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.
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.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
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.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
I rate Dremio a nine in terms of stability.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
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.
Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources.
I assess Apache Hadoop's fault tolerance during hardware failures positively since we have hardware failover, which works without problems.
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.
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
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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