BigQuery vs Oracle Autonomous Data Warehouse comparison

Google and Oracle are both solutions in the Cloud Data Warehouse category. Google is ranked #3 with an average rating of 8.7, while Oracle is ranked #13 with an average rating of 6.5. Google holds a 7.9% mindshare in CDW, compared to Oracle’s 4.3% mindshare. Additionally, 100% of Google users are willing to recommend the solution, compared to 95% of Oracle users who would recommend it.

BigQuery

Read 44 BigQuery reviews

5,305 Views
4,403 Comparison Views

100% willing to recommend

Oracle Autonomous Data Ware...

Read 19 Oracle Autonomous Data Warehouse reviews

3,193 Views
2,331 Comparison Views

95% willing to recommend

BigQuery

Oracle Autonomous Data Ware...

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 18, 2024

Oracle Autonomous Data Warehouse and BigQuery are competitors in the data warehousing industry. BigQuery seems to have the upper hand due to its extensive analytics capabilities and straightforward usage despite its complex pricing model, while Oracle excels in support and integration capabilities.

Features: Oracle Autonomous Data Warehouse offers automated tuning, easy scalability, and comprehensive data management. Meanwhile, BigQuery stands out with real-time analytics, seamless integration with external tools, and high scalability.

Room for Improvement: Oracle could improve in areas like pricing complexity, integration with non-Oracle environments, and enhancing self-service features for non-technical users. BigQuery might need enhancements in user interface intuitiveness, simplifying complex query management, and offering more in-depth customer support for new users.

Ease of Deployment and Customer Service: Oracle provides a streamlined deployment experience and strong customer support, ensuring quick setup and integration. BigQuery offers a flexible serverless model, beneficial for cloud-native environments, though it may pose challenges for users unfamiliar with Google Cloud.

Pricing and ROI: Oracle has a complex cost setup but promises solid ROI due to its extensive features and support. BigQuery's pricing appears straightforward but gets complex with higher data usage. However, it offers high ROI through its powerful analytics capabilities and scalable cost model, catering to different organizational needs.

To learn more, read our detailed BigQuery vs. Oracle Autonomous Data Warehouse Report (Updated: June 2026).

Buyer's Guide

BigQuery vs. Oracle Autonomous Data Warehouse

June 2026

Download the complete report

Helped 908,834 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

BigQuery

Ranking in Cloud Data Warehouse

3rd

Average Rating

8.2

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Oracle Autonomous Data Ware...

Ranking in Cloud Data Warehouse

13th

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2026, in the Cloud Data Warehouse category, the mindshare of BigQuery is 7.9%, up from 6.9% compared to the previous year. The mindshare of Oracle Autonomous Data Warehouse is 4.3%, down from 4.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
BigQuery	7.9%
Oracle Autonomous Data Warehouse	4.3%
Other	87.8%

Cloud Data Warehouse

Featured Reviews

Mikah Sellers

Principal at Sgt Suds

Has supported detailed policy and education analysis with low-code data exploration

I do not use AWS as my main cloud provider within the company and am not currently using it with AWS. My company did not buy it through the AWS Marketplace as I'm the founder of the company. I do not use BigQuery's integration with Google Analytics for custom behavior analysis. I have not utilized the geospatial analysis capabilities with BigQuery as I'm doing mostly human capital work, so geospatial wouldn't make sense. I would rate their service on a scale of one to 10 for Google as ten. I would rate BigQuery as nine out of 10. My experience with the pricing for BigQuery was through a grant from Google. Their pricing is fairly priced and pretty comparable to Microsoft's offering. I haven't had direct experience, but I did look at the pricing of the Microsoft offering and it's pretty similar. I do not deal with Google AI tools such as Vertex.

Read full review

Kwajah Mohiuddin

Global Head of Architecture at a financial services firm with 1,001-5,000 employees

Provides self-repair features, but the setup is complex

We use the product for online applications. We use it in the financial industry The product has self-repair features. The tool tunes itself. It separates compute from storage. We can scale storage and compute separately. The setup is complex. Oracle is a complex tool. I have been using Oracle…

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"It's straightforward to set up."

"The solution's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements."

"We like the machine learning features and the high-performance database engine."

"This solution is highly scalable."

"As a cloud solution, it's easy to set up."

"It has a well-structured suite of complimentary tools for data integration and so forth."

"I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."

"The scalability is excellent, as it can handle large datasets and scale up pretty easily as the data volume grows while remaining very reliable with no bugs, glitches, crashes, or freezes."

More BigQuery pros

"The solution integrates well with Power BI."

"It is a very stable tool...It is an extremely scalable tool."

"I like the fact that the solution is self-patching, that it's running machine-learning generally across its logs all the time in order to identify any issues and to self-repair."

"The analytics have been very good. We've found them to be quite useful."

"The product has self-repair features."

"The performance and scalability are awesome."

"It is a stable and scalable solution."

"I loved the simplicity of loading the data and simply relying on the self-tuning capabilities of ADW."

More Oracle Autonomous Data Warehouse pros

Cons

"I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up."

