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Compare Google Cloud BigQuery and IBM Netezza Performance Server 
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At a Glance
Market Segments
Enterprise (38.0% of reviews)
Enterprise (59.7% of reviews)
Entry-Level Pricing
Free
No pricing available
Google Cloud BigQuery
Star Rating
(1,202)4.5 out of 5
Market Segments
Enterprise (38.0% of reviews)
Entry-Level Pricing
Free
Free Trial is available
Browse all 5 pricing plansIBM Netezza Performance Server
Star Rating
(84)4.1 out of 5
Market Segments
Enterprise (59.7% of reviews)
Entry-Level Pricing
No pricing available
Learn more about IBM Netezza Performance ServerSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that IBM Netezza Performance Server excels in built-in data analytics with a score of 9.1, allowing for robust data insights directly within the platform. In contrast, Google Cloud BigQuery, while strong, scores slightly lower at 8.6 in this area, indicating that Netezza may offer a more integrated experience for analytics.
- Reviewers mention that Google Cloud BigQuery shines in data integration, achieving a score of 9.0, which is higher than IBM Netezza's 8.6. This suggests that BigQuery may provide a more seamless experience when connecting with various data sources, making it a preferred choice for users needing extensive data connectivity.
- G2 users highlight that IBM Netezza Performance Server has a superior data compression feature, scoring 8.8 compared to BigQuery's 8.3. This indicates that Netezza may be more efficient in managing storage costs and optimizing data storage, which is crucial for enterprises dealing with large datasets.
- Users on G2 report that Google Cloud BigQuery offers better scalability with a score of 9.1, compared to Netezza's 8.5. This suggests that BigQuery is more adept at handling increasing workloads and can better accommodate growing data needs, making it a strong contender for businesses anticipating rapid growth.
- Reviewers mention that IBM Netezza Performance Server has a more favorable user experience in terms of ease of setup, scoring 8.3, while BigQuery scores 8.6. This slight edge for BigQuery indicates that users may find it easier to get started with the Google platform, which can be a significant factor for teams looking to implement solutions quickly.
- Users say that both platforms provide solid data security, with IBM Netezza scoring 9.0 and Google Cloud BigQuery at 9.1. However, the slight edge for BigQuery suggests that it may have more advanced security features, which is a critical consideration for organizations handling sensitive data.
Pricing
Entry-Level Pricing
Google Cloud BigQuery
Free
Free
New customers get $300 in free Google Cloud credits to spend on BigQuery with free trial sign-up.
- 10 GB storage
- Up to 1 TB queries per month
IBM Netezza Performance Server
No pricing availableFree Trial
Google Cloud BigQuery
Free Trial is available
IBM Netezza Performance Server
No trial information availableRatings
Meets Requirements
8.8
612
8.4
55
Ease of Use
8.7
626
8.8
53
Ease of Setup
8.8
401
8.3
23
Ease of Admin
8.5
224
8.2
24
Quality of Support
8.4
564
8.3
48
Has the product been a good partner in doing business?
