

Anaconda Business and Dremio compete in the data management and data science platform market. Anaconda Business has the upper hand with its comprehensive library ecosystem and security, while Dremio excels in data integration and management across diverse environments.
Features: Anaconda Business is known for an extensive collection of pre-built libraries crucial for data science, an integration of Jupyter Notebooks for real-time output visualization, and advanced security and package management capabilities supporting Python and R. Dremio stands out with its ability to sit on various data storages like Amazon S3, features ensuring effective data lineage and providence, and creation of visual tables which facilitate smooth data governance and transformation.
Room for Improvement: Anaconda Business can improve performance under heavy workloads, simplify package updates, and enhance documentation accuracy. Dremio, on the other hand, faces challenges with execution speed for large and complex queries, needs better Delta connector support, and could benefit from more comprehensive documentation, particularly for its community version.
Ease of Deployment and Customer Service: Anaconda Business, often deployed on-premises, is supported by extensive community backing and active customer service contributing to a smoother user experience. Dremio, generally operating in public and hybrid cloud environments, struggles a bit with technical support, relying more on community-driven solutions, but emphasizes its cloud-centric strategy.
Pricing and ROI: Anaconda Business offers a cost-effective solution being open-source, significantly reducing setup and licensing costs, though additional resources like GPUs might incur substantial costs. Dremio, although competitively priced compared to alternatives like Snowflake, becomes expensive when scaling beyond the community version, delivering substantial value through its extensive data management features which justify the costs.
Everyone being able to work smoothly without unnecessary delays.
I have seen a return on investment; specifically, when we talk about efficiency, it's both time-saving and money-saving.
I have seen a return on investment with time saved by 50% and less downtime, allowing the team to deliver projects faster with fewer errors.
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.
Anaconda Business customer support is very active with a quick response time.
Overall, support was reliable when we needed it, just not super-fast every single time.
The customer support for Anaconda Business provides a better approach.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
As more environments or users get added, it still runs smoothly without major slowdowns.
Anaconda Business scales very well because it is built around centralized environment and package management.
Anaconda does not have scalability restrictions as it depends on the type of machine running it.
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.
Earlier, setting up or troubleshooting conflicts could take anywhere from thirty minutes to an hour, but now most setups just work.
Anaconda Business is stable to an extent, but it sometimes crashes on systems with insufficient RAM.
I rate Dremio a nine in terms of stability.
It would also be nice to have clearer error messages when something fails, so it is easier to understand what went wrong without digging too much.
They should enhance the security point of view; it's good, but it needs some more advanced features.
The pricing should be a little lower for a single person to use, as it might be affordable for an organization, but for my single use, it is difficult.
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.
Anaconda is an open-source tool, so I do not pay anything for it.
My experience with pricing, setup cost, and licensing is that it is a little costly, but it is useful.
My experience with pricing, setup cost, and licensing indicates that it is a bit costly, but it is useful.
Anaconda Business has positively impacted my organization because, when discussing the security point of view, it's exceptional; when comparing it to other solutions, Anaconda Business is superior.
We find the advanced security, governance, and collaborative features for organizations using Python and R particularly useful.
Anaconda Business positively impacts our organization by protecting us from compliance and security risks while keeping the environment consistent, allowing our team to focus on insight and innovation instead of worrying about setups, security, and software issues.
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 (%) |
|---|---|
| Anaconda Business | 2.6% |
| Dremio | 2.3% |
| Other | 95.1% |

| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 2 |
| Large Enterprise | 19 |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
Anaconda Business provides a comprehensive platform for data science applications, integrating extensive libraries and seamless Python and R compatibility, enhancing developer productivity.
Anaconda Business offers data science professionals a platform combining extensive library support with pre-built models and seamless integration of Python and R environments. With features like a user-friendly interface and integrated Jupyter Notebook, it facilitates real-time code execution and debugging. Environmental management is simplified via Conda, while cloud-based access and package management enhance user experience. Community support and integration with applications like RStudio and Jupyter aid in data science and deep learning tasks.
What are the key features of Anaconda Business?Anaconda Business is widely used in industries like machine learning and data analysis, where it's employed for tasks such as predictive modeling and data visualization. Organizations utilize its compatibility with tools like Scikit-learn and TensorFlow for creating statistical models, supporting applications in fields such as analytics, education, subrogation, and warehouse management.
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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