

Denodo and Elastic Search compete in the data management category, focusing on virtualization and integration versus search and log management, respectively. Denodo presents an edge in data virtualization with its comprehensive integration capabilities, while Elastic Search outperforms in scalability and search functionalities.
Features: Denodo excels in data virtualization and integration, highly valued for performance and ease of use in complex environments. Its data catalog supports diverse data sources with robust query optimization. Elastic Search offers outstanding search and log management capabilities with scalability and extensibility, providing high-performance text-based searches across various data types.
Room for Improvement: Denodo's data catalog needs a user-friendly interface and enhanced integration with data semantics. Better training materials and documentation could enhance user experience. Elastic Search requires better alerting features and improved machine learning capabilities. Simplifying certain component configurations and scalability on large data sets would benefit initial deployment experiences.
Ease of Deployment and Customer Service: Both Denodo and Elastic Search allow flexible deployment options across on-premises, public, and hybrid cloud setups. Denodo may require more technical support and better documentation from customers, while Elastic Search's community support is strong, though technical expertise is sometimes necessary for optimal deployment and configuration. Denodo's customer service may experience support delays, while Elastic Search offers consistent support through community resources.
Pricing and ROI: Denodo is expensive but often offers significant ROI within six months, with costs tied to CPU usage and connector fees. Elastic Search provides a free open-source version, incurring costs related to technical expertise for deployment and support. Its enterprise features position its cost at par with similar solutions, offering a flexible model considered cost-effective compared to alternatives like Splunk.
If you don't need to write a whole ETL to make the data virtualization, you need way fewer people to write a query instead of writing an ETL pipeline.
I have seen a return on investment, which showed up in improved customer satisfaction scores.
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.
They have a good methodology for learning how to use the tool.
Denodo's customer support team is very competent and responsive.
If we raise a ticket, it can be resolved or addressed within a reasonable time frame, so support is good.
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.
For huge data requests, it cannot scale automatically; admin action is required.
Denodo's scalability comes into play specifically when there is data transfer.
While the solution scales well on a single machine, I have doubts about its scalability when deployed as part of a Java component cluster.
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.
I would rate it nine out of ten because it is very reliable, always performing as expected.
If JVM does not function properly, it may cause Denodo to fail to connect to different sources.
Denodo is stable and good.
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.
Ensuring data caching is up to date is critical.
Denodo needs better communication on how the product can be deployed for specific solutions.
The system has dependencies on other environments, like JVM, which can affect its performance.
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.
For small companies, it's not a solution that most small companies can afford.
Denodo is considered pricey, limiting its use to large enterprises or government organizations that can afford its comprehensive setup.
Denodo's pricing is not affordable for small companies and is more suitable for medium to large enterprises.
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.
You can host it on-premises, which would incur zero cost, or take it as a SaaS-based service, where the expenses remain minimal.
Denodo's ability to connect to multiple data sources and perform extract-transform-load (ETL) operations on the fly is noteworthy.
The most valuable feature of Denodo is its uniformity of self-site data access types, which allows it to connect to almost any storage technology and feed it transparently.
Denodo supports SQL base, so if you want to join two tables or two views, you can use SQL, which is an advantage as most developers or business people know SQL.
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.
| Product | Market Share (%) |
|---|---|
| Denodo | 2.8% |
| Elastic Search | 1.6% |
| Other | 95.6% |


| Company Size | Count |
|---|---|
| Small Business | 15 |
| Midsize Enterprise | 6 |
| Large Enterprise | 20 |
| Company Size | Count |
|---|---|
| Small Business | 37 |
| Midsize Enterprise | 10 |
| Large Enterprise | 43 |
Denodo is a leading data integration, management, and delivery platform that uses a logical approach to enable data science, hybrid and multi-cloud data integration, self-service BI, and enterprise data services. Organizations of different sizes across various industries utilize the product to get above the data silos. The solution offers organizations the freedom to migrate data to the cloud, or logically unify data warehouses and data lakes, without affecting business. This can ultimately result in the evolution of data strategies.
The platform accelerates data provisioning through reduced data replication, provides business users the freedom to select their preferred applications, and enables consistent security and governance across multiple systems. The solution offers one of the leading logical data fabric solutions by initiating data virtualization and eliminating the complexity and exposing the data in business-friendly formats.
Denodo also offers modern data integration and management for hybrid and multi-cloud environments for Denodo Platform for Cloud. This service can be purchased through the bring-your-own-license (BYOL) option. Users seeking faster deployment can license the product to popular cloud providers, including Amazon AWS, Google Cloud Platform, and Microsoft Azure. The solution integrates, manages, and delivers data in complex environments with high performance, governance, and security. It also offers additional solutions, such as the Denodo Platform for Cloud Modernization, the Denodo Platform for Cloud Data Integration, and the Denodo Platform for Cloud Analytics, which overcome common cloud data challenges.
Denodo Features
At the beginning of 2022, version 8.0 of Denodo introduced several new key features of the platform. These include:
Denodo Benefits
Denodo offers various benefits for its users through its services. Some of the greatest advantages of using this platform include:
Reviews from Real Users
Naresh M., a senior application developer at a financial services firm, appreciates Denodo because it offers quick and simple web services creation.
Alisson M., a senior BigData DevOps engineer at Schaeffler, says that Denodo is great for queries and scouting data.
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.
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