"BigQuery can be very expensive if not used properly. Introducing AI tools that would allow you to optimize the data extraction process is an area of serious improvement."

"There is a limitation when copying data directly from BigQuery; it only supports up to ten MB when copying data to the clipboard."

"The price is a bit high but the technology is worth it."

"We'd like to see more local data residency."

"The main challenges are in the areas of performance and cost optimizations."

"When it comes to queries or the code being executed in the data warehouse, the management of this code, like integration with the GitHub repository or the GitLab repository, is kind of complicated, and it's not so direct."

"It can be slower and more problematic compared to other platforms such as Snowflake."

More BigQuery cons

"My main suggestion for Oracle is the configuration and key values that come for JSON files. When we create a table, especially if you see in our RedShift or some other stuff, if I create a table on top of a JSON file with multiple array columns or superset columns, those column values create some difficulty in Oracle."

"Optimization should be better."

"The setup is complex."

"It is good as data warehouses go, but it is not that good for really big data."

"Ease of interconnectivity could be improved by which I mean setting up the VPN access and the like from on-premises to cloud."

"Ease of connectivity could be improved."

"It is very important the integration with other platforms be made to be as easy as it is with an on-premises deployment."

"They should make the solution more user-friendly."

More Oracle Autonomous Data Warehouse cons

Pricing and Cost Advice

"BigQuery is inexpensive."

"The product’s pricing could be more flexible for end users."

"One terabyte of data costs $20 to $22 per month for storage on BigQuery and $25 on Snowflake. Snowflake is costlier for one terabyte, but BigQuery charges based on how much data is inserted into the tables. BigQuery charges you based on the amount of data that you handle and not the time in which you handle it. This is why the pricing models are different and it becomes a key consideration in the decision of which platform to use."

"The solution's pricing is cheaper compared to other solutions."

"The pricing is adaptable, ensuring that organizations can tailor their usage and costs based on their specific requirements and configurations within the Google Cloud Platform."

"We are above the free threshold, so we are paying around 40 euros per month for BigQuery."

"The solution is pretty affordable and quite cheap in comparison to PDP or Cloudera."

"BigQuery pricing can increase quickly. It's a high-priced solution."

More BigQuery pricing and cost advice

"The cost is perfect with Oracle Universal credit."

"The solution's cost is reasonable."

"On a scale from one to ten, where one is a low price and ten is a high price, I rate the pricing an eight."

"In terms of architecture and pricing structure, I feel it is a little bit costly compared to Azure. It's fine compared to RedShift, but compared to Azure, it's a bit pricey when you calculate for one TB storage plus around five hours of reporting with the frequency of 1TB data. The cost adds up, making Oracle a bit expensive."

"Cloud solutions are cheaper, but in the long run, they may not be much cheaper. They certainly have a lower initial cost. The licensing is yearly, and it is based on the size of the hardware and the number of users."

"You pay as you go, and you don't pay for services that you don't use."

"The licensing cost of the product can vary since you can integrate it very easily with other products or other cloud products...You pay as you use it, so it is not yearly or monthly payments to be made toward Oracle."

"The solution is expensive."

More Oracle Autonomous Data Warehouse pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Cloud Data Warehouse solutions are best for your needs.

See recommendations

908,834 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

16%

Outsourcing Company

11%

Media Company

10%

Manufacturing Company

Financial Services Firm

10%

Manufacturing Company

10%

Media Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	15
Midsize Enterprise	10
Large Enterprise	19

By reviewers
Company Size	Count
Small Business	7
Midsize Enterprise	1
Large Enterprise	11

Questions from the Community

What is your experience regarding pricing and costs for BigQuery?

I believe the cost of BigQuery is competitive versus the alternatives in the market, but it can become expensive if the tool is not used properly. It is on a per-consumption basis, the billing, so ...

See all answers

What needs improvement with BigQuery?

The queries that BigQuery provides are not optimized at a greater level. For simple tasks it is good, but if I want to write complex analyzing queries, it doesn't give the queries up to the mark, s...

See all answers

What is your primary use case for BigQuery?

My main use case for BigQuery is the analysis of our data, creating dashboards from large datasets, and querying the databases that we have, including both structured and unstructured data imported...

See all answers

What is your experience regarding pricing and costs for Oracle Autonomous Data Warehouse?

We pay approximately $70,000 per month. The cost includes maintenance and support.

See all answers

What needs improvement with Oracle Autonomous Data Warehouse?

Optimization should be better. The SQLs are sometimes very slow. I also noticed that Java is not supported, which is not ideal.

See all answers

What is your primary use case for Oracle Autonomous Data Warehouse?

We are using Oracle Autonomous Data Warehouse for analytics in my company.

