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.

My customers use Denodo for data integration. Before using Denodo, they did not have a platform to control all data sources with one platform. Denodo allows connecting different data sources for analysis and reporting.

The most valuable feature of Denodo is security, especially the privilege control. Denodo can connect over 150 data sources, making data integration effective.

The GUI interface allows easy connection of all data sources like API, JSON, or any JDBC source.

Additionally, the enterprise version has a global security management feature to control privileges by role.

I am still waiting for Denodo to support vector databases. Currently, Denodo does not provide a vector database, which my customers build on their own to use AI functionalities.

I have been working with Denodo for one and a half years.

Denodo is stable. It can handle API stress tests effectively.

Denodo is scalable.

As a presales engineer, I do not have direct experience with the initial setup since my role is focused on selling the product.

Pricing is case by case, so I cannot provide specific details.

I only work for Denodo. I am not familiar with other solutions.

If customers want to make their data valuable, they must know which data they are using. By using Denodo's data category feature, they can identify and manage valuable data effectively.

I'd rate the solution eight out of ten.

The idea behind data virtualization is to create an abstract layer for easy consumption by BI tools such as MicroStrategy, Tableau, Power BI, and others. This abstraction layer aims to avoid the complexity of directly accessing data sources, thus simplifying data integration. Denodo facilitates this by mediating between different data platforms like Teradata, cloud data warehouses, Oracle databases, and others, unifying them under a common virtual layer. This enables seamless access to unified data across diverse platforms and technologies.

Furthermore, Denodo includes an optimization engine that enhances performance through techniques such as static and dynamic optimization, cost-based optimization, and caching mechanisms. These optimizations help improve query performance, reduce memory usage, and enhance overall system efficiency. This optimization capability is a key feature of Denodo and other data virtualization tools, enabling efficient data access and integration across heterogeneous environments.

Denodo provides several server connectivity options with other tools such as ODPC and UDPC. It supports API integrations, allowing integration with a wide range of databases using different technologies, including NoSQL and relational databases. For databases that do not have a universal connector, Denodo allows users to create custom JDBC connectors. Additionally, it supports integration with websites using KPI connectors on-premises, offering a variety of connector options.

They need to invest more in the optimization engine. It will be a fantastic tool for them to enhance automation and improve the GUI interface, especially for Integration, as some tools are still not fully integrated, such as Iceberg. Batch processing is very good but not yet fully integrated. Therefore, they should focus on improving the GUI interface, enhancing cloud capabilities, and integrating modern technologies like Iceberg. Additionally, they should continue refining the optimization engine itself.

I have been using Denodo for three years.

It's a very stable tool. We faced problems related to vulnerability checks from the operating system itself, which were quickly resolved. It's robust and stable; we haven't encountered any major issues with it. We did need to allocate additional memory to handle our data volume.

I rate the solution’s stability a nine out of ten.

Denodo is scalable and supports load balancers for achieving scalability through multiple load balancing techniques. In our production environment, we have thirty-two cores distributed across four machines, each with eight cores. These machines are managed using a load balancer tool to ensure scalability and efficient resource allocation.

I rate the solution’s scalability an eight out of ten.

They provide extensive documentation which covers administration, development, and support comprehensively, providing detailed explanations for every step. In contrast, other data virtualization solutions like Data Virtualization and IBM Data Virtualization often lack such detailed documentation, which is crucial for implementing solutions to various problems. Additionally, Denodo provides excellent support. They have a dedicated and knowledgeable staff who are readily available when needed.

I have experience working with telco data visualization and analytics. I've utilized Denodo for data integration and analytics in telecommunications. Additionally, I've worked with mobility and IoT technologies for revenue assurance and management. I have proficiency in the Microsoft Stack, including SSIS, as well as experience with AWS Cloud services.

The installation is not extensive. It takes about one week. The entire project deployment, including development, usually spans around six months. For instance, development itself might take approximately three to four months, with the remaining time allocated to post-production tests, use case implementations, and other activities.

They have achieved significant improvements in KPIs, such as customer churn, reducing it by approximately eight to twelve percent. This is an actual figure. They have also increased customer loyalty by the same margin.

Denodo is expensive.

I rate the product’s pricing a six out of ten, where one is cheap and ten is expensive.

The main motivation was their use of multiple technologies such as SQL servers and Oracle, spanning nearly twenty-five different databases. This multitude of data sources and information spread across various databases necessitated robust data integration and visualization capabilities. Denodo played a crucial role in enabling the bank to unify and visualize information from these disparate schemas and technologies. This achievement significantly enhanced their ability to derive comprehensive insights from the diverse datasets. Numerous insights and use cases have already been successfully implemented across various departments within the bank. These initiatives have encompassed areas like predictive analytics, customer behavior analysis, and the development of machine learning models for HR and revenue forecasting.

