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 4.9% mindshare in CDW, compared to Google’s 7.8% 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

1,512 Views
1,346 Comparison Views

85% willing to recommend

BigQuery

Read 42 BigQuery reviews

3,122 Views
2,560 Comparison Views

100% willing to recommend

AWS Lake Formation

BigQuery

Comparison Buyer's Guide

Download the report

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: February 2026).

Buyer's Guide

AWS Lake Formation vs. BigQuery

February 2026

Download the complete report

Helped 882,333 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

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 February 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 4.9%, down from 5.1% compared to the previous year. The mindshare of BigQuery is 7.8%, up from 7.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Market Share Distribution
Product	Market Share (%)
BigQuery	7.8%
AWS Lake Formation	4.9%
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.

Read full review

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

"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."

"I can easily move data from cold storage to regular storage."

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

"The features and capabilities of AWS Lake Formation that I have found most valuable are that it is really convenient to see all the different data assets that were configured and understand who has and what type of service has or does not have access to those services."

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

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

"It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services."

"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."

More AWS Lake Formation pros

"It's similar to a Hadoop cluster, except it's managed by Google."

"There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."

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

"The interface is what I find particularly valuable."

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

"One of the most significant advantages lies in the decoupling of storage and compute which allows to independently scale storage and compute resources, with the added benefit of extremely cost-effective storage akin to object storage solutions."

"The best features of BigQuery for me are the fact that it's low-code, no-code; you don't have to be a data scientist to really utilize the tool."

"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."

More BigQuery pros

Cons

"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."

"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."

"The main challenge we faced with AWS Lake Formation was related to cross-account sharing. Granting access to other AWS accounts for tables or databases in a different AWS account was somewhat difficult."

"AWS Lake Formation's pricing could be cheaper."

"Lake Formation could enhance its capabilities in audit logs, real-time monitoring, and advanced data governance."

"In our experience what could be improved are not the support, performance or monitoring, but at a managerial level, the very expensive professional services of AWS. This could be an area of improvement for them. It's too expensive to acquire their support."

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

"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."

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."

"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."

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

"The price could be better. Compared to competing solutions, BigQuery is expensive. It's only suitable for enterprise customers, not small and medium-sized businesses, as they cannot afford this kind of solution. In the next release, it would be better if they improved their AI bot. Although machine learning and artificial intelligence are doing wonders, there is still a lot of room to enhance them."

"So our challenge in Yemen is convincing many people to go to cloud services."

"The product’s performance could be much faster."

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

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

More BigQuery cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

"The tool has competitive pricing."

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

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

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

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

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

"1 TB is free of cost monthly. If you use more than 1 TB a month, then you need to pay 5 dollars extra for each TB."

More BigQuery 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

882,333 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

22%

Computer Software Company

Manufacturing Company

Retailer

Financial Services Firm

15%

Manufacturing Company

14%

Computer Software Company

12%

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	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...

See all answers

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...

See all answers

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...

See all answers

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...

See all answers

Comparisons

Snowflake vs AWS Lake Formation

Compared 16% of the time

Azure Data Factory vs AWS Lake Formation

Compared 16% of the time

Databricks vs AWS Lake Formation

Compared 16% of the time

Amazon Redshift vs AWS Lake Formation

Compared 13% of the time

Amazon EMR vs AWS Lake Formation

Compared 7% of the time

More AWS Lake Formation Competitors

Oracle Autonomous Data Warehouse vs BigQuery

Compared 17% of the time

Snowflake vs BigQuery

Compared 11% of the time

Teradata vs BigQuery

Compared 10% of the time

OpenText Analytics Database (Vertica) vs BigQuery

Compared 8% of the time

SAP Business Warehouse vs BigQuery

Compared 5% of the time

More BigQuery Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

February 2026

Download AWS Lake Formation product report

Buyer's Guide

BigQuery

February 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

Information Not Available

Buyer's Guide

AWS Lake Formation vs. BigQuery

February 2026

Free Report: AWS Lake Formation vs. BigQuery

Find out what your peers are saying about AWS Lake Formation vs. BigQuery and other solutions. Updated: February 2026.

DOWNLOAD NOW

882,333 professionals have used our research since 2012.

See our AWS Lake Formation vs. BigQuery 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.