See all answers

Comparisons

Snowflake vs BigQuery

Compared 10% of the time

Teradata vs BigQuery

Compared 9% of the time

Snowflake Analytics vs BigQuery

Compared 7% of the time

Databricks vs BigQuery

Compared 6% of the time

IBM Db2 Warehouse vs BigQuery

Compared 5% of the time

More BigQuery Competitors

Snowflake vs Oracle Autonomous Data Warehouse

Compared 16% of the time

Microsoft Azure Synapse Analytics vs Oracle Autonomous Data Warehouse

Compared 9% of the time

Databricks vs Oracle Autonomous Data Warehouse

Compared 9% of the time

Amazon Redshift vs Oracle Autonomous Data Warehouse

Compared 6% of the time

VMware Tanzu Data Solutions vs Oracle Autonomous Data Warehouse

Compared 5% of the time

More Oracle Autonomous Data Warehouse Competitors

Product Reports

Buyer's Guide

BigQuery

August 2026

Download BigQuery product report

Buyer's Guide

Oracle Autonomous Data Warehouse

July 2026

Download Oracle Autonomous Data Warehouse product report

Also Known As

No data available

Overview

BigQuery is a powerful cloud-based data warehouse offering advanced SQL querying, seamless Google integration, and scalable handling of large datasets. Its serverless architecture and built-in AI capabilities facilitate efficient data processing and insights extraction.

BigQuery provides an efficient data analysis platform with low-latency performance and cost-effective on-demand pricing. Leveraging Google's cloud infrastructure for data storage, it offers robust security and high availability. While it excels in SQL support and caching features, it can improve on user accessibility, integration with diverse tools, and machine learning feature expansion. Making it more accessible for smaller entities through improved cost management and local data compliance is essential. Enhancements in query speed and intuitive interfaces can further optimize performance.

What features are offered by BigQuery?

User-friendly interface: Simplifies data interaction, making complex processes more manageable.
Serverless architecture: Eliminates the need for infrastructure management, improving scalability and efficiency.
Advanced SQL querying: Enables complex data retrieval and manipulation with ease.
Machine learning integration: Facilitates AI-driven insights directly from the platform.
Cost-effective pricing: On-demand pricing helps manage expenses by paying only for used resources.
Seamless Google integration: Works well with familiar Google services for a cohesive experience.
High availability and robust security: Ensures data is secure and accessible when needed.

What benefits should be considered when evaluating BigQuery?

Efficient data processing: Quickly handles large datasets, supporting fast data-driven decision-making.
Scalable infrastructure: Grows capacity alongside business needs without manual infrastructure adjustments.
Enhanced data analysis: SQL support and caching features improve analysis precision and speed.
AI and machine learning capabilities: Enable advanced data insights and predictive analytics.
Integration with existing tools: Ensures smooth workflow transitions with services such as Google Analytics and Tableau.

In industries like healthcare, finance, and marketing, BigQuery is extensively used for data storage, generating reports, and supporting ETL processes. Educational institutions leverage it for analytics, aligning seamlessly with Google Cloud for serverless infrastructure efficiencies.

Google

Oracle Autonomous Data Warehouse is a cloud-based service offering advanced data management capabilities, including automated administration and high performance for analytics tasks. It is ideal for enterprises prioritizing security, easy maintenance, and dynamic scale.

Oracle Autonomous Data Warehouse stands out by offering self-managing capabilities that minimize administrative overhead, allowing organizations to focus on data-driven decision-making. With features such as transparent data encryption, seamless cloud integration, and automated query tuning, it ensures secure and efficient data operations. Its architecture separates compute and storage, enhancing scalability and performance. Despite its setup complexity and higher cost than some competitors, it offers deep integration with Oracle Database, ensuring reliable performance and fast data exchange.

What features define Oracle Autonomous Data Warehouse?

Self-Patching: Automatically updates to ensure security and performance.
Auto-Scaling: Dynamically adjusts resources to meet workload demands.
Query Tuning: Optimizes queries for better performance without user intervention.
Seamless Cloud Integration: Easily connects with Oracle and other cloud applications.
Transparent Data Encryption: Provides secure data handling to meet compliance needs.
Smart Scan: Efficiently processes queries to reduce data retrieval times.

What benefits can be expected from Oracle Autonomous Data Warehouse?

Cost-Effectiveness: Reduces operational costs through automation and serverless operations.
High Security: Ensures data protection with encryption and isolated resources.
Performance Enhancement: High-speed data processing and query response times.
Easy Maintenance: Minimal administrative tasks due to automation.
Scalability: Effortlessly scales resources to accommodate growth.

Oracle Autonomous Data Warehouse is widely used in finance, banking, transport, and manufacturing, supporting data analytics in financial systems, procurement, and student management. It facilitates large-scale transaction processing, offering centralized reporting and dynamic resource allocation, which is crucial for enhancing performance across industries.

Oracle

Sample Customers

Information Not Available

Hertz, TaylorMade Golf, Outront Media, Kingold, FSmart, Drop-Tank

Buyer's Guide

BigQuery vs. Oracle Autonomous Data Warehouse

June 2026

Free Report: BigQuery vs. Oracle Autonomous Data Warehouse

Find out what your peers are saying about BigQuery vs. Oracle Autonomous Data Warehouse and other solutions. Updated: June 2026.

DOWNLOAD NOW

908,834 professionals have used our research since 2012.

See our BigQuery vs. Oracle Autonomous Data Warehouse report.

See our list of best Cloud Data Warehouse vendors.

We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.