

OpenText Analytics Database (Vertica) and Dremio compete in the field of data management solutions. Vertica holds the upper hand in speed and performance, making it ideal for large-scale analytics, while Dremio is favored for its seamless integration capabilities.
Features: Vertica is recognized for its ability to handle large volumes of data with high speed and cost-efficiency. It excels in fast query performance and provides enhanced data compression. Dremio stands out for its integration abilities with various data storages, creation of virtual datasets, and offering versatile data management and lineage capabilities.
Room for Improvement: Vertica faces challenges in workload management, requiring better support for transactions and SQL optimization. Users seek improvements in integration with other databases. Dremio requires enhancements in handling large, complex queries, better support for Delta connectors, and integration with databases like Databricks and Salesforce.
Ease of Deployment and Customer Service: Vertica offers flexibility with on-premises, hybrid, and private cloud deployments but requires expert handling. Its technical support receives mixed reviews. Dremio, primarily in public and hybrid clouds, is easy to deploy and has generally excellent customer service, although it also experiences inconsistent technical support.
Pricing and ROI: Vertica offers flexible pricing based on data size and provides good ROI with real-time analytics, though it can become expensive without proper data management. Dremio's pricing is attractive, especially for budget-conscious businesses, offering competitive integration capabilities, although its community version has scalability limitations.
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.
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.
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 | Market Share (%) |
|---|---|
| Dremio | 7.1% |
| OpenText Analytics Database (Vertica) | 6.2% |
| Other | 86.7% |


| 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 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.
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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