I am switching from Denodo to other solutions. Currently, I work as a Data Engineer Team Lead at an Etex company, where I work with Oracle Data Integrator (ODI), Power BI, and sometimes Tableau. I use Oracle Data Integrator as my primary tool.

Denodo is part-time work with a Saudi company. My main position involves working as a team lead for ODI and Power BI. As a consultant, I determine whether Denodo is used in companies or if their customers are using it.

My Saudi company is Denodo partner in Egypt, and we work for clients in Saudi Arabia. I have been working with Denodo for the last three years.

I find the features related to data governance most valuable. The data catalog is particularly interesting, and it provides an overall point of view that is useful. From my experience as a data engineering team lead, I can build a data warehouse in just a minute from different sources without needing time for data transfer or any additional work.

Denodo can be improved by entering new markets. I am in Egypt now, and I think there are not enough companies dealing with Denodo. I faced issues when trying to find a new job using Denodo in Egypt, as it is very hard to find opportunities. Denodo should tap into new markets in the Middle East. Last year, I gave training for Saudi clients in Dubai, and even in Dubai, it needs time to find training centers or trainers to help spread Denodo practices.

I have been working with Denodo for the last three years.

I experienced a performance-related issue the last time I dealt with my client in Saudi Arabia, but I solved it by using some workarounds.

The issue I faced was related to data volume. My client has almost 100 million records, and the performance was impacted in a way that required optimization.

I did not always contact technical support from Denodo. I only contacted them when I took an exam as I am a certified Denodo consultant and developer. I faced a problem during the installation of Denodo on my laptop, and they helped me greatly to bypass this issue. This happened two years ago.

I solved the problem by myself.

The implementation is not complex at all for Denodo.

Denodo's security features are very aligned with my customer expectations. I can implement row-level security or column-level security, and I can hide columns from some departments. These security capabilities are very interesting.

I am satisfied with the performance for sure.

I did not deal with the pricing of Denodo as my company's financial requirements are managed by another team, so I do not know how much it costs.

Denodo's interoperability options make the integration process very easy.

I experienced something interesting when I integrated Denodo with SAP, and it was fantastic. Data governance is something very interesting as Denodo helped clients get what they need. I did not use the new AI features, as I expect no additional features from them related to generative AI. I did not deal with vector databases and do not have experience with them. I would generally recommend Denodo to others. They need to promote the product in the Middle East, but I am recommending Denodo to different companies. I am not sure how customers are dealing with Denodo from the AWS Marketplace or directly. I give this review a rating of eight out of ten.

My main use cases for Denodo involve implementing APIs to connect different data sources with new software to improve the process in Ecopetrol.

Denodo's best features are that it is very user-friendly and easy to use. It is flexible, and the data catalog is very useful.

The data cataloging has helped me in data discovery because this feature is very useful. All the people of the company can find different data for different projects. We created a data catalog for the company and to publish the API, and the different projects can use the APIs and reuse them.

To make it easier to use or a better tool, the architecture and internal design of the folders for organizing the structure of the different data could be more structured. Denodo does not have a guide for easily structuring the data.

In the last 12 months, I am not still working with Denodo. This year I have not, but last December, I was using it.

Denodo is stable and good. I do not have crashes normally.

My thoughts on scalability are that it is good because I can use different setups or configurations.

I do not use the technical support, so I cannot rate the customer service or technical support.

I have not evaluated other data integration tools before because Denodo was the tool in the project and I do not see other tools.

The initial setup of Denodo was medium complex.

I do not know the pricing for Denodo because I only design and develop.

The security was good for us.

I do not have anything else on my mind as far as valuable features.

I do not know if the dynamic query optimization helped us improve performance or analytical queries as I am not familiar with dynamic query optimization.

My overall rating for this solution is 9.

I used Denodo for a few months early this year, from January to March. My main use case for Denodo during that period was visualizing data from Snowflake and other databases across multiple databases and doing various types of analytics on the data inserted in those databases. We used Denodo for getting the data across multiple databases and matching the data in between these databases to have a single entry point for our queries.

