

In the data management and analytics category, KNIME Business Hub and Dremio present strong competition, with KNIME having an edge in accessibility and cost-effectiveness due to its visual workflow capabilities and open-source perks.
Features: KNIME Business Hub offers extensive integration capabilities with R and Python, robust machine learning tools, and strong community support. Dremio provides excellent data management features, unified access for data across storage layers, and effective data lineage management.
Room for Improvement: KNIME users suggest enhancing data visualization, documentation, and performance with large datasets. Dremio could improve query performance, especially for nested queries, and enhance integrations with diverse data sources and connectors.
Ease of Deployment and Customer Service: KNIME supports hybrid and on-premises deployments, with strong community support for user issues. Dremio excels in cloud integration with data lakes and hybrid environments, though it could benefit from faster response times and more comprehensive technical support documentation.
Pricing and ROI: KNIME offers a free desktop version and competitive pricing for its server edition, offering significant ROI due to its open-source nature. Dremio, while less expensive than some competitors, incurs higher costs for larger-scale deployments, making KNIME a more cost-effective choice for extensive data science capabilities without high licensing fees.
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.
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.
Starburst comes with around 50 connectors now.
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.
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.
For graphics, the interface is a little confusing.
The machine learning and profileration aspects are fascinating and align with my academic background in statistics.
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.
Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.
The first feature that stands out for me in Dremio is the federated type of query, which allows the possibility to use multiple endpoints without worrying about writing custom SQL that runs only for SQL Server or for Postgres and Redshift.
KNIME is more intuitive and easier to use, which is the principal advantage.
It is more elastic and modern compared to SAP Data Services, allowing node creation and regrouping components or steps for reuse in different projects.
| Product | Market Share (%) |
|---|---|
| KNIME Business Hub | 8.7% |
| Dremio | 2.3% |
| Other | 89.0% |

| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 20 |
| Midsize Enterprise | 16 |
| Large Enterprise | 29 |
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.
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