AWS Lake Formation vs BigQuery comparison

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Amazon Web Services (AWS) and Google are both solutions in the Cloud Data Warehouse category. Amazon Web Services (AWS) is ranked #4 with an average rating of 8.1, while Google is ranked #2 with an average rating of 8.7. Amazon Web Services (AWS) holds a 3.3% mindshare in CDW, compared to Google’s 6.9% 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

2,858 Views
2,201 Comparison Views

85% willing to recommend

BigQuery

Read 44 BigQuery reviews

6,052 Views
5,023 Comparison Views

100% willing to recommend

AWS Lake Formation

BigQuery

Comparison Buyer's Guide

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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: September 2026).

Buyer's Guide

AWS Lake Formation vs. BigQuery

September 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

4th

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

2nd

Average Rating

8.2