Denodo's products help reduce time to market for new products and promotions, ensuring they reach customers promptly. This prevents revenue loss, especially at the customer and complaint levels, among other benefits.

Denodo has three layers of security. It is integrated with Active Directory for authentication. It supports single sign-on within the organization, particularly in the banking sector, where it is a leader. This integration allows for auditing and monitoring of all staff members. This forms the first layer of security. The second layer involves fine-grained access control over specific tables, views, and even columns. Denodo provides specific controls to determine who can access which tables and columns at a granular level. The third layer consists of generic governance rules governing user groups such as development, operations, and administration teams. For instance, developers may have read-write access, administrators have full administrative privileges, and operations staff have predefined access rights. These rules govern the authorization and authentication processes within Denodo.

Denodo is not an expert in AI nor does it propose AI-specific use cases. However, its abstraction layer can inspire ideas related to AI, such as predictive analytics for customer behavior, complaints analysis, churn prediction, forecasting future revenue trends, and identifying new customer benefits for a bank's portfolio, for example.

It provide a six-month warranty post-production, followed by five years of product support. This includes updates, new batches, upgrades, and more. One admin and two development team are required for maintainance.

Overall, I rate the solution a nine out of ten.

I use the tool to integrate and standardize data. It converts all kinds of data. It can convert non-relational data to relational data. It can create a standardized way to consume data.

The API generator helps generate APIs. The integration with Presto and other MPPs is valuable. It also provides integrations with Lucene and Elasticsearch. The tool has zero data replication. We can consume data fast. The product democratizes data. People talk about data democratization. Providing access to data is one thing, but the solution gives data in a format that people can consume.

Data transformation must be improved. Denodo is not the best tool for data transformation.

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

The product is pretty stable. I rate the stability a nine out of ten.

The product is highly scalable. I rate the scalability a ten out of ten.

The technical support is very good. I rate the support an eight and a half out of ten.

The setup is simple and straightforward. The deployment takes about two months. We required two data architects to deploy the product. The vendor maintains the solution. The tool can be deployed on-premise, in the cloud, or in multi-cloud environments.

By using more data in decision-making, our strategy became more accurate. Eventually, we can replace a lot of processes and a lot of systems that we have.

Starburst and Tray.io are too technical. Denodo is very user-friendly. Denodo is a technological tool for non-technical people. It is very easy to use. Denodo is way more mature than Starburst and Tray.io.

We connect all the data sources with Denodo, and Denodo connects with the metadata. We do not move the data. We embrace the fact that data now resides in distributed data sources. Through the metadata, we create a catalog. The catalog pretty much centralizes all our data management. Through the catalog, all the information is standardized.

We can visualize the information, manipulate the data, and integrate the catalog with data-consuming systems like Power BI, Tableau, and Apache. We do not centralize data. People think we need to get all the data outside the sources and put it in a repository to manage data properly. It is not necessary. We can integrate and create the catalog without replicating or moving any data. We can integrate with all the data-consuming systems we have and create a logical data layer.

Overall, I rate the solution a nine out of ten.

In the past, transforming data from databases to other tools was a tedious and time-consuming process. Denodo eliminates that hassle, saving me time and money.

So, Denodo improves data integration processes. It reduced the time to go live and required fewer human resources to make data accessible across the company.

Moreover, it supports our reporting needs. I use Bar BI for reporting, and Denodo's ability to connect directly to native data sources without complex configurations is a great feature.

I like the integrated views. They're incredibly useful, along with the overall integration capabilities.

The logical data warehouse functionality is fantastic. It truly stands out.

The ClearOptimizer and Virtual Cache are great features. They work together seamlessly to optimize performance.

I'm waiting for them to introduce the generative AI capabilities they mentioned would be available by the end of the year. That would be a game-changer.

Moreover, I would like to see the ability to utilize vector databases.

For some internal strategies, I need to publish data into vector databases. Denodo currently integrates with ChatGPT, but the ability to manage and utilize them directly within Denodo would be a significant improvement.

I have been working with it for a year now.

We never had performance or scalability challenges with Denodo.

The customer service and support are fantastic.

I used SSIS as an ETL tool and some IBM staging tools, but not extensively.

I used SSIS at my previous company and then switched to Denodo here.

The time to market is significantly faster with Denodo, both for deployment and publishing updates. Additionally, the web service capabilities are fantastic. We can deploy any web service in just minutes without complex development or security concerns.

If you need immediate access to data (within 3-5 days), Denodo is a great solution. However, for complex data silos, I recommend trying a demo for a week or two before making a decision.

Overall, I would rate the solution an eight out of ten.