

KNIME Business Hub and Dremio compete in the data analytics and management software category. KNIME appears to have the upper hand due to its flexibility and beginner-friendly no-code interface, making it suitable for a broader range of users.
Features: KNIME Business Hub offers tools for ETL processes, integration with multiple languages, and a comprehensive algorithm library, catering to both beginners and advanced users. Its no-code interface is a significant advantage. Dremio provides a unified platform for data lakes, excellent data lineage, and reflection features, enabling seamless connections with diverse data sources. Its strength lies in integrated data management capabilities.
Room for Improvement: KNIME could enhance data visualization, support for large datasets, and documentation. Users report performance issues with large data volumes and a steep learning curve. Dremio faces challenges with execution speed on large queries, limits in connectors, and complex integration with platforms like Databricks. Improvement in memory management and documentation is needed.
Ease of Deployment and Customer Service: KNIME Business Hub offers flexibility in deployment options, including on-premises and cloud scenarios. Dremio excels in deploying across public and hybrid clouds, favorable for modern data environments. Both have strong community support, but Dremio has better professional support and documentation. KNIME, being open-source, has less formal technical support but benefits from an active community.
Pricing and ROI: KNIME is cost-effective with a free open-source desktop version, appealing to small teams and academic users. The enterprise version offers scalability at a higher price. Dremio, although pricier, provides significant ROI through robust data management and processing capabilities, making it suitable for larger enterprises. Both offer substantial ROI, though KNIME's entry cost is lower, appealing to budget-conscious users.
Dremio surely saves time, reduces costs, and all those things because we don't have to worry so much about the infrastructure to make the different tools communicate.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
While they cannot always provide immediate answers, they are generally efficient and simplify tasks, especially in the initial phase of learning KNIME.
My mark for technical support for KNIME Business Hub is about a 7, as most of the support is in the community, and it is quite good because it is open source.
Dremio's scalability can handle growing data and user demands easily.
Internally, if it's on Docker or Kubernetes, scalability will be built into the system.
I rate Dremio a nine in terms of stability.
For now, KNIME Business Hub is excellent for me and for our team.
From 1 to 10, I would rate the stability of KNIME Business Hub quite good, around an 8 or 9.
Starburst comes with around 50 connectors now.
It should be easier to get Arctic or an open-source version of Arctic onto the software version so that development teams can experiment with it.
I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement.
I would like to see additional functions in KNIME Business Hub that can connect to generative AI, allowing users to describe the workflow for easier workflow generation and creation.
When I import this data set in the File Reader node, I have problems with this field because it is a date, but the problem is that it imports it as text.
Computer vision is the most important because now there is a new age of large language models and visual language models.
Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.
Dremio has positively impacted my organization as nowadays we are connected to multiple databases from multiple environments, multiple APIs, and applications, and Dremio organizes everything in an amazing way for me.
You just get the source, connect the data, get visualization, get connected, and do whatever you want.
KNIME is more intuitive and easier to use, which is the principal advantage.
KNIME is simple and allows for fast project development due to its reusability.
It is very important that I have the workflow automation integrated with Python nodes.
| Product | Mindshare (%) |
|---|---|
| KNIME Business Hub | 5.6% |
| Dremio | 2.2% |
| Other | 92.2% |


| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 21 |
| Midsize Enterprise | 16 |
| Large Enterprise | 32 |
Dremio offers a comprehensive platform for data warehousing and data engineering, integrating seamlessly with data storage systems like Amazon S3 and Azure. Its main features include scalability, query federation, and data reflection.
Dremio's core strength lies in its ability to function as a robust data lake query engine and data warehousing solution. It facilitates the creation of complex queries with ease, thanks to its support for Apache Airflow and query federation across endpoints. Despite challenges with Delta connector support, complex query execution, and expensive licensing, users find it valuable for managing ad-hoc queries and financial data analytics. The platform aids in SQL table management and BI traffic visualization while reducing storage costs and resolving storage conflicts typical in traditional data warehouses.
What are Dremio's most valuable features?Dremio is primarily implemented in industries requiring extensive data engineering and analytics, including finance and technology. Companies use it for constructing data frameworks, efficiently processing financial analytics, and visualizing BI traffic. It acts as a viable alternative to AWS Glue and Apache Hive, integrating seamlessly with multiple databases, including Oracle and MySQL, offering robust solutions for data-driven strategies. Despite some challenges, its ability to reduce data storage costs and manage complex queries makes it a favorable choice among enterprise users.
KNIME Business Hub offers a no-code interface for data preparation and integration, making analytics and machine learning accessible. Its extensive node library allows seamless workflow execution across various data tasks.
KNIME Business Hub stands out for its user-friendly, no-code platform, promoting efficient data preparation and integration, even with Python and R. Its node library covers extensive data processes from ETL to machine learning. Community support aids users, enhancing productivity with minimal coding. However, its visualization, documentation, and interface require refinement. Larger data tasks face performance hurdles, demanding enhanced cloud connectivity and library expansions for deep learning efficiencies.
What are the most important features of KNIME Business Hub?KNIME Business Hub finds application in data transformation, cleansing, and multi-source integration for analytics and reporting. Companies utilize it for predictive modeling, clustering, classification, machine learning, and automating workflows. Its coding-free approach suits educational and professional settings, assisting industries in data wrangling, ETLs, and prototyping decision models.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.