Denodo Reviews

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.

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.

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.

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.

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.

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.

We are currently using it in our company, and it's our data virtualization tool. We fall under the federated analytical model. Basically, we have data that moves into our warehouse, and from the warehouse, when we want to build our reports, we use it. So, we are mainly using it for reporting purposes at this point. Instead of building aggregated views in our warehouse, we put that on the Denodo layer.

We aggregate the data in our Denodo layer. Because Denodo is a virtualization tool, we try not to cache the data, but we've cached it whenever needed. Our dashboards usually run once a day or twice a day. So, we refresh the data once or twice a day. Sometimes, we also refresh every hour, but mostly, we refresh once or twice a day. For that, we don't need the data materialized in a view. We don't need the data to be there. We just create these views on Denodo. We have Power BI and Qlik Sense, and we just use those tools to pull the data from Denodo.

There are other uses as well that we are thinking of as a company. We have business users who do want to look at the backend of what's going on. They may just want to run something like a quick analysis. For example, for a question, they just want to go take a look at the actual data. For that purpose, we can give these analysts access, and they can go in and take a look at that. Eventually, we want to go to a place where we want to enable self-service analytics for our business users. We are thinking that Denodo could help us in that direction. I know different companies use Denodo for different purposes, but we are using it on the IT side. Currently, we are using it mainly for reporting purposes, but we could also move towards self-service and make it available to our business users as well. That's for the future. We haven't done that yet. For now, we are only doing it at the BI layer.

One thing that we have noticed is that when you have a BI tool, you end up building a lot of the logic in the BI tool, but as a company, every company wants to be tool agnostic because today, you could be in the Qlik Sense, and tomorrow, you may decide to go with Tableau or something else that is there. If you have put a lot of logic within the tool, transitioning or moving away from one BI tool to another tool becomes a very intensive process. By keeping the logic in Denodo, you can move to any tool. That's what we have done at this point because we used to have Cognos in my company before. When we did the migration from Cognos to Power BI, it was quite intensive for us because a lot of the logic was sitting in SQL queries within Cognos. To move to Power BI, we had to do a lot of backward thinking. These reports were built more than 5 years ago, or even 10 years ago, and the people who built them were no longer with the company. The logic was pretty much gone. We didn't have good documentation, but now, after we have put the code in Denodo, we have visibility. Any developer, not a business analyst, can come and take a look at that code and discern how the data is moving from the warehouse to Denodo. All that Power BI or Qlik Sense is doing is just pulling the data from that view. We can do averages and other calculations in the tool, but at least our aggregation and the logic are in the Denodo layer, and then going forward, we can take it to any tool.

Last year, they moved from version 7.0 to 8.0, and version 8.0 gives a lot of flexibility.

I work with an insurance company and our main reason for using Denodo is to bring together all our data into one platform in the cloud. The company has very diverse data sources including data stored in the cloud, XML files, Db2 and SQL databases, and SAM / VSAM files on legacy mainframe platforms. Thus, management decided that they wanted all the data in one place by connecting these different data sources for better visualization and reporting.

It's really working well for us and we are using it for both of our claims centers with our claims management solution as well as our premium management solution. One instance of Denodo is for the underwriting team and the other is for the actuary team. In total, we have around 45 people using it.

We were originally using the solution on-premises but we are now using the cloud version deployed on Azure.

There are a lot of benefits of using Denodo in our company. Suppose, as an underwriter, you are approving a policy for a customer; typically, you will have to do a number of SQL queries or perform macros on Excel to find out information such as, "How many years has this customer been with the company and how is their claim history?" or "How is this customer's business actually doing?" There are many things that underwriters want to check before they approve any underwriting amount. And this means, for any approval, they're taking up to three or four hours, especially in a commercial insurance setting with all the changes that have taken place during Covid.

Lots of companies have filed for bankruptcy or are not doing as well as they should be, so there are many changes that the underwriting teams have to take into account. These teams could be doing great business by writing the right amount for premiums for the company or for the client, and they have been having a lot of difficulties reaching certain data points in order to give their final verdict.

However, now that everything is in front of them in one virtualized screen, they can quickly see all the necessary data such as claims files, history files with previous premium history, how much lifetime value the client has for the company, etc. They can obtain all these pieces of information from a single search and then make an appropriate decision to approve or deny, all within 15 to 20 minutes. Considering that the time to do such things has come down to 20 minutes from three hours, you can tell how much time has been saved by using Denodo.

Also, some of the underwriters are over 60 years old, having been in the company for 35 years or more, and they don't love technology that much. Yet, with the training that we've given them, they're slowly starting to enjoy it and are now able to appreciate how much time it has saved them. They are very aware that Denodo is helping them, even though they would have been happy to stick with Excel.