8.7
217
7.9
23
Product Direction (% positive)
9.2
600
6.7
50
Features by Category
Administration
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Federation
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Transformation
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
8.6
123
Not enough data
8.3
121
Not enough data
Functionality
9.0
121
Not enough data
9.1
124
Not enough data
9.0
124
Not enough data
8.9
123
Not enough data
9.4
124
Not enough data
Data Management
8.9
223
|
Verified
8.6
13
|
Verified
8.3
214
|
Verified
8.8
12
|
Verified
8.9
219
|
Verified
8.8
11
|
Verified
8.6
215
|
Verified
9.1
9
|
Verified
8.3
204
|
Verified
8.0
9
|
Verified
8.7
205
|
Verified
8.5
11
|
Verified
Integration
8.6
204
|
Verified
7.5
8
|
Verified
8.6
216
|
Verified
8.8
11
|
Verified
8.6
204
|
Verified
9.2
10
|
Verified
Deployment
7.8
190
|
Verified
8.7
9
|
Verified
9.1
215
|
Verified
8.2
11
|
Verified
Performance
9.2
220
|
Verified
8.5
9
|
Verified
Security
8.7
211
|
Verified
8.9
11
|
Verified
9.2
217
|
Verified
9.0
10
|
Verified
Generative AI
7.7
132
Not enough data
7.9
129
Not enough data
Storage
9.1
70
Not enough data
8.8
70
Not enough data
Availability
8.4
62
Not enough data
8.5
66
Not enough data
8.6
68
Not enough data
Performance
8.5
65
Not enough data
Security
8.7
70
Not enough data
8.8
69
Not enough data
8.4
69
Not enough data
8.7
65
Not enough data
Support
8.7
66
Not enough data
8.9
66
Not enough data
Centralized computation
9.1
27
Not enough data
Localized computation
8.5
28
Not enough data
Statistical Tool
8.7
77
Not enough data
9.0
78
Not enough data
8.8
78
Not enough data
Data Analysis
9.3
77
Not enough data
9.1
78
Not enough data
Decision Making
8.8
79
Not enough data
8.7
77
Not enough data
8.7
79
Not enough data
8.7
77
Not enough data
Generative AI
7.6
50
Not enough data
7.9
50
Not enough data
Marketing Operations
8.8
66
Not enough data
9.2
64
Not enough data
8.9
65
Not enough data
9.4
66
Not enough data
8.8
63
Not enough data
8.5
63
Not enough data
Campaign Activity
9.1
68
Not enough data
9.5
67
Not enough data
8.7
64
Not enough data
9.2
62
Not enough data
8.9
63
Not enough data
9.1
65
Not enough data
Agentic AI - Marketing Analytics
8.8
16
Not enough data
9.6
16
Not enough data
8.3
16
Not enough data
Database
8.8
178
Not enough data
8.9
176
Not enough data
8.8
170
Not enough data
Integrations
8.4
139
Not enough data
8.6
136
Not enough data
Platform
8.9
153
Not enough data
8.9
168
Not enough data
8.6
136
Not enough data
Processing
9.3
170
Not enough data
9.1
164
Not enough data
Data Transformation
8.8
288
Not enough data
9.3
295
Not enough data
Connectivity
8.1
256
Not enough data
8.2
254
Not enough data
8.7
283
Not enough data
8.8
274
Not enough data
Operations
8.5
283
Not enough data
8.7
282
Not enough data
8.3
260
Not enough data
8.4
267
Not enough data
8.5
259
Not enough data
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
Google Cloud BigQuery and IBM Netezza Performance Server are categorized as Data Warehouse
Unique Categories
Google Cloud BigQuery is categorized as Columnar Databases, Big Data Integration Platforms, ETL Tools, Big Data Processing and Distribution, Big Data Analytics, Marketing Analytics, Predictive Analytics, and Data Clean Room
IBM Netezza Performance Server is categorized as Data Virtualization
Reviews
Reviewers' Company Size
Google Cloud BigQuery
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM Netezza Performance Server
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
16.2%
Computer Software
13.6%
Retail
7.1%
Financial Services
6.5%
Marketing and Advertising
5.1%
Other
51.6%
Information Technology and Services
19.4%
Insurance
11.9%
Banking
11.9%
Retail
7.5%
Financial Services
6.0%
Other
43.3%
Alternatives
Discussions
Is Big Query free?
3 Comments
BG
noRead more
Is BigQuery part of Google Cloud Platform?
2 Comments
AY
Yes, Bigquery is a GCP product and a serveless data wharehouse.Read more
How BQ legacy SQl is different form the standard SQL?
1 Comment
OD
Legacy SQL is a non-standard SQL only used by BigQuery. Standard SQL is compliant with the SQL 2011.
Google recommends using Standard SQL and looking at the...Read more
IBM Netezza Performance Server has no discussions with answers
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