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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.
In a world surrounded by data, tools that allow navigation of large data volumes ensure decisions are data-driven.
Power BI is easy to deploy within an hour, providing robust security against data leaks.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
The significant drawback I notice is that Microsoft's size makes it hard to get specific change requests addressed unless they involve a bug.
We have a partnership with Microsoft, involving multiple weekly calls with dedicated personnel to ensure our satisfaction.
The support is good because there is also a community available.
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.
You expect only a small percentage of users concurrently, but beyond a thousand concurrent users, it becomes difficult to manage.
With increasing AI capabilities, architectural developments within Microsoft, and tools like Fabric, I expect Power BI to scale accordingly.
As more data is processed, performance issues may arise.
I rate Dremio a nine in terms of stability.
In terms of stability, there's no data loss or leakage, and precautions are well-managed by Microsoft.
We typically do not have problems with end-user tools like Excel and Power BI.
It is very stable for small data, but with big data, there are performance challenges.
Starburst comes with around 50 connectors now.
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.
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.
This makes Power BI difficult to manage as loading times can reach one or two minutes, which is problematic today.
Access was more logical in how it distinguished between data and its formatting.
Microsoft updates Power BI monthly based on user community feedback.
I found the setup cost to be expensive
Power BI isn't very cheap, however, it is economical compared to other solutions available.
The pricing for Microsoft Power BI is low, which is a good selling point.
Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.
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.
You just get the source, connect the data, get visualization, get connected, and do whatever you want.
In today's data-driven environment, these tools are of substantial value, particularly for large enterprises with numerous processes that require extensive data analysis.
Within the organization, Microsoft Power BI is used to create dashboards and gain insights into data, enhancing data-driven decision-making.
To reduce the need for highly skilled personnel, we can engage someone who is just familiar and has a basic understanding of Microsoft Power BI, while AI can handle the major tasks through either agent AI or requirement analysis.
| Product | Market Share (%) |
|---|---|
| Dremio | 2.3% |
| Databricks | 9.6% |
| KNIME Business Hub | 8.7% |
| Other | 79.4% |
| Product | Market Share (%) |
|---|---|
| Microsoft Power BI | 9.4% |
| Tableau Enterprise | 6.7% |
| Amazon QuickSight | 3.7% |
| Other | 80.2% |


| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 135 |
| Midsize Enterprise | 57 |
| Large Enterprise | 165 |
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.
Microsoft Power BI is a powerful tool for data analysis and visualization. This tool stands out for its ability to merge and analyze data from various sources. Widely adopted across different industries and departments, Power BI is instrumental in creating visually appealing dashboards and generating insightful business intelligence reports. Its intuitive interface, robust visualization capabilities, and seamless integration with other Microsoft applications empower users to easily create interactive reports and gain valuable insights.
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