

Find out in this report how the two Data Management Platforms (DMP) solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
For a lot of different tasks, including machine learning, it is a nice solution.
When it comes to big data processing, I prefer Databricks over other solutions.
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.
Whenever we reach out, they respond promptly.
As of now, we are raising issues and they are providing solutions without any problems.
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
They have a good methodology for learning how to use the tool.
Denodo's customer support team is very competent and responsive.
If we raise a ticket, it can be resolved or addressed within a reasonable time frame, so support is good.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
I would rate the scalability of this solution as very high, about nine out of ten.
For huge data requests, it cannot scale automatically; admin action is required.
While the solution scales well on a single machine, I have doubts about its scalability when deployed as part of a Java component cluster.
Its complexity in configuring and the requirement to install different connectors for different sources affects its scalability.
They release patches that sometimes break our code.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
Databricks is definitely a very stable product and reliable.
I would rate it nine out of ten because it is very reliable, always performing as expected.
If JVM does not function properly, it may cause Denodo to fail to connect to different sources.
Denodo is stable and good.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
Ensuring data caching is up to date is critical.
Denodo needs better communication on how the product can be deployed for specific solutions.
The system has dependencies on other environments, like JVM, which can affect its performance.
It is not a cheap solution.
For small companies, it's not a solution that most small companies can afford.
Denodo is considered pricey, limiting its use to large enterprises or government organizations that can afford its comprehensive setup.
Denodo's pricing is not affordable for small companies and is more suitable for medium to large enterprises.
Databricks' capability to process data in parallel enhances data processing speed.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
Denodo's ability to connect to multiple data sources and perform extract-transform-load (ETL) operations on the fly is noteworthy.
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 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.
| Product | Market Share (%) |
|---|---|
| Denodo | 5.2% |
| Databricks | 2.3% |
| Other | 92.5% |


| Company Size | Count |
|---|---|
| Small Business | 25 |
| Midsize Enterprise | 12 |
| Large Enterprise | 56 |
| Company Size | Count |
|---|---|
| Small Business | 15 |
| Midsize Enterprise | 6 |
| Large Enterprise | 19 |
Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.
Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.
What features make Databricks unique?
What benefits can users expect from Databricks?
In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.
Denodo is a leading data integration, management, and delivery platform that uses a logical approach to enable data science, hybrid and multi-cloud data integration, self-service BI, and enterprise data services. Organizations of different sizes across various industries utilize the product to get above the data silos. The solution offers organizations the freedom to migrate data to the cloud, or logically unify data warehouses and data lakes, without affecting business. This can ultimately result in the evolution of data strategies.
The platform accelerates data provisioning through reduced data replication, provides business users the freedom to select their preferred applications, and enables consistent security and governance across multiple systems. The solution offers one of the leading logical data fabric solutions by initiating data virtualization and eliminating the complexity and exposing the data in business-friendly formats.
Denodo also offers modern data integration and management for hybrid and multi-cloud environments for Denodo Platform for Cloud. This service can be purchased through the bring-your-own-license (BYOL) option. Users seeking faster deployment can license the product to popular cloud providers, including Amazon AWS, Google Cloud Platform, and Microsoft Azure. The solution integrates, manages, and delivers data in complex environments with high performance, governance, and security. It also offers additional solutions, such as the Denodo Platform for Cloud Modernization, the Denodo Platform for Cloud Data Integration, and the Denodo Platform for Cloud Analytics, which overcome common cloud data challenges.
Denodo Features
At the beginning of 2022, version 8.0 of Denodo introduced several new key features of the platform. These include:
Denodo Benefits
Denodo offers various benefits for its users through its services. Some of the greatest advantages of using this platform include:
Reviews from Real Users
Naresh M., a senior application developer at a financial services firm, appreciates Denodo because it offers quick and simple web services creation.
Alisson M., a senior BigData DevOps engineer at Schaeffler, says that Denodo is great for queries and scouting data.
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