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.

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.

Positive

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.

Positive

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.

Positive

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.

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.