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 #8 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 4.7% mindshare in CDW, compared to Google’s 8.0% 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

1,610 Views
1,385 Comparison Views

85% willing to recommend

BigQuery

Read 43 BigQuery reviews

3,228 Views
2,615 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: March 2026).

Buyer's Guide

AWS Lake Formation vs. BigQuery

March 2026

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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

8th

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

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of March 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 4.7%, down from 5.0% compared to the previous year. The mindshare of BigQuery is 8.0%, up from 7.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
BigQuery	8.0%
AWS Lake Formation	4.7%
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.

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Quotes from Members

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

Pros

"AWS Lake Formation lets you see all your data and tables on one screen."

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

"A favorite feature of AWS Lake Formation is that it provides us with visibility into who has access to a particular table or database in Glue."

"The most valuable features of AWS Lake Formation were the access model itself, as it allows implementation of filters, Blueprints, and row-level and column-level security to mask data that shouldn't be accessed by certain entities, enabling granular control without exposing PII data."

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

"The most important advantage in using AWS Lake Formation is its ability to connect the data lake to the other technologies in AWS. This is what I advise my clients."

"The LF-Tag system with granular permissions was key to the project as a functionality of AWS Lake Formation."

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

More AWS Lake Formation pros

"Even non-coders can review the data in BigQuery."

"It's straightforward to set up."

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

"BigQuery excels at structuring data, performing predictions, and conducting insightful analyses and it leverages machine learning and artificial intelligence capabilities, powered by Google's Duarte AI."

"This solution is highly scalable."

"Its integration with other tools like Atlan through a Google Chrome extension is highly beneficial."

"The integrated data storage features are good."

"BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes."

More BigQuery pros

Cons

"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."

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

"The solution could make improvements around orchestration and doing some automation stuff on AWS front automation. It would be useful if we could use automation to build images and use hardened images which are CIS compliant."

"I would appreciate online support, which I don't have access to in my corporation at the bank, so that is important."

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

"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."

"You need to have data experience to use the product."

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

More AWS Lake Formation cons

"There is a good amount of documentation out there, but they're consistently making changes to the platform, and, like, their literature hasn't been updated on some plans."

"The initial setup could be improved making it easier to deploy."

"The solution hinges on Google patterns so continued improvement is important."

"The processing capability can be an area of improvement."

"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."

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

"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."

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

More BigQuery cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

"The pricing appears to be competitive for the intended usage scenarios we have in mind."

"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 platform is inexpensive."

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

"The pricing is good and there are no additional costs involved."

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

"The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."

"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."

More BigQuery pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

23%

Manufacturing Company

Retailer

Computer Software Company

Financial Services Firm

15%

Manufacturing Company

14%

Computer Software Company

10%

Media Company

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	13
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?

With what I have seen in BigQuery, I had some response times problems, but then it is an analytical database and not a transactional database, so it comes with eventual consistency. I cannot have e...

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Comparisons

Snowflake vs AWS Lake Formation

Compared 14% of the time

Azure Data Factory vs AWS Lake Formation

Compared 13% of the time

Databricks vs AWS Lake Formation

Compared 12% of the time

Amazon Redshift vs AWS Lake Formation

Compared 11% of the time

Amazon EMR vs AWS Lake Formation

Compared 6% of the time

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Oracle Autonomous Data Warehouse vs BigQuery

Compared 16% of the time

Snowflake vs BigQuery

Compared 10% of the time

Teradata vs BigQuery

Compared 10% of the time

OpenText Analytics Database (Vertica) vs BigQuery

Compared 8% of the time

Dremio vs BigQuery

Compared 4% of the time

More BigQuery Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

March 2026

Download AWS Lake Formation product report

Buyer's Guide

BigQuery

March 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

March 2026

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