

Alteryx and Dremio both operate in the data analytics and processing software market. Alteryx seems to have the upper hand in terms of advanced data preparation and predictive analytics capabilities, while Dremio shines in integration and cost-effectiveness.
Features: Alteryx is renowned for its intuitive and codeless data blending, allowing users to handle large datasets effortlessly and output them to Tableau. It excels at drag-and-drop workflow creation, spatial analysis, and predictive analytics. Dremio integrates seamlessly with existing data storage systems, enables users to interact with data virtually, and efficiently executes complex queries without extra resources.
Room for Improvement: Alteryx could enhance its visualization features and integrate more interactive solutions internally. It also needs to standardize certain advanced analytics for user-friendliness. Dremio would benefit from improved support for connectors, better handling of nested queries, and addressing integration performance issues with large queries.
Ease of Deployment and Customer Service: Alteryx supports primarily on-premises deployment, with some cloud options, and benefits from a strong user community, though direct support may be less efficient. Dremio offers hybrid cloud flexibility but faces challenges with customer support and documentation that require users to engage in additional troubleshooting for optimal performance.
Pricing and ROI: Alteryx is seen as costly, presenting barriers for smaller companies despite users recognizing significant ROI through efficiency gains and reduced manual work. Dremio is considered more cost-effective, with favorable pricing compared to competitors like Snowflake, offering significant ROI potential through data lake capabilities and infrastructure cost savings.
Tasks that earlier took hours in Excel or SQL are now completed in minutes.
Alteryx would actually save time and a lot of money and effort for the team and increase efficiency.
Alteryx helps familiarize managers with artificial intelligence-driven possibilities.
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.
I contacted customer support once or twice, and they were quick to respond.
The customer service was not good because we weren't premium support users.
Customer support is good since I've had no issues and can easily contact representatives who respond promptly.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
Alteryx can be scaled to different machines or scaled up with different servers and deployed in the cloud.
Alteryx is scalable for most enterprise analytics and data preparation workloads.
Alteryx is scalable, and I would give it eight out of ten.
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.
I didn't need to reach out to Alteryx for support because available documents usually provide enough information to resolve issues.
I have not encountered any lagging, crashing, or instability in the system during these three months of usage.
I have not noticed anything with the product itself, but with some of the connectors they have provided, there are some issues.
I rate Dremio a nine in terms of stability.
The tool could include more native connectors, such as for global ERPs, instead of requiring additional fees for these connections.
The support structure changed; initially, we received great support, however, it later became less reliable due to licensing issues and a tiered support system.
The additional features that Alteryx needs to work on to make it more competitive include better collaboration and easier integration through API.
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.
The price is very high, with licensing typically starting around five thousand dollars plus user per year.
Alteryx is more cost-effective compared to Informatica licenses, offering savings.
It has a fair price when considering a larger-scale implementation.
Alteryx not only represents data but also supports decision-making by suggesting the next steps.
Analysts who do not have any coding experience can still work on the transformation and preparation of data, which is quite useful.
Alteryx includes built-in tools such as drive time analysis and linear regression, which are much harder to achieve in standard BI tools such as Power BI or Tableau.
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.
| Product | Mindshare (%) |
|---|---|
| Alteryx | 3.8% |
| Dremio | 2.2% |
| Other | 94.0% |

| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 16 |
| Large Enterprise | 54 |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
Alteryx provides user-friendly, no-code tools for data blending, preparation, and analysis. Its drag-and-drop interface and in-database capabilities simplify integration with data sources while maintaining data integrity.
Alteryx offers a comprehensive suite for automation of data workflows, reducing manual tasks and enhancing processing efficiency. Known for robust predictive and spatial analytics, it effectively handles large datasets. The platform's flexibility allows for custom script deployments, supported by a strong community. However, Alteryx faces challenges with high pricing, lack of cloud support, and limited data visualization tools. Users express a need for more in-built data science functionalities, improved API integration, and a smoother learning curve. Connectivity and documentation gaps, along with complex workflows, are noted concerns, suggesting areas for enhancement. Alteryx is widely used for tasks like ETL processes, data preparation, predictive modeling, and report generation, supporting functions like financial projections and spatial analysis.
What features define Alteryx?Alteryx is implemented across industries for diverse needs such as anomaly detection in finance, customer segmentation in marketing, and tax automation in auditing. Teams leverage its capabilities for data blending and predictive modeling to enhance operational efficiency and address specific business needs effectively.
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.
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