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Compare Cloudera Data Engineering and IBM watsonx.ai 
Featured Products
At a Glance
Market Segments
Mid-Market (39.1% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Cloudera Data Engineering
Star Rating
(23)4.7 out of 5
Market Segments
Mid-Market (39.1% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about Cloudera Data EngineeringIBM watsonx.ai
Star Rating
(135)4.4 out of 5
Market Segments
Small-Business (41.0% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Free Trial is available
Learn more about IBM watsonx.aiSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Cloudera Data Engineering excels in ease of use and setup, with users highlighting its intuitive interface and quick onboarding process. Many appreciate how it integrates seamlessly with tools like Apache Spark and Airflow, making data pipeline management efficient.
- Users say that IBM watsonx.ai offers a robust customization capability, particularly for creating AI assistants. Reviewers commend its user-friendly AI studio, which allows for the efficient development of chatbots and other applications using pre-trained models.
- According to verified reviews, Cloudera Data Engineering is praised for its workflow automation and analytics capabilities, especially in handling both historical and streaming data. This makes it a strong choice for organizations focused on data processing at scale.
- Reviewers mention that while IBM watsonx.ai is a solid MLOps platform, it sometimes lacks the depth of features found in Cloudera Data Engineering, particularly in areas like data ingestion and wrangling, where Cloudera's offerings are noted to be superior.
- Users highlight that Cloudera Data Engineering has received higher ratings for quality of support, with many noting the responsive assistance provided by the Cloudera team, which enhances the overall user experience.
- G2 reviewers indicate that IBM watsonx.ai, while having a larger market presence, has a slightly lower overall satisfaction score compared to Cloudera Data Engineering. This suggests that while it may cater well to small businesses, it may not meet the same level of satisfaction for mid-market users.
Pricing
Entry-Level Pricing
Cloudera Data Engineering
No pricing availableIBM watsonx.ai
No pricing availableFree Trial
Cloudera Data Engineering
No trial information availableIBM watsonx.ai
Free Trial is available
Ratings
Meets Requirements
9.2
23
8.8
87
Ease of Use
9.3
23
8.8
120
Ease of Setup
9.8
7
8.4
111
Ease of Admin
9.5
7
8.7
38
Quality of Support
9.2
22
8.7
85
Has the product been a good partner in doing business?
9.3
7
8.9
38
Product Direction (% positive)
10.0
23
9.9
88
Features by Category
Deployment
Not enough data
9.1
9
Not enough data
8.5
9
Not enough data
7.8
9
Not enough data
8.7
9
Not enough data
8.7
9
Deployment
Not enough data
9.3
9
Not enough data
8.7
9
Not enough data
8.3
9
Not enough data
8.9
9
Not enough data
9.1
9
Management
Not enough data
8.0
9
Not enough data
8.5
9
Not enough data
8.5
9
Not enough data
9.3
9
Operations
Not enough data
9.1
9
Not enough data
8.7
9
Not enough data
9.3
9
Management
Not enough data
8.5
9
Not enough data
9.0
8
Not enough data
8.5
8
Generative AI
Not enough data
9.1
9
Not enough data
9.3
9
System
9.5
16
8.2
31
Model Development
9.8
16
8.7
33
9.3
17
8.3
35
9.2
15
8.7
31
9.3
16
8.2
33
Model Development
9.2
15
8.5
32
Machine/Deep Learning Services
9.2
16
Feature Not Available
9.4
16
8.9
32
9.3
16
8.6
32
9.2
16
8.1
32
Machine/Deep Learning Services
9.6
15
8.5
32
9.6
14
8.8
32
Deployment
9.3
16
8.2
32
9.5
17
8.6
32
9.4
16
8.8
32
Generative AI
9.4
14
8.8
31
9.0
14
8.8
31
9.1
15
Feature Not Available
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 Type
Not enough data
8.8
13
Not enough data
Feature Not Available
Not enough data
8.5
12
Synthesis Type
Not enough data
9.0
12
Not enough data
9.2
12
Data Transformation
Not enough data
8.6
12
Not enough data
9.3
12
Not enough data
9.7
12
Not enough data
9.2
12
Not enough data
9.2
12
Scalability and Performance - Generative AI Infrastructure
Not enough data
9.4
8
Not enough data
9.0
8
Not enough data
9.4
8
Cost and Efficiency - Generative AI Infrastructure
Not enough data
7.9
8
Not enough data
8.6
7
Not enough data
8.3
7
Integration and Extensibility - Generative AI Infrastructure
Not enough data
9.5
7
Not enough data
8.6
7
Not enough data
8.8
7
Security and Compliance - Generative AI Infrastructure
Not enough data
8.3
7
Not enough data
8.8
7
Not enough data
8.6
7
Usability and Support - Generative AI Infrastructure
Not enough data
9.0
8
Not enough data
9.0
7
Content Generation - AI Content Creation Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management - AI Content Creation Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration - Machine Learning
Not enough data
9.0
21
Learning - Machine Learning
Not enough data
9.2
23
Not enough data
9.1
22
Not enough data
9.0
21
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
8.7
15
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
9
Not enough data
8.1
6
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
6
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
6
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
8.3
10
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
8.3
6
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
7.4
13
Not enough data
8.7
9
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
9.4
6
Not enough data
8.8
10
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
7
Not enough data
8.9
6
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.4
6
Not enough data
9.2
6
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
6
Customization - AI Agent Builders
Not enough data
9.0
8
Not enough data
9.2
8
Not enough data
9.0
7
Functionality - AI Agent Builders
Not enough data
8.6
7
Not enough data
9.2
8
Not enough data
9.3
7
Not enough data
8.8
7
Data and Analytics - AI Agent Builders
Not enough data
9.0
7
Not enough data
8.8
7
Not enough data
9.0
7
Integration - AI Agent Builders
Not enough data
9.2
8
Not enough data
9.0
7
Not enough data
9.0
7
Not enough data
8.6
7
Data Ingestion & Preparation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Model Construction & Automation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
Cloudera Data Engineering and IBM watsonx.ai are categorized as Data Science and Machine Learning Platforms
Unique Categories
Cloudera Data Engineering has no unique categories
IBM watsonx.ai is categorized as Synthetic Data, MLOps Platforms, Machine Learning, AI Content Creation Platforms, Generative AI Infrastructure, Large Language Model Operationalization (LLMOps), AI Agent Builders, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
Cloudera Data Engineering
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM watsonx.ai
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
26.1%
Computer Software
26.1%
Consulting
13.0%
Banking
8.7%
Program Development
4.3%
Other
21.7%
Information Technology and Services
19.7%
Computer Software
11.5%
Consulting
7.4%
Financial Services
6.6%
Banking
5.7%
Other
49.2%
Alternatives
Discussions
Cloudera Data Engineering has no discussions with answers
IBM watsonx.ai has no discussions with answers
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