AWS Lake Formation vs BigQuery comparison

Amazon Web Services (AWS) and Google are both solutions in the Cloud Data Warehouse category. Amazon Web Services (AWS) is ranked #7 with an average rating of 8.1, while Google is ranked #4 with an average rating of 8.5. Amazon Web Services (AWS) holds a 5.0% mindshare in CDW, compared to Google’s 7.7% mindshare. Additionally, 85% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Google users who would recommend it.

AWS Lake Formation

Read 21 AWS Lake Formation reviews

1,416 Views
1,317 Comparison Views

85% willing to recommend

BigQuery

Read 42 BigQuery reviews

2,882 Views
2,392 Comparison Views

100% willing to recommend

AWS Lake Formation

BigQuery

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Executive SummaryUpdated on Dec 18, 2024

AWS Lake Formation and BigQuery are competing in the cloud services market, particularly in data management and analytics. BigQuery stands out with its superior feature set, offering robust analytics capabilities, while AWS Lake Formation emphasizes strong data governance and security.

Features: AWS Lake Formation focuses on data lake creation, security, governance, and simplifies access rights management. BigQuery offers high-speed SQL analytics, real-time querying, and machine learning integration. The distinction is in AWS's strong governance compared to BigQuery's advanced analytical capabilities.

Room for Improvement: AWS Lake Formation could enhance real-time analytics and improve its machine learning features. It might also benefit from simplified ETL job setups. BigQuery could lower costs for extensive use cases, refine data governance tools, and offer better integration with non-Google services.

Ease of Deployment and Customer Service: AWS Lake Formation is well-integrated with the AWS ecosystem, offering seamless service integration and extensive support. BigQuery’s serverless architecture allows for quick deployment with minimal configuration. While AWS offers personalized customer service, BigQuery provides extensive documentation and online resources.

Pricing and ROI: AWS Lake Formation is cost-effective with flexible pricing models based on storage needs, potentially offering lower initial setup costs. BigQuery uses pay-as-you-go billing for queries, which might be costlier in high-use scenarios but efficient for variable workloads. AWS provides ROI through efficient data management, while BigQuery offers ROI via enhanced analytical capabilities.

To learn more, read our detailed AWS Lake Formation vs. BigQuery Report (Updated: December 2025).

Buyer's Guide

AWS Lake Formation vs. BigQuery

December 2025

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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

AWS Lake Formation

Ranking in Cloud Data Warehouse

7th

Average Rating

8.0

Reviews Sentiment

5.7

Number of Reviews

Ranking in other categories

No ranking in other categories

BigQuery

Ranking in Cloud Data Warehouse

4th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 5.0%, up from 5.0% compared to the previous year. The mindshare of BigQuery is 7.7%, up from 7.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Market Share Distribution
Product	Market Share (%)
BigQuery	7.7%
AWS Lake Formation	5.0%
Other	87.3%

Cloud Data Warehouse

Featured Reviews

Ciro Baldim Guerra

Sr Analytics Engineer at Itau Unibanco S.A.

Has improved data governance by enabling clear ownership and structured access across teams

In my company, Itaú, we don't utilize all AWS offerings due to rigorous security measures. We operate approximately six to eight months behind other available services. I'm uncertain if gaps exist because of this limitation, though the system functions effectively for us. AWS Lake Formation offers column-level access control for databases, but we haven't implemented this feature either because it hasn't been approved by our compliance, governance, or security areas. In our current setup, everyone from my business unit uses the same consumer account. When access is requested for a table, everyone using that business unit account receives access. This could present a security concern, though it benefits new team members who automatically receive all necessary access permissions. However, I struggle to identify specific improvements needed in AWS Lake Formation.

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Luís Silva

Chief Technical Lead at a consultancy with 201-500 employees

Handles large data sets efficiently and offers flexible data management capabilities

The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data. It is kind of difficult to explain, but structured data and the ability to handle large data sets are key features. The data integration capabilities in BigQuery were, in fact, an issue at the beginning. There are two types of integrations. As long as integration is within Google, it is pretty simple. When you start to try to connect external clients to that data, it becomes more complex. It is not related to BigQuery, it is related to Google security model, which is not easy to manage. I would not call it an integration issue of BigQuery, I would call it an integration issue of Google security model.

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

"We use this to reduce latency from minutes to seconds, as we aim for real-time visibility into patient healthcare monitoring."

"AWS Lake Formation significantly improves the structure of the data mesh, making it superior to previous structures we used."

"In the shortest form, what I appreciated about AWS Lake Formation was that the schema definition and data cataloging were quite good."

"The solution has many features that are applicable to events such as audits."

"The solution is quite good at handling analytics. It's done a good job at helping us centralize them."

