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.
Amazon SageMaker definitely provides 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.
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.
I would rate customer support a ten out of ten.
We contact the technical support at least weekly; currently, it's more or less daily because we face performance issues, crashes of queries sometimes, and we also need support for configuration of the query engines and so on.
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.
Dremio's scalability depends on your hardware and your deployment.
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.
We refactored the query, and that worked, but it still doesn't make you confident to go to production with a product that might crash depending on the query you send it.
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.
Before we wrap up, I want to add that Dremio is lacking a semantic layer in a manner that the user can just choose dimensions, metrics, and filters to query the data.
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.
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.
I chose eight out of ten because Dremio is expensive for the Enterprise version, particularly regarding licensing.
I had a good experience with pricing, as the pricing seems okay to me and the client.
Dremio does have a core of the engine that is open-source, but all the important features for the enterprise version are under license.
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.
Multiple data sources can be queried in one query, and it is not just a query engine.
| Product | Mindshare (%) |
|---|---|
| Amazon SageMaker | 3.2% |
| Dremio | 2.0% |
| Other | 94.8% |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 11 |
| Large Enterprise | 18 |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 6 |
| Large Enterprise | 5 |
Amazon SageMaker accelerates machine learning workflows by offering features like Jupyter Notebooks, AutoML, and hyperparameter tuning, while integrating seamlessly with AWS services. It supports flexible resource selection, effective API creation, and smooth model deployment and scaling.
Providing a comprehensive suite of tools, Amazon SageMaker simplifies the development and deployment of machine learning models. Its integration with AWS services like Lambda and S3 enhances efficiency, while SageMaker Studio, featuring Model Monitor and Feature Store, supports streamlined workflows. Users call for improvements in IDE maturity, pricing, documentation, and enhanced serverless architecture. By addressing scalability, big data integration, GPU usage, security, and training resources, SageMaker aims to better assist in machine learning demands and performance optimization.
What features does Amazon SageMaker offer?In industries like finance, retail, and healthcare, Amazon SageMaker supports training and deploying machine learning models for outlier detection, image analysis, and demand forecasting. It aids in chatbot implementation, recommendation systems, and predictive modeling, enhancing data science collaboration and leveraging compute resources efficiently. Tools like Jupyter notebooks, Autopilot, and BlazingText facilitate streamlined AI model management and deployment, increasing productivity and accuracy in industry-specific applications.
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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