

Databricks and Darwin compete in the data analytics and machine learning platform category. Databricks has the upper hand with its versatility and scalability, while Darwin excels in automated model generation.
Features: Databricks offers robust capabilities such as Delta data format optimization, collaborative notebooks, and efficient machine learning libraries. It supports multiple programming languages and integrates with Azure Machine Learning. Darwin provides automated model generation, streamlining the process for non-data scientists to build and iterate models efficiently.
Room for Improvement: Databricks could enhance its visualization capabilities and deeper integration with Power BI and Tableau. Its pricing is high, and integration with data sources could be improved. Darwin's dashboards need to be more user-friendly for broader use, and its ability to handle unsupervised models requires enhancement.
Ease of Deployment and Customer Service: Databricks supports various deployment options, including public, private, and hybrid clouds but faces scalability challenges. Its customer service has mixed reviews. Darwin's documentation reduces the need for technical support but has limited deployment flexibility, primarily operating on public cloud environments.
Pricing and ROI: Databricks' pay-per-use model can be expensive but offers ease of use and scalability, positively impacting ROI. Darwin is considered cost-effective, especially compared to hiring data scientists, providing value through streamlined model development and deployment while typically only incurring licensing costs.
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.
Previously, when using Snowflake, we had customer reps who were really knowledgeable and helped us to avoid beginner mistakes.
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.
We've suffered from the lack of professionals with previous experience, which makes it difficult to dig ourselves out of the situation we've found ourselves in.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
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.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
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.
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.
Databricks' capability to process data in parallel enhances data processing speed.
| Product | Market Share (%) |
|---|---|
| Databricks | 9.6% |
| Darwin | 1.0% |
| Other | 89.4% |

| Company Size | Count |
|---|---|
| Small Business | 6 |
| Large Enterprise | 2 |
| Company Size | Count |
|---|---|
| Small Business | 25 |
| Midsize Enterprise | 12 |
| Large Enterprise | 56 |
SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.
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.
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