"AWS Lake Formation has several valuable features that enhance data management, and one particularly beneficial aspect is how it facilitates better collaboration within data teams."

"We use AWS Lake Formation typically for the data warehouse."

"We have observed measurable benefits in terms of cost savings, time savings, resource savings, and efficiency improvements in our workflows."

More AWS Lake Formation pros

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

"BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases."

"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."

"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."

"BigQuery is a powerful tool for managing and analyzing large datasets. The versatility of BigQuery extends to its compatibility with external data visualization tools like Power BI and Tableau. This means you not only get query results but can also seamlessly integrate and visualize your data for better insights."

"When integrating their system into the cloud-based solutions, we were able to increase their efficiency and overall productivity twice compared with their on-premises option."

"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."

"BigQuery's querying capabilities are very optimized for large datasets."

More BigQuery pros

Cons

"I haven't seen any measurable benefits from using AWS Lake Formation, such as time saving, resource saving, or efficiency improvements."

"Information about the pricing, cost, and setup cost of the AWS solutions would be beneficial."

"I think AWS Lake Formation could improve by enforcing the least privilege by design, moving from ad hoc grants to role-based access controls."

"Rather than creating an additional hundred tools, optimizing a tool to have a centralized location to do governance would be beneficial."

"For the end-users, it's not as user-friendly as it could be."

"Athena can be a bit clunky when writing queries, indicating a potential enhancement point for easier user interaction with query tools such as DataGrip using provided driver JARs."

"Despite following official documentation, configuration problems persisted, requiring weeks of support from multiple AWS engineers to resolve."

"It falls short when it comes to more granular access control, such as cell-level or row-level entitlements which is a significant drawback for organizations that require precise control over who can access specific rows of data."

More AWS Lake Formation cons

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

"When I open many of the Google Cloud products, I am in an environment that I do not feel familiar with; it is a little overwhelming."

"Some of the queries are complex and difficult to understand."

"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."

"Sometimes, support specialists might not have enough experience or business understanding, which can be an issue."

"It would be beneficial if BigQuery could be made more affordable."

"There are some limitations in the query latency compared to what it was three years ago."

More BigQuery cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

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

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

"BigQuery is inexpensive."

"The price could be better. Usually, you need to buy the license for a year. Whenever you want more, you can subscribe to it, and you can use it. Otherwise, you can terminate the license. You can use it daily or monthly, and we use it based on a project's requirements."

"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."

"Price-wise, I think that is very reasonable."

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

"The tool has competitive pricing."

More BigQuery pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Computer Software Company

Manufacturing Company

Retailer

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

13%

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	15

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	9
Large Enterprise	20

Questions from the Community

What is your experience regarding pricing and costs for AWS Lake Formation?

I don't understand much about the pricing of AWS Lake Formation, but I know how to search for the cost of Glue jobs, and I use the calculator in Amazon. I use a tool to preview the cost based on th...

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What needs improvement with AWS Lake Formation?

Regarding areas of AWS Lake Formation that could be improved or enhanced, I prefer not to answer, mainly because I do not believe that I would be the most valuable person to ask, as I have not used...

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What is your primary use case for AWS Lake Formation?

My usual use cases for AWS Lake Formation involved securing and governing the data resources that we configured in AWS, but we did not use the analytics or machine learning capabilities specificall...

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What do you like most about BigQuery?

The initial setup process is easy.

See all answers

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?

There are areas that could be improved with BigQuery, such as more bolt-on capabilities and the ability to use more bolt-ons for APIs. Having more of a library of connectors would be really benefic...

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Comparisons

Snowflake vs AWS Lake Formation

Compared 18% of the time

Azure Data Factory vs AWS Lake Formation

Compared 18% of the time

Amazon Redshift vs AWS Lake Formation

Compared 15% of the time

Databricks vs AWS Lake Formation

Compared 15% of the time

Amazon EMR vs AWS Lake Formation

Compared 8% of the time

More AWS Lake Formation Competitors

Oracle Autonomous Data Warehouse vs BigQuery

Compared 20% of the time

Teradata vs BigQuery

Compared 11% of the time

Snowflake vs BigQuery

Compared 11% of the time

OpenText Analytics Database (Vertica) vs BigQuery

Compared 8% of the time

Azure Data Factory vs BigQuery

Compared 5% of the time

More BigQuery Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

January 2026

Download AWS Lake Formation product report

Buyer's Guide

BigQuery

January 2026

Download BigQuery product report

Overview

AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis.

Amazon Web Services (AWS)

BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

Google

Sample Customers

bp, Cerner, Expedia, Finra, HESS, intuit, Kellog's, Philips, TIME, workday

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Buyer's Guide

AWS Lake Formation vs. BigQuery

December 2025

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