Databricks and Amazon SageMaker compete in the big data processing and machine learning platforms category. Databricks has the upper hand in terms of versatility and collaboration capabilities, while SageMaker excels in comprehensive machine learning tools and integration with AWS services.
Features: Databricks offers versatility with support for multiple programming languages and extensive built-in functions. Its scalability and integration capabilities enhance big data processing, combined with collaborative features and an intuitive environment that boosts productivity. Amazon SageMaker provides a broad range of tools for managing machine learning workflows with built-in algorithms and efficient model deployment. Seamless integration with AWS services, AutoML features, and simplified model deployment are key advantages.
Room for Improvement: Databricks could improve its visualization capabilities and integration with popular BI tools like Power BI and Tableau. Pricing is a concern with calls for more flexibility and transparent cost structures. SageMaker requires better documentation, more affordable pricing, and enhanced support for serverless GPUs. The interface poses challenges for beginners, and integration with some data sources needs refinement.
Ease of Deployment and Customer Service: Databricks offers flexible deployment options across public, hybrid, and private clouds with positive feedback on support services and proactive customer service. SageMaker also operates primarily on public clouds with comprehensive AWS integration. Although its supporting documentation is well-regarded, users seek better guidance on complex configurations and improved deployment communication.
Pricing and ROI: Databricks provides competitive cloud pricing with a pay-as-you-go model but is perceived as expensive compared to open-source options. Despite this, it delivers significant ROI through efficient big data processing. SageMaker is considered costly due to its pricing nature and expensive deployments, yet it offers robust machine learning capabilities. Both platforms showcase potential ROI by streamlining operations and cutting traditional infrastructure expenses, though pricing complexity remains a barrier for some.
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.
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 rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
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 patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
I would rate the scalability of this solution as very high, about nine out of ten.
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.
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 | Market Share (%) |
|---|---|
| Databricks | 14.5% |
| Amazon SageMaker | 6.0% |
| Other | 79.5% |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 11 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 25 |
| Midsize Enterprise | 12 |
| Large Enterprise | 56 |
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.
Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.
Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.
What are the key features of Databricks?
What are the benefits or ROI to look for in Databricks reviews?
Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.
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