For example, we had a bunch of files arriving on Azure Data Lake Storage, and we needed to match when these files arrived to what we needed to do with those files that were stored in a Snowflake instance. So, we used Denodo to cross this data and select queries, and then apply the Azure function that we needed to do to these files that were arriving on our Azure storage.

Denodo offers a great query language, the VQL, which was very easy to understand for someone that only knows SQL. It was easy to work with, and it was easy to integrate various types of databases in general. The usability of the platform is very great. The federated queries were also a feature that I used a lot. The easy usability of Denodo helped my work and made things easier for productivity. We were able to ship code faster than if we needed to use another type of data visualization tool or even an ETL.

When we have multiple databases, the standard or common approach is just to have an ETL that matches the data, and we need to write this whole ETL. With Denodo, we can simplify this whole work to a few queries or setups in the databases and just make a few queries. This was actually easier, and it was faster to deliver a solution for a client instead of building a whole ETL pipeline or simply backfilling one database in another. Denodo simplifies your work and helps you ship code or ship solutions in a faster way. I really like the UI that Denodo has on the web app version. It was very simple and actually very beautiful to see. For a complicated program, it was very easy to understand how it works, even for someone who is just approaching the data engineering career or side of the programming language.

The desktop version of the Denodo app has a difficult UX to understand, touch, and set up. But they have fixed that on the web app version, which is nice. Overall, the platform works great with the current setup that they have. I didn't really get to know the quirks and the bad side of the platform, as I've only used it for three months. In my opinion, it's a great software, and it's not buggy at all. The interface is simple and easy to understand.

I've been working in my current field for eight years right now.

Denodo is stable.

The customer support for Denodo is great. They are very easy to work with, and all my questions and concerns were answered in a very short amount of time.

I didn't previously use a different solution before Denodo. When I saw Denodo for the first time, I thought it was great, but it was not my choosing to use Denodo; it was the standard platform for the company I was working at.

We use Microsoft Azure as the cloud provider for Denodo's private cloud deployment.

I can't share any metrics with you because I was just a developer. But my general impression is that yes, Denodo saves a lot of employees and development time. Because 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 didn't really manage the setup cost, licensing, and pricing for Denodo. But in my opinion, it's very expensive. For small companies, it's not a solution that most small companies can afford. But for big companies, the pricing seems fair. It would be nice to have a cheaper version of Denodo for small businesses.

My main advice is to read the documentation for Denodo. It's a very powerful solution, but it has a lot of quirks and a lot of things that can be done and should be done the right way to have better performance when using Denodo. The documentation is very clear and will help you 99% of the times. The review rating is 9.

Denodo is designed for a distributed data fabric. Instead of a centralized data lake, we are in a distributed data mesh. Whenever a team wants to publish some data product, they are publishing data through Denodo, and their data store might be different; it might be in Azure SQL, in an Azure Databricks data table, or in ADLS.

Whenever a user is looking for data, instead of connecting to each individual data source, they can only pull data through Denodo views, which is a virtualization layer. Role-based access control can be implemented. Not everybody can pull data; whoever is looking for data and whatever data they are looking for, that approval we can give, and based on that, they can pull.

For real-time analytics, we have an Azure Kafka endpoint. There are some use cases with the Azure Kafka endpoint; we have transferred data through that, and through Databricks, we have transformed the data and stored it in some persistent layer such as a Databricks data table or any other data store.

One of the best features of Denodo is that it's one unified layer. All programmers or software need to manage different endpoints through one Denodo endpoint. We have custom-developed a tool called Marketplace where we can see whatever data products are available. It is centralized through one portal, a one-stop shop.

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. The distributed data mesh functionality allows teams to publish data products through Denodo, regardless of their data store type.

Role-based access control is another valuable feature, allowing selective data access based on approval. The Azure Kafka endpoint enables real-time analytics capabilities.

