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.

Positive

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.

Neutral

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.

Positive

It is easy to set up.

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