

OpenText Analytics Database (Vertica) and Dremio compete in the data analytics and big data solutions sector. Vertica holds an advantage with its high-speed query performance and massive data load support, while Dremio stands out for its multi-data-source integration and user-friendly query building.
Features: Vertica is known for high-speed query performance, clustering, and scalability. It effectively manages Very Large Databases (VLDB) with robust support for OLAP and external procedures in C, Java, and R. Dremio supports multiple data sources including S3, Azure Data Factory, and Hive. It excels in advanced data lineage features and offers a user-friendly interface for query building.
Room for Improvement: Vertica faces challenges with transaction-heavy environments and lacks robust workload management tools. Complex data deletion processes and documentation enhancements are areas for development. Dremio needs better handling of large queries, expanded connector varieties, and improved SQL automation. Licencing limitations and integration with streaming pipelines also require attention.
Ease of Deployment and Customer Service: Vertica supports on-premises, hybrid, and public cloud deployments, offering flexibility but potential complexity. Customer service reviews are mixed. Dremio, primarily cloud-focused, faces integration challenges. Both could improve technical support, but Dremio is more accessible for cloud initiatives.
Pricing and ROI: Vertica uses a pricing model based on data storage, offering transparency and affordability for large deployments. Despite perceptions of high costs, it is seen as providing excellent ROI. Dremio's pricing is competitive with platforms like Snowflake but can become costly beyond community version limits. Both products deliver significant value through their analytic capabilities.
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.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
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 rate Dremio a nine in terms of stability.
Starburst comes with around 50 connectors now.
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.
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.
Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.
The first feature that stands out for me in Dremio is the federated type of query, which allows the possibility to use multiple endpoints without worrying about writing custom SQL that runs only for SQL Server or for Postgres and Redshift.
You just get the source, connect the data, get visualization, get connected, and do whatever you want.
| Product | Market Share (%) |
|---|---|
| Dremio | 7.9% |
| OpenText Analytics Database (Vertica) | 6.2% |
| Other | 85.9% |


| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 23 |
| Large Enterprise | 38 |
Dremio is a data analytics platform designed to simplify and expedite the data analysis process by enabling direct querying across multiple data sources without the need for data replication. This solution stands out due to its approach to data lake transformation, offering tools that allow users to access and query data stored in various formats and locations as if it were all in a single relational database.
At its core, Dremio facilitates a more streamlined data management experience. It integrates easily with existing data lakes, allowing organizations to continue using their storage of choice, such as AWS S3, Microsoft ADLS, or Hadoop, without data migration. Dremio supports SQL queries, which means it seamlessly integrates with familiar BI tools and data science frameworks, enhancing user accessibility and reducing the learning curve typically associated with adopting new data technologies.
What Are Dremio's Key Features?
What Benefits Should Users Expect?
When evaluating Dremio, potential users should look for feedback on its query performance, especially in environments with large and complex data sets. Reviews might highlight the efficiency gains from using Dremio’s data reflections and its ability to integrate with existing BI tools without significant changes to underlying data structures. Also, check how other users evaluate its ease of deployment and scalability, particularly in hybrid and cloud environments.
How is Dremio Implemented Across Different Industries?
Dremio is widely applicable across various industries, including finance, healthcare, and retail, where organizations benefit from rapid, on-demand access to large volumes of data spread across disparate systems. For instance, in healthcare, Dremio can be used to analyze patient outcomes across different data repositories, improving treatment strategies and operational efficiencies.
What About Dremio’s Pricing, Licensing, and Support?
Dremio offers a flexible pricing model that caters to different sizes and types of businesses, including a free community version for smaller teams and proof-of-concept projects. Their enterprise version is subscription-based, with pricing varying based on the deployment scale and support needs. Customer support is comprehensive, featuring dedicated assistance, online resources, and community support.
OpenText Analytics Database Vertica is known for its fast data loading and efficient query processing, providing scalability and user-friendliness with a low cost per TB. It supports large data volumes with OLAP, clustering, and parallel ingestion capabilities.
OpenText Analytics Database Vertica is designed to handle substantial data volumes with a focus on speed and efficient storage through its columnar architecture. It offers advanced performance features like workload isolation and compression, ensuring flexibility and high availability. The database is optimized for scalable data management, supporting data scientists and analysts with real-time reporting and analytics. Its architecture is built to facilitate hybrid deployments on-premises or within cloud environments, integrating seamlessly with business intelligence tools like Tableau. However, challenges such as improved transactional capabilities, optimized delete processes, and better real-time loading need addressing.
What features define OpenText Analytics Database Vertica?OpenText Analytics Database Vertica's implementation spans industries such as finance, healthcare, and telecommunications. It serves as a central data warehouse offering scalable management, high-speed processing, and geospatial functions. Companies benefit from its capacity to integrate machine learning and operational reporting, enhancing analytical capabilities.
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