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Compare Azure Databricks and IBM Cloud Pak for Data 
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At a Glance
Market Segments
Enterprise (49.5% of reviews)
Enterprise (50.0% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Azure Databricks
Star Rating
(227)4.5 out of 5
Market Segments
Enterprise (49.5% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Azure DatabricksIBM Cloud Pak for Data
Star Rating
(88)4.3 out of 5
Market Segments
Enterprise (50.0% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about IBM Cloud Pak for DataSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that IBM Cloud Pak for Data excels in Data Ingestion & Wrangling with a score of 9.5, making it a preferred choice for organizations needing robust data preparation capabilities. In contrast, Azure Databricks, while strong, scores slightly lower in this area, indicating it may not be as efficient for users focused on data wrangling.
- Reviewers mention that Azure Databricks shines in Real-Time Analytics with a score of 8.8, which is crucial for businesses requiring immediate insights. IBM Cloud Pak for Data, while competent, has a slightly lower score, suggesting it may not meet the same level of performance for real-time data processing.
- G2 users highlight that Azure Databricks offers superior Spark Integration with a score of 9.0, which is essential for users leveraging big data technologies. IBM Cloud Pak for Data's score of 8.6 indicates it may not provide the same level of seamless integration with Spark, potentially affecting users' workflows.
- Users on G2 report that IBM Cloud Pak for Data provides excellent Customization options, scoring 8.0 for Custom VMs, which allows for tailored environments. Azure Databricks, while offering customization, does not match this level of flexibility, which may be a deciding factor for enterprises with specific needs.
- Reviewers mention that Azure Databricks has a strong edge in Data Querying, scoring 9.3, which is vital for users needing efficient data retrieval. IBM Cloud Pak for Data's score of 9.1 is commendable but indicates that Azure Databricks may offer a more optimized experience in this area.
- Users say that IBM Cloud Pak for Data's Quality of Support is rated at 8.3, which is slightly lower than Azure Databricks' 8.4. This suggests that while both platforms provide good support, Azure Databricks may have a slight advantage in customer service responsiveness and effectiveness.
Pricing
Entry-Level Pricing
Azure Databricks
No pricing availableIBM Cloud Pak for Data
No pricing availableFree Trial
Azure Databricks
No trial information availableIBM Cloud Pak for Data
No trial information availableRatings
Meets Requirements
8.8
160
8.5
47
Ease of Use
8.7
161
8.1
47
Ease of Setup
8.6
75
7.2
26
Ease of Admin
8.5
61
7.6
27
Quality of Support
8.5
147
8.3
42
Has the product been a good partner in doing business?
8.8
60
8.1
25
Product Direction (% positive)
9.5
152
8.8
47
Features by Category
Customization
Not enough data
8.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Infrastructure
Not enough data
Not enough data
Not enough data
9.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Infrastructure Provision
Not enough data
7.7
8
Not enough data
8.1
7
Not enough data
7.9
7
Not enough data
8.3
6
Not enough data
8.5
8
Not enough data
7.9
8
Not enough data
8.3
8
Not enough data
8.3
7
Management
Not enough data
8.6
7
Not enough data
8.3
7
Not enough data
8.1
8
Functionality
Not enough data
8.3
8
System
Not enough data
9.5
11
Model Development
Not enough data
8.3
8
Not enough data
8.5
8
Not enough data
9.1
9
Not enough data
8.8
8
Model Development
Not enough data
8.5
12
Machine/Deep Learning Services
Not enough data
8.7
9
Not enough data
9.4
8
Not enough data
9.6
8
Not enough data
9.0
7
Machine/Deep Learning Services
Not enough data
9.2
8
Not enough data
9.3
7
Deployment
Not enough data
9.3
9
Not enough data
9.3
9
Not enough data
8.9
9
Generative AI
Not enough data
8.3
5
Not enough data
8.3
5
Not enough data
8.3
5
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Data Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
8.3
5
Analytics
Not enough data
Not enough data
Security
Not enough data
9.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI - Data Fabric
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Scalability and Performance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Cost and Efficiency - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration and Extensibility - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Security and Compliance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Usability and Support - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Statistical Tool
Not enough data
8.3
5
Not enough data
8.8
8
Not enough data
8.6
7
Data Analysis
Not enough data
8.7
9
Not enough data
9.0
7
Decision Making
Not enough data
8.3
6
Not enough data
8.8
7
Not enough data
8.8
8
Not enough data
8.3
8
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Data Transformation
8.8
130
|
Verified
8.5
27
|
Verified
9.3
83
|
Verified
9.1
15
|
Verified
Connectivity
8.4
120
|
Verified
8.0
23
|
Verified
9.0
133
|
Verified
8.6
22
|
Verified
9.0
129
|
Verified
8.1
25
|
Verified
9.1
132
|
Verified
8.7
24
|
Verified
Operations
8.3
127
|
Verified
8.7
26
|
Verified
8.6
127
|
Verified
8.9
25
|
Verified
8.5
117
|
Verified
8.4
23
|
Verified
8.5
111
|
Verified
8.8
24
|
Verified
9.3
77
|
Verified
8.7
13
|
Verified
Building Reports
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Platform
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
Azure Databricks and IBM Cloud Pak for Data are categorized as Big Data Analytics
Unique Categories
Azure Databricks has no unique categories
IBM Cloud Pak for Data is categorized as Data Fabric, Virtual Private Cloud (VPC), Data Science and Machine Learning Platforms, Predictive Analytics, Infrastructure as a Service (IaaS), and Generative AI Infrastructure
Reviews
Reviewers' Company Size
Azure Databricks
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM Cloud Pak for Data
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Information Technology and Services
23.0%
Computer Software
12.0%
Retail
5.0%
Financial Services
4.5%
Hospital & Health Care
4.0%
Other
51.5%
Computer Software
12.9%
Information Technology and Services
8.6%
Banking
8.6%
Financial Services
5.7%
Education Management
5.7%
Other
58.6%
Alternatives
Discussions
Is Azure Databricks PaaS or SAAS?
2 Comments
PG
Its PaaS tool as it offers developers to build their ETL pipelines for large datasets using multi cluster environment of sparkRead more
What is the best way to databricks in ADF
1 Comment
AS
Firstly, you need to create a Linked Service for ADF to connect to your Databricks Cluster. You can connect to as many clusters as you want.
Then create a...Read more
IBM Cloud Pak for Data has no discussions with answers
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