive) Configuration and Modularity: You don't *have* to implement a full ELK stack. In fact, you could just run one, two or three components. However, marrying up ES with something like Syslog-ng, you combine two very powerful, feature-rich software packages in their own right, into an amazingly powerful data collection and gathering tool.

(Positive) Shallow learning curve: if you can write your own Unix configuration files, you will be able to maintain and develop on Elasticsearch.

Time saving

Employee time

Innovation

Quick to implement a powerful, effective search product that makes our user happy

Cheap, fast, and scalable - an ideal component of our stack

We have had great luck with implementing Elasticsearch for our search and analytics use cases.

While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.

We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.

Improved the speed of our website

Improved the user experience by providing highly efficient text search

We're using it for logging, which makes it easy to query the errors and solve them

It takes time to understand the advanced queries in Elasticsearch