

Databricks and KNIME Business Hub both operate in the data analytics and processing category. KNIME seems to have the upper hand in cost-effectiveness, making it accessible for smaller teams while still offering robust features.
Features: Databricks offers interactive clusters, integration with multiple programming languages, and advanced machine learning libraries. It is particularly efficient in managing and processing large-scale data loads. KNIME Business Hub provides a no-code visual interface for data preparation and ETL processes, supports a wide range of algorithms, and offers easy integration with other languages like Python and Java, making data processing and model building simpler.
Room for Improvement: Databricks could enhance its visualization tools, improve the transparency of its pricing structure, and expand integration features. KNIME Business Hub could benefit from improving its ability to handle very large datasets, enhancing its visualization tools, and organizing documentation for better user support.
Ease of Deployment and Customer Service: Databricks is primarily cloud-based, simplifying deployment but with limited on-premises capabilities. Its customer service is generally responsive, albeit with some reported delays. KNIME Business Hub provides flexible deployment options, catering to both cloud and on-premises needs. Its documentation is comprehensive, although user reviews on technical support are mixed, with more emphasis on self-service solutions.
Pricing and ROI: Databricks is known for its premium pricing model which is usage-based. Its ROI is considered positive for large-scale workloads due to its efficiency and capabilities. KNIME Business Hub is praised for its cost-effectiveness, offering a free version for smaller teams and priced server options, resulting in a positive ROI owing to its affordability and feature richness.
When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
As of now, we are raising issues and they are providing solutions without any problems.
Whenever we reach out, they respond promptly.
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.
While they cannot always provide immediate answers, they are generally efficient and simplify tasks, especially in the initial phase of learning KNIME.
Databricks is an easily scalable platform.
I would rate the scalability of this solution as very high, about nine out of ten.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
They release patches that sometimes break our code.
Databricks is definitely a very stable product and reliable.
We could use their job clusters, however, that increases costs, which is challenging for us as a startup.
This feature, if made publicly available, may act as a game-changer, considering many global organizations use SAP data for their ERP requirements.
If I could right-click to copy absolute paths or to read files directly into a data frame, it would standardize and simplify the process.
For graphics, the interface is a little confusing.
The machine learning and profileration aspects are fascinating and align with my academic background in statistics.
It is not a cheap solution.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
Databricks' capability to process data in parallel enhances data processing speed.
It is more elastic and modern compared to SAP Data Services, allowing node creation and regrouping components or steps for reuse in different projects.
KNIME is more intuitive and easier to use, which is the principal advantage.
| Product | Market Share (%) |
|---|---|
| Databricks | 10.7% |
| KNIME Business Hub | 10.1% |
| Other | 79.2% |

| Company Size | Count |
|---|---|
| Small Business | 25 |
| Midsize Enterprise | 12 |
| Large Enterprise | 56 |
| Company Size | Count |
|---|---|
| Small Business | 20 |
| Midsize Enterprise | 16 |
| Large Enterprise | 29 |
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.
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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