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 the solution in my company as a virtualization tool. The tool helps users to connect with many databases and resources in the data lake. With the tool, I can create views for the front end of Tableau and other BI tools. I combine data from different or various resources and combine them to create views, which is also useful for analysis purposes. On top of the views I create with the tool, I also make dashboards.

The most valuable feature of the solution for data virtualization in our company stems from the fact that we have various data sources, and we combine those data sources. My company also uses the solution to connect to box files and for ingestion purposes.

Sometimes, Windows-related functions do not work properly in Denodo. The analytic functions in SQL do not work properly. The aforementioned area in the product needs to be taken care of for improvements.

I have been using Denodo for two and a half years.

It is a very stable solution.

It is a scalable solution. I can add as many resources as I want to in Denodo.

There are around 5,000 to 10,000 people in the company that use the product.

I am unsure if the solution is deployed on an on-premises model, but I am sure that it is not deployed on the cloud.

The product's deployment phase was done with an in-house IT infra team.

Denodo has streamlined data integration from multiple sources, such as various databases offered by Salesforce or PeopleSoft.

The tool helps with big data analytics or cloud integration processes since I use it to create views from various sources, and on top of it, I also make dashboards.

I work as a third-party or outsourced consultant in a company in India, and my team works in the USA, where Denodo is available in the service. In my company, there are around 70 people working with the product.

The performance and scalability of the product are nice. I only face issues with the analytical functions since they don't work properly for SQL.

I rate the product's UI an eight out of ten.

If a company has to deal with multiple data sources, then Denodo can be used for virtualization.

I rate the overall tool a ten out of ten.

We use the solution for data virtualization to publish the data as a product. We have different teams who have their own database. If a team needs any data, they get it from Denodo.

It is easy to virtualize data using the solution. If required, we can also use the solution for caching.

The solution is slow when there are many virtualization layers. The solution is also slow when we connect the on-premise solution with the one on the cloud through a network gateway.

I have been using the solution for two and a half years.

The product is stable.

More than 100 people in the organization are using the solution.

As a developer, I can easily set up the solution on my local Kubernetes. Local configuration is pretty easy.

A lot of virtualization leads to data latency. We can do caching on the intermediate layer. We can also leverage Databricks if we have any complex transformation.

I have a local Docker container. I can easily integrate my local container through Git repo and clone it in our depth server. Denodo is the only tool I know for data virtualization. I will recommend the solution to others. Overall, I rate the product a ten out of ten.