In terms of improvements for Denodo, regarding performance, in cases where there are multiple virtualizations—such as reading from one Denodo view that is virtualized, and from that view there's also virtualization, and another team is reading from that view—if multiple virtualizations happen with no caching in between, it becomes slow.

This occurs because it is cascading; whenever at the top level someone is reading data, that request is getting cascaded to the nth level, causing issues, especially in cases such as Power BI reports. We need to consider implementing some persistent layer in between.

The scaling process should improve because many things are getting automated. The scale-out part needs to be automated, though I am uncertain whether Denodo has already implemented that feature.

I have been working with Denodo since joining this organization, which is two and a half years. I believe the organization has been using Denodo for five years.

Denodo is scalable and is installed on a cluster, enabling scale-out capabilities. However, it cannot be scaled on the go. For huge data requests, it cannot scale automatically; admin action is required. The scaling process should improve as many things are becoming automated. The scale-out part needs to be automated, though I am uncertain whether Denodo has already implemented that feature.

I would rate Denodo's technical support an eight out of ten because whenever there is planned maintenance or if the Denodo server is down, we receive emails from the support team. If we raise a ticket, it can be resolved or addressed within a reasonable time frame, so support is good.

I am not certain what was used before Denodo as they had started using it before I joined. At that time, they might have had some legacy systems, and each team had their own ETL pipeline.

We are not using AWS, and a different team is responsible for onboarding or approving new software or licenses. Regarding advanced metadata management features, someone from the Denodo SME or Denodo admin team would be better positioned to comment.

We have used the caching mechanism. For huge data sizes, we are using Snowflake. Denodo has the feature to persist data in any data store. While Denodo doesn't typically persist data as it's a virtualization layer, we can persist data in Snowflake for large datasets.

Denodo can be deployed both on-premises and in the cloud. Initially, they started on-premises with the Denodo server, but have now migrated to the cloud. Denodo is the backbone for the BU I'm working for. If someone wants to share data or bring data, they need to go through Denodo, making it the backbone of this data mesh architecture.

I am not familiar with Denodo's pricing as that is handled by the budget or senior management team. We might be a partner of Denodo, but I'm not certain about this relationship. We are using Databricks Spark, not core Apache Spark.

I would rate the product overall an eight out of ten. I am satisfied, but there is always scope for improvement. If they fix scalability automation and virtualization problems, I would give them a higher rating.

I use Denodo to create a virtualized data environment rather than a physical one. This is helpful when enterprise data is spread across different data sources, such as cloud data warehouses and data lakes, and is used for developing AI and ML strategies. It creates a unified virtualized environment so the algorithms do not need to connect to multiple data sources individually.

Denodo creates a virtualized environment that provides one unified destination for all data sources so that AI and ML algorithms can connect to that virtualized destination without needing to establish connections with multiple data sources.

The most valuable feature in Denodo is data virtualization. This feature focuses on creating a virtualized data environment that allows for simpler connections between data sources and systems. It significantly eases the process of developing AI and ML strategies by centralizing access to various data sources and allowing algorithms to efficiently connect to and utilize data.

Denodo's complexity level is high since it connects multiple sources, sometimes creating glitches in the production environment. The stability of the features can be improved. The system has dependencies on other environments, like JVM, which can affect its performance.

I have been using Denodo for about two to three years.

Denodo shows stability concerns due to its dependency on external environments, such as JVM. If JVM does not function properly, it may cause Denodo to fail to connect to different sources.

I would rate Denodo's scalability as six to seven. Its complexity in configuring and the requirement to install different connectors for different sources affects its scalability.

Denodo's technical support is rated as seven. Although they are proactive in conducting webinars and connecting with customers, they are perceived as less proactive compared to some other vendors.

Configuring Denodo involves setting up a Java environment and installing different connectors for various data sources. This can take one or two weeks depending on the number of sources.

Denodo's pricing is not affordable for small companies and is more suitable for medium to large enterprises. On a scale of one to ten, where ten is most expensive, it is rated around three to four in terms of cost.

