Databricks and Amazon SageMaker are competitors in the cloud-based analytics and machine learning platform category. Databricks seems to have the upper hand in integration and flexibility with open-source tools, while Amazon SageMaker shines with its AWS ecosystem integration.
Features: Databricks boasts rich features including integrated machine learning libraries, Delta Lake for handling large data tasks, and seamless support for multiple programming languages. It excels in collaboration and data visualization capabilities. Amazon SageMaker is distinguished by its exceptional AWS integrations and comprehensive model deployment capabilities, which are crucial for handling various machine learning workloads.
Room for Improvement: Databricks could enhance advanced visualization tools, extend integration features, and provide better documentation alongside improved cost transparency. Users also desire improvements in model scoring and the user interface. Amazon SageMaker could improve usability, increase integration with third-party data tools, and enhance documentation, especially considering its high costs and complex user experience that affect scalability and cost management.
Ease of Deployment and Customer Service: Databricks offers versatile deployment options across cloud environments, with a setup process generally praised for its swiftness and clarity. Customer support is effective, though there are remarks about slower response times. Amazon SageMaker provides cloud and on-premises deployment with notable AWS service integration. However, some cost constraints and complexity in solution management have been highlighted by users.
Pricing and ROI: Databricks follows a pay-per-use model, which is attractive for large-scale analytics but can be considered expensive. Its users appreciate the value in terms of cloud cost savings and significant ROI driven by efficiencies and scalability. Amazon SageMaker also uses a pay-as-you-go model and is noted for high computing instance costs, although its integration with AWS can make these costs justifiable for some users. For others, the pricing is seen as restrictive, suggesting a need for cost optimization to boost competitiveness.
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.
This reduction in both time and money resulted in real-time impact and significant cost savings.
For a lot of different tasks, including machine learning, it is a nice solution.
When it comes to big data processing, I prefer Databricks over other solutions.
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.
Whenever we reach out, they respond promptly.
As of now, we are raising issues and they are providing solutions without any problems.
I would give Databricks customer support a rating of ten.
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.
The sky's the limit with Databricks.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
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.
They release patches that sometimes break our code.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
Databricks is definitely a very stable product and reliable.
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.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
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.
It is not a cheap solution.
I believe that in terms of credits for Databricks, we're spending between £15,000 and £20,000 a month.
My experience with pricing, implementation costs, and licensing is that it is very efficient and very fast.
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.
Databricks' capability to process data in parallel enhances data processing speed.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
| Product | Mindshare (%) |
|---|---|
| Databricks | 7.6% |
| Amazon SageMaker | 3.4% |
| Other | 89.0% |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 11 |
| Large Enterprise | 18 |
| Company Size | Count |
|---|---|
| Small Business | 27 |
| Midsize Enterprise | 12 |
| Large Enterprise | 57 |
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.
Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.
Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.
What features make Databricks unique?
What benefits can users expect from Databricks?
In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.
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