TrustRadius

Azure Databricks vs. Domino Enterprise MLOps Platform

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Azure Databricks
Score 8.7 out of 10
N/A
Azure Databricks is a service available on Microsoft's Azure platform and suite of products. It provides the latest versions of Apache Spark so users can integrate with open source libraries, or spin up clusters and build in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. The solution includes autoscaling and auto-termination to improve…N/A
Domino Enterprise MLOps Platform
Score 8.0 out of 10
Enterprise companies (1,001+ employees)
The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality and impact of data science at scale. Domino is presented as open and flexible, to empower professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Domino Enterprise MLOps…N/A
Pricing
Azure DatabricksDomino Enterprise MLOps Platform
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Azure DatabricksDomino Enterprise MLOps Platform
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Features
Azure DatabricksDomino Enterprise MLOps Platform
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Azure Databricks
8.1
2 Ratings
3% below category average
Domino Enterprise MLOps Platform
-
Ratings
Connect to Multiple Data Sources6.32 Ratings00 Ratings
Extend Existing Data Sources9.02 Ratings00 Ratings
Automatic Data Format Detection9.12 Ratings00 Ratings
MDM Integration8.01 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Azure Databricks
6.3
2 Ratings
28% below category average
Domino Enterprise MLOps Platform
-
Ratings
Visualization5.92 Ratings00 Ratings
Interactive Data Analysis6.82 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Azure Databricks
8.0
2 Ratings
2% below category average
Domino Enterprise MLOps Platform
-
Ratings
Interactive Data Cleaning and Enrichment7.02 Ratings00 Ratings
Data Transformations8.92 Ratings00 Ratings
Data Encryption9.12 Ratings00 Ratings
Built-in Processors7.12 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Azure Databricks
8.3
2 Ratings
1% below category average
Domino Enterprise MLOps Platform
-
Ratings
Multiple Model Development Languages and Tools8.12 Ratings00 Ratings
Automated Machine Learning8.92 Ratings00 Ratings
Single platform for multiple model development8.12 Ratings00 Ratings
Self-Service Model Delivery8.12 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Azure Databricks
8.5
2 Ratings
0% below category average
Domino Enterprise MLOps Platform
-
Ratings
Flexible Model Publishing Options8.02 Ratings00 Ratings
Security, Governance, and Cost Controls9.12 Ratings00 Ratings
Best Alternatives
Azure DatabricksDomino Enterprise MLOps Platform
Small Businesses
Jupyter Notebook
Jupyter Notebook
Score 9.4 out of 10
Jupyter Notebook
Jupyter Notebook
Score 9.4 out of 10
Medium-sized Companies
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
Enterprises
Posit
Posit
Score 10.0 out of 10
Posit
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Score 10.0 out of 10
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User Ratings
Azure DatabricksDomino Enterprise MLOps Platform
Likelihood to Recommend
9.8
(3 ratings)
-
(0 ratings)
Usability
8.0
(1 ratings)
-
(0 ratings)
User Testimonials
Azure DatabricksDomino Enterprise MLOps Platform
Likelihood to Recommend
Microsoft
Suppose you have multiple data sources and you want to bring the data into one place, transform it and make it into a data model. Azure Databricks is a perfectly suited solution for this. Leverage spark JDBC or any external cloud based tool (ADG, AWS Glue) to bring the data into a cloud storage. From there, Azure Databricks can handle everything. The data can be ingested by Azure Databricks into a 3 Layer architecture based on the delta lake tables. The first layer, raw layer, has the raw as is data from source. The enrich layer, acts as the cleaning and filtering layer to clean the data at an individual table level. The gold layer, is the final layer responsible for a data model. This acts as the serving layer for BI For BI needs, if you need simple dashboards, you can leverage Azure Databricks BI to create them with a simple click! For complex dashboards, just like any sql db, you can hook it with a simple JDBC string to any external BI tool.
Verified User
Anonymous
Read full review
Domino Data Lab
No answers on this topic
Pros
Microsoft
  • SQL
  • Data management
  • Data access
Verified User
Anonymous
Read full review
Domino Data Lab
No answers on this topic
Cons
Microsoft
  • Their pipeline workflow orchestration is pretty primitive. Lacks some common features
  • Workspace UI and navigation requires steep learning curve
  • Personally, I am not fond of their autosave feature. Its dangerous for production level notebooks scripts
Pranshu Gupta | TrustRadius Reviewer
Pranshu Gupta
Associate Data Engineer
Read full review
Domino Data Lab
No answers on this topic
Usability
Microsoft
Based on my extensive use of Azure Databricks for the past 3.5 years, it has evolved into a beautiful amalgamation of all the data domains and needs. From a data analyst, to a data engineer, to a data scientist, it jas got them all! Being language agnostic and focused on easy to use UI based control, it is a dream to use for every Data related personnel across all experience levels!
Verified User
Anonymous
Read full review
Domino Data Lab
No answers on this topic
Alternatives Considered
Microsoft
Against all the tools I have used, Azure Databricks is by far the most superior of them all! Why, you ask? The UI is modern, the features are never ending and they keep adding new features. And to quote Apple, "It just works!" Far ahead of the competition, the delta lakehouse platform also fares better than it counterparts of Iceberg implementation or a loosely bound Delta Lake implementation of Synapse
Verified User
Anonymous
Read full review
Domino Data Lab
No answers on this topic
Return on Investment
Microsoft
  • Helped reduce time for collecting data
  • Reduced cost in maintaining multiple data sources
  • Access for multiple users and management of users/data in a single platform
Verified User
Anonymous
Read full review
Domino Data Lab
No answers on this topic
ScreenShots

Domino Enterprise MLOps Platform Screenshots

Screenshot of The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality and impact of data science at scale.Screenshot of The Self-Service Infrastructure Portal makes data science teams more productive with access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming DevOps tasks, data scientists can focus on the tasks at hand.Screenshot of The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle.Screenshot of The System of Record has a reproducibility engine, search and knowledge management, and integrated project management. Teams can find, reuse, reproduce, and build on any data science work to amplify innovation.Screenshot of Model monitoring capabilities ensure that all production models maintain peak performance. Automated alerts provide notification when data and quality drift occurs so users can re-train, rebuild, and re-publish the model.Screenshot of Nexus is a single pane of glass to run data science and ML workloads across any compute cluster — in any cloud, region, or on-premises. It unifies data science silos across the enterprise, providing one place to build, deploy, and monitor models.