

Databricks and KNIME Business Hub compete in the data analytics and machine learning platform category. Databricks appears to have an edge in handling large-scale data processing and advanced machine learning capabilities, while KNIME shines in providing user-friendly, no-code solutions ideal for those lacking coding expertise.
Features: Databricks offers powerful analytics capabilities, robust machine learning support, and features such as Delta Lake and SQL integration. It provides a collaborative environment with notebooks suitable for both Python and R. In comparison, KNIME Business Hub excels in its no-code interface, extensive library of nodes, and excellent workflow tools, making it suitable for users without a coding background.
Room for Improvement: Databricks users seek improvements in pricing, visualization capabilities, and advanced integrations with tools like Power BI. Enhancements in simplifying management and expanding machine learning features are also desired. KNIME needs better performance in handling large datasets, improved data visualization abilities, and enhanced documentation to assist new users. There's also a need for stronger integration capabilities with various platforms.
Ease of Deployment and Customer Service: Databricks provides flexible deployment options via major cloud providers like Azure and AWS, offering a versatile environment but with varied experiences in technical support. KNIME Business Hub typically deploys on-premises or private cloud, backed by comprehensive documentation reducing frequent needs for support, although when engaged, users report satisfactory service.
Pricing and ROI: Databricks is often considered expensive with usage-centered pricing models that depend on cloud choice but is viewed as cost-effective for large-scale processing. KNIME offers cost savings with its free desktop version and competitively priced server option for enterprise use, providing significant value through its open-source strategy. Users note Databricks delivers ROI in cost savings, while KNIME’s open-source nature inherently reduces costs.
This reduction in both time and money resulted in real-time impact and significant cost savings.
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.
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.
While they cannot always provide immediate answers, they are generally efficient and simplify tasks, especially in the initial phase of learning KNIME.
The sky's the limit with Databricks.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
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.
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.
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.
I believe that in terms of credits for Databricks, we're spending between £15,000 and £20,000 a month.
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.
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.
| Product | Market Share (%) |
|---|---|
| Databricks | 9.3% |
| KNIME Business Hub | 7.5% |
| Other | 83.2% |

| Company Size | Count |
|---|---|
| Small Business | 26 |
| 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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