Compare AWS Data Pipeline and Google Cloud BigQuery 
At a Glance
Market Segments
Mid-Market (61.9% of reviews)
Enterprise (38.0% of reviews)
Entry-Level Pricing
No pricing available
Free
AWS Data Pipeline
Star Rating
(24)4.1 out of 5
Market Segments
Mid-Market (61.9% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about AWS Data PipelineGoogle Cloud BigQuery
Star Rating
(1,225)4.5 out of 5
Market Segments
Enterprise (38.0% of reviews)
Pros & Cons
Entry-Level Pricing
Free
Browse all 5 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Google Cloud BigQuery excels in user satisfaction, boasting a significantly higher overall score compared to AWS Data Pipeline. Users appreciate its serverless architecture and the ability to focus on SQL without worrying about infrastructure, making data analysis feel smooth and efficient.
- According to verified reviews, Google Cloud BigQuery's implementation process is praised for its intuitive onboarding and ease of setup, with users highlighting the pay-as-you-go model that simplifies operations and allows for experimentation with larger datasets. In contrast, AWS Data Pipeline has received mixed feedback regarding its setup, with some users finding it less straightforward.
- Users say that Google Cloud BigQuery's performance is remarkable, capable of handling billions of queries efficiently. This capability is a significant advantage for enterprises needing to process large volumes of data quickly, while AWS Data Pipeline, although effective for managing complex workflows, does not match the same level of performance in high-demand scenarios.
- Reviewers mention that Google Cloud BigQuery's support for BigQuery ML and AI tooling enhances its functionality, making it a robust choice for data scientists and analysts. On the other hand, while AWS Data Pipeline integrates well with other AWS services, some users feel it lacks the advanced analytical capabilities that BigQuery offers.
- G2 reviewers highlight that Google Cloud BigQuery's quality of support is generally rated higher, with users appreciating the responsiveness and helpfulness of the support team. In comparison, AWS Data Pipeline's support is seen as adequate but not as strong, with some users expressing a desire for more comprehensive assistance.
- According to recent user feedback, Google Cloud BigQuery's ease of administration is a strong point, allowing users to manage their data environments with minimal hassle. Conversely, AWS Data Pipeline users report challenges in administration, which can complicate day-to-day operations and affect overall productivity.
Pricing
Entry-Level Pricing
AWS Data Pipeline
No pricing availableGoogle 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
Free Trial
AWS Data Pipeline
No trial information availableGoogle Cloud BigQuery
Free Trial is available
Ratings
Meets Requirements
9.1
18
8.8
627
Ease of Use
8.6
18
8.7
643
Ease of Setup
6.9
7
8.7
417
Ease of Admin
6.9
7
8.5
227
Quality of Support
8.3
17
8.3
573
Has the product been a good partner in doing business?
7.4
7
8.6
221
Product Direction (% positive)
6.1
18
9.3
615
Features by Category
Management
Not enough data
8.6
121
Not enough data
8.3
119
Functionality
Not enough data
9.0
119
Not enough data
9.1
123
Not enough data
9.0
123
Not enough data
8.9
122
Not enough data
9.3
122
Data Management
Not enough data
8.9
225
|
Verified
Not enough data
8.3
210
|
Verified
Not enough data
8.9
215
|
Verified
Not enough data
8.6
219
|
Verified
Not enough data
8.3
204
|
Verified
Not enough data
8.6
201
|
Verified
Integration
Not enough data
8.5
202
|
Verified
Not enough data
8.6
214
|
Verified
Not enough data
8.6
201
|
Verified
Deployment
Not enough data
7.7
186
|
Verified
Not enough data
9.1
223
|
Verified
Performance
Not enough data
9.1
233
|
Verified
Security
Not enough data
8.7
208
|
Verified
Not enough data
9.2
214
|
Verified
Generative AI
Not enough data
7.6
127
Not enough data
7.8
124
Storage
Not enough data
9.1
70
Not enough data
8.8
70
Availability
Not enough data
8.4
62
Not enough data
8.5
66
Not enough data
8.6
68
Performance
Not enough data
8.5
65
Security
Not enough data
8.7
70
Not enough data
8.8
69
Not enough data
8.4
69
Not enough data
8.7
65
Support
Not enough data
8.7
66
Not enough data
8.9
66
Centralized computation
Not enough data
9.1
27
Localized computation
Not enough data
8.5
28
Statistical Tool
Not enough data
8.7
78
Not enough data
9.0
78
Not enough data
8.8
79
Data Analysis
Not enough data
9.3
78
Not enough data
9.1
79
Decision Making
Not enough data
8.8
79
Not enough data
8.7
77
Not enough data
8.7
79
Not enough data
8.7
77
Generative AI
Not enough data
7.5
50
Not enough data
7.8
50
Marketing Operations
Not enough data
8.8
65
Not enough data
9.2
64
Not enough data
8.9
64
Not enough data
9.4
65
Not enough data
8.8
63
Not enough data
8.4
62
Campaign Activity
Not enough data
9.1
67
Not enough data
9.5
67
Not enough data
8.7
64
Not enough data
9.2
61
Not enough data
8.9
62
Not enough data
9.0
64
Agentic AI - Marketing Analytics
Not enough data
8.7
15
Not enough data
9.6
15
Not enough data
8.2
15
Database
Not enough data
8.8
175
Not enough data
8.9
173
Not enough data
8.7
167
Integrations
Not enough data
8.4
136
Not enough data
8.5
133
Platform
Not enough data
8.9
151
Not enough data
8.9
165
Not enough data
8.5
133
Processing
Not enough data
9.2
168
Not enough data
9.1
161
Data Transformation
Not enough data
8.8
283
Not enough data
9.3
293
Connectivity
Not enough data
8.1
251
Not enough data
8.2
249
Not enough data
8.7
278
Not enough data
8.7
269
Operations
Not enough data
8.5
279
Not enough data
8.7
277
Not enough data
8.3
255
Not enough data
8.4
262
Not enough data
8.5
254
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
AWS Data Pipeline and Google Cloud BigQuery are categorized as ETL Tools
Unique Categories
AWS Data Pipeline has no unique categories
Google Cloud BigQuery is categorized as Columnar Databases, Big Data Integration Platforms, Big Data Processing and Distribution, Big Data Analytics, Marketing Analytics, Predictive Analytics, Data Warehouse, and Data Clean Room
Reviews
Reviewers' Company Size
AWS Data Pipeline
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Google Cloud BigQuery
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
Computer Software
23.8%
Telecommunications
14.3%
Information Technology and Services
9.5%
Financial Services
9.5%
Sports
4.8%
Other
38.1%
Information Technology and Services
16.1%
Computer Software
13.5%
Retail
7.0%
Financial Services
6.5%
Marketing and Advertising
5.2%
Other
51.8%
Alternatives
Discussions
AWS Data Pipeline has no discussions with answers
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
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