Amazon SageMaker and Dremio compete in the machine learning and data management sector. Based on the comparison, Amazon SageMaker seems to lead with its comprehensive machine learning workflow capabilities and integration within the AWS ecosystem.
Features: Amazon SageMaker offers features like Random Cut Forest for anomaly detection, seamless AWS service integration, and SageMaker Studio, known for simplifying model deployment and monitoring. Dremio stands out with its capability to layer over multiple data storages, memory competition efficiency, and notable federated query capabilities that facilitate complex query building across data sources.
Room for Improvement: Amazon SageMaker faces challenges with its immature IDE, expensive pricing, and limited integration with systems like Hadoop. Users wish for more straightforward interfaces and increased documentation. Dremio could enhance its Delta connector support, improve data cataloging, and refine SQL capabilities in its interface to manage dynamic scaling and performance better during large queries.
Ease of Deployment and Customer Service: Amazon SageMaker leverages public cloud deployment within AWS, offering varying customer service responsiveness, favoring premium clients. Dremio provides deployment flexibility with hybrid and on-premises environments, albeit relying on documentation over direct support, where improvements in clarity and speed could be beneficial.
Pricing and ROI: Pricing complexity affects both Amazon SageMaker and Dremio. SageMaker's pay-as-you-go model makes it costly yet justified by its comprehensive offerings. Dremio, considered less expensive than Snowflake, presents good value relative to its features. Both products offer ROI potential, with SageMaker enabling cost reductions and Dremio adding value based on specific use cases.
The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.
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.
The technical support from AWS is excellent.
The support is very good with well-trained engineers.
The response time is generally swift, usually within seven to eight hours.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
The availability of GPU instances can be a challenge, requiring proper planning.
It works very well with large data sets from one terabyte to fifty terabytes.
Amazon SageMaker is scalable and works well from an infrastructure perspective.
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.
There are issues, but they are easily detectable and fixable, with smooth error handling.
The product has been stable and scalable.
I rate the stability of Amazon SageMaker between seven and eight.
I rate Dremio a nine in terms of stability.
Having all documentation easily accessible on the front page of SageMaker would be a great improvement.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
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 cost for small to medium instances is not very high.
For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.
The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
They offer insights into everyone making calls in my organization.
The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models.
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 (%) |
|---|---|
| Amazon SageMaker | 4.0% |
| Dremio | 2.4% |
| Other | 93.6% |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 11 |
| Large Enterprise | 17 |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
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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