

Find out in this report how the two Cloud Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
We have not purchased any licensed products, and our use of Elastic Search is purely open-source, contributing positively to our ROI.
It is stable, and we do not encounter critical issues like server downtime, which could result in data loss.
The main benefits observed from using Elastic Search include improvements in operational efficiency, along with cost, time, and resource savings.
I have seen a return on investment with TIBCO Spotfire; it has significantly reduced our manual report creation time and increased data-driven decision-making efficiency, so our ROI is good in that aspect.
The customer support for Elastic Search is one of the best I have ever tried.
They have always been really responsible and responsive to my requests.
It has been sufficient to visit conferences such as SCALE in Southern California Linux Expo, where Elastic Search has a booth to talk to their staff.
Having a local partner was crucial for us while they were there.
I would rate the customer support for TIBCO Spotfire nine out of ten.
TIBCO's support, especially for integration products, is swift.
I would rate its scalability a ten.
Since we're on the cloud, whenever we need to upgrade or add resources, they handle everything.
We haven't encountered any problems so far, and there is the potential for auto-scaling.
The platform was able to support multiple simultaneous users and connect to cloud analytics in the backend, making it quite scalable.
The data transfer sometimes exceeded the bandwidth limits without proper notification, which caused issues.
The stability of Elasticsearch was very high.
When you put one keyword, everything related to that keyword in your ecosystem will showcase all the results.
There are no significant stability issues as long as the product's capabilities are understood and not overly pushed.
The action logs allowed us to check traceability and see how mature and stable the setup was if corruption, performance drift, or synchronization issues occurred.
Overall, TIBCO Spotfire is a good product and has been a stable platform.
From a technical point of view, there are no significant issues recalled as Elastic Search has been absolutely awesome for this use case and covers 100% of the needs.
If I need to parse one million records saved into Elastic Search, it becomes a nightmare because I need to do the pagination, and it is very problematic in that regard.
Observability features like search latency, indexing rate, and maybe rejected requests should be added to make the platform more reliable and accessible for everyone.
TIBCO Spotfire and ClickView need improvement in scheduling, which is critical yet lacking in quality.
It could be very useful if the coding part of using the platform could be more user-friendly in terms of visual capability.
For regulatory submissions, we sometimes had to additionally deploy external tools to provide our client with a perfect export.
On the AWS side, it is very expensive because they charge based on query basis or how much data is transferred in and out, making it very expensive.
Having the hosted solution and not having to pay for essentially a DevOps person on staff to manage makes it affordable.
We used the open-source version of Elasticsearch, which was free.
TIBCO Spotfire's pricing is high, particularly in traditional markets like Turkey, impacting market penetration.
My experience with pricing, setup cost, and licensing for TIBCO Spotfire appears fair.
Elastic Search makes handling large data volumes efficient and supports complex search operations.
The most valuable feature of Elasticsearch was the quick search capability, allowing us to search by any criteria needed.
The speed with which Elastic Search is able to search through all of the documents we place into it is quite remarkable, as we search through 65 billion documents in less than a second in most cases, on a constant consistent basis.
TIBCO Spotfire integrates with R and Python, offering a differentiator from the competition.
Our client wanted a single analytic environment to serve as a one-stop place where their teams could explore, visualize, and collaborate in real time while maintaining security and regulatory expectations.
I think having Python included is a big win because you get the chance to customize your algorithm and your in-house development without having a big dependency on a third party.

| Company Size | Count |
|---|---|
| Small Business | 37 |
| Midsize Enterprise | 10 |
| Large Enterprise | 42 |
| Company Size | Count |
|---|---|
| Small Business | 18 |
| Midsize Enterprise | 12 |
| Large Enterprise | 42 |
Elasticsearch is a prominent open-source search and analytics engine known for its scalability, reliability, and straightforward management. It's a favored choice among enterprises for real-time data search, analysis, and visualization. Open-source Elasticsearch is free, offering a comprehensive feature set and scalability. It allows full control over deployments but requires managing and maintaining the infrastructure. On the other hand, Elastic Cloud provides a managed service with features like automated provisioning, high availability, security, and global reach.
Elasticsearch excels in handling time-sensitive data and complex search requirements across large datasets. Its scalability allows it to handle growing data volumes efficiently, maintaining high performance and fast response times. Integrated with Kibana, Elasticsearch enables powerful data visualization, providing real-time insights crucial for data-driven decision-making.
Elastic Cloud reduces operational overhead and improves scalability and performance, though it comes with associated costs. It is available on your preferred cloud provider — AWS, Azure, or Google Cloud. Customers who want to manage the software themselves, whether on public, private, or hybrid cloud, can download the Elastic Stack.
At its core, Elasticsearch is renowned for its full-text search capabilities, capable of performing complex queries and supporting features like fuzzy matching and auto-complete.
Peer reviews from various professionals highlight its strengths and weaknesses. Pros include its detection and correlation features, flexibility, cloud-readiness, extensibility, and efficient search capabilities. However, users have noted challenges like steep learning curves, data analysis limitations, and integration complexities. The platform is generally viewed as stable and scalable, with varying degrees of satisfaction regarding its usability and feature set.
In summary, Elasticsearch stands out for its high-speed search, scalability, and versatile analytics, making it a go-to solution for organizations managing large datasets. Its adaptability to different enterprise needs, robust community support, and continuous development keep it at the forefront of enterprise search and analytics solutions. However, potential users should be aware of its learning curve and the need for skilled personnel for optimization.
TIBCO Spotfire is a versatile data analytics platform that can handle large datasets and be deployed on-premise or in the cloud. Its dynamic reporting, easy creation of dynamic dashboards, and highly customizable tool make it a valuable asset for process data analysis, industrial performance dashboards, problem root cause analysis, business intelligence, and real-time analytics for IoT devices in the energy sector.
The solution also offers data science functions, map visuals, R and Python integration, and the ability to write HTML and JavaScript text areas. Spotfire has helped organizations deliver projects more efficiently and cost-effectively, allowing for faster data analysis.
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