TrustRadius

Apache Spark MLib vs. Azure Databricks

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark MLib
Score 9.0 out of 10
N/A
MLlib is Apache Spark's scalable machine learning library.N/A
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
Pricing
Apache Spark MLibAzure Databricks
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache Spark MLibAzure Databricks
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Features
Apache Spark MLibAzure Databricks
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Apache Spark MLib
-
Ratings
Azure Databricks
8.1
2 Ratings
3% below category average
Connect to Multiple Data Sources00 Ratings6.22 Ratings
Extend Existing Data Sources00 Ratings9.02 Ratings
Automatic Data Format Detection00 Ratings9.12 Ratings
MDM Integration00 Ratings8.01 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Apache Spark MLib
-
Ratings
Azure Databricks
6.4
2 Ratings
27% below category average
Visualization00 Ratings5.92 Ratings
Interactive Data Analysis00 Ratings6.82 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Apache Spark MLib
-
Ratings
Azure Databricks
8.0
2 Ratings
2% below category average
Interactive Data Cleaning and Enrichment00 Ratings7.02 Ratings
Data Transformations00 Ratings8.92 Ratings
Data Encryption00 Ratings9.12 Ratings
Built-in Processors00 Ratings7.12 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Apache Spark MLib
-
Ratings
Azure Databricks
8.3
2 Ratings
1% below category average
Multiple Model Development Languages and Tools00 Ratings8.12 Ratings
Automated Machine Learning00 Ratings8.92 Ratings
Single platform for multiple model development00 Ratings8.12 Ratings
Self-Service Model Delivery00 Ratings8.12 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Apache Spark MLib
-
Ratings
Azure Databricks
8.5
2 Ratings
0% below category average
Flexible Model Publishing Options00 Ratings8.02 Ratings
Security, Governance, and Cost Controls00 Ratings9.12 Ratings
Best Alternatives
Apache Spark MLibAzure Databricks
Small Businesses
InterSystems IRIS
InterSystems IRIS
Score 7.7 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
Posit
Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache Spark MLibAzure Databricks
Likelihood to Recommend
-
(0 ratings)
9.8
(3 ratings)
Usability
-
(0 ratings)
8.0
(1 ratings)
User Testimonials
Apache Spark MLibAzure Databricks
Likelihood to Recommend
Apache
No answers on this topic
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
Pros
Apache
No answers on this topic
Microsoft
  • SQL
  • Data management
  • Data access
Verified User
Anonymous
Read full review
Cons
Apache
No answers on this topic
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
Usability
Apache
No answers on this topic
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
Alternatives Considered
Apache
No answers on this topic
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
Return on Investment
Apache
No answers on this topic
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
ScreenShots