For new users evaluating Denodo, it is crucial to determine whether you truly need a virtualized environment. If you do not have numerous data sources, Denodo might not be necessary. However, if you do, Denodo makes sense.

I rate the overall solution as eight out of ten.

I am a consultant and a freelance data engineer with experience in Denodo. I work as a data warehouse and BI consultant, owner, and managing partner. I assist my clients, primarily in the public sector, including government organizations, with IT solutions using Denodo as part of their infrastructure.

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 acts like a database query engine without a specific database storage technology. It is a very intelligent query engine, providing an abstractness to information at a meta and meta two-level.

Additionally, Denodo's capabilities offer a strong and intuitive solution for data access.

Denodo is like a Swiss army knife, but it's challenging to deploy it as a clear solution because it presents itself as a tool that can do anything. Denodo needs better communication on how the product can be deployed for specific solutions, and there are price issues, as it's considered expensive. It requires improving the story of how it can solve specific problems.

I have two years of experience working with Denodo.

The stability of Denodo is high. I would rate it nine out of ten because it is very reliable, always performing as expected.

I have not worked with large deployments of Denodo, particularly in a clustered environment. While the solution scales well on a single machine, I have doubts about its scalability when deployed as part of a Java component cluster. Therefore, I rate its scalability at seven or eight.

Denodo's customer support team is very competent and responsive. They try to simulate problems and address issues promptly, although most problems relate to network or authorization protocols, not the product itself.

For me, the initial setup was not a problem due to my extensive experience with UNIX and databases. However, customers have found multilingual support somewhat lacking, and documentation has not always kept pace with rapid product changes.

Denodo is considered pricey, limiting its use to large enterprises or government organizations that can afford its comprehensive setup.

While I rate Denodo nine out of ten, I recommend considering the specific use case before adopting it. Denodo is a tool and should be part of a well-thought-out solution. Don't rely on marketing claims that say Denodo can handle everything. Solutions should be designed to solve specific business problems.

I work with business views for different areas.

Denodo is a good tool for data virtualization and cataloging. Its catalog is useful for data governance and helps document information for databases and fields.

Denodo is also effective for developing solutions quickly, facilitating user reports, and offering good data governance. It provides useful APIs and integrates with other systems.

The cache configuration is more complicated.

I have used the solution for two years.

Denodo has good support with the vendors and it is timely. They have a good methodology for learning how to use the tool, and the documentation is very thorough.

It is easy to set up.

Overall, I would rate Denodo a nine out of ten.

Denodo is used for data virtualization and not for data visualization. We are in the initial phase of using Denodo, performing a proof of concept (POC) to understand its features. It is used for connecting to various data sources and combining data without storing it.

Denodo provides flexibility in connecting to multiple data sources. It enhances data performance by using data caching. It allows for combining heterogeneous data formats and provides a mechanism for querying in natural language.

The data catalog feature is valuable as it helps define data structures without storing data. Denodo's ability to connect to multiple data sources and perform extract-transform-load (ETL) operations on the fly is noteworthy. It supports API usage to call and present data in JSON or Tableau format without visualization capabilities.

As we are still in the initial phase, it's difficult to identify weaknesses. However, ensuring data caching is up to date is critical. While AI and ML integration is present, understanding its implementation better could be beneficial.

I have started using Denodo since last month.

Denodo provides high reliability, with features like vertical scaling, horizontal scaling, and failover nodes.

Denodo provides high availability and flexibility in infrastructure and architecture management.

I have not yet needed to raise a ticket with Denodo's technical support as most questions have been answered through the community resources. I am in touch with some Denodo technical team members who explain the product features.

I used Qlik Sense for over six to seven years. 95% of my queries were answered through Qlik's community, and their support was prompt. We were Qlik customers, not partners.

The installation process is simple, whether on Windows or Linux servers. The setup is easy and provides flexibility from an infrastructure and architecture point of view.

End users of Denodo will mostly be non-technical, as they will query the system in plain English for data insights.

I'd rate the solution nine out of ten.