

Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
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.
If you're actually using Domo at a very limited case and you're being charged $20,000, we've seen ROI there, but once it goes really high, you really need to check your metrics and check your profit.
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.
No matter how complex the problem is, it can be taken care of by the support team.
They were quite professional and in around three to five working days, they had identified where they suspected there was an issue and I was able to fix it.
It's very easy to get technical support from Domo.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
I would rate the scalability of this solution as very high, about nine out of ten.
The fact that you're able to easily identify the pipelines or flows that have errors, and it notifies you when you're building a pipeline where you can run previews and tell where to fix issues, is helpful.
When fetching files larger than 100 MB from SFTP or any other portal, Domo becomes slow due to the heavy file size.
Everything comes under the same umbrella and it's pretty user-friendly.
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.
In recent years, I haven't had such cases. It's quite stable and I don't have any reservations on its stability.
In terms of overall stability of the platform, it's very stable.
During that time, we faced issues from the project side as Domo was not visible in our portal.
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.
End users require a license to run their own reports and dashboards, which are fairly expensive.
Some technical aspects such as Beast Mode calculation could be improved in Domo, as it would provide more clarity and help in giving insights to clients or customer business team requirements.
One of the areas where we've had frustrations with Domo is the aesthetics. The aesthetics are quite limited compared to other BI tools such as Tableau and Power BI.
It is not a cheap solution.
Domo's pricing is high compared to other BI tools, and it is costly.
For long-time users, it can become expensive, but the trade-off is access to the entire platform instead of licensing different components separately.
They quoted approximately one dollar per KB.
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.
App Studio is valuable because it allows all the customization we needed; we can decode it, with the view and grid which are all I need, drill-downs, and everything can be done the way I need it.
None of the other tools provide the kind of support that enables chatting and working on the same item simultaneously.
I have been using it for four years and have been able to extract the information I need from it.
| Product | Market Share (%) |
|---|---|
| Databricks | 8.3% |
| Snowflake | 17.7% |
| Dremio | 8.9% |
| Other | 65.1% |
| Product | Market Share (%) |
|---|---|
| Domo | 3.9% |
| Microsoft Power BI | 14.1% |
| Tableau Enterprise | 10.3% |
| Other | 71.7% |


| Company Size | Count |
|---|---|
| Small Business | 25 |
| Midsize Enterprise | 12 |
| Large Enterprise | 56 |
| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 12 |
| Large Enterprise | 20 |
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?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.
Domo is a cloud-based, mobile-first BI platform that helps companies drive more value from their data by helping organizations better integrate, interpret and use data to drive timely decision making and action across the business. The Domo platform enhances existing data warehouse and BI tools and allows users to build custom apps, automate data pipelines, and make data science accessible for anyone through automated insights that can be shared with internal or external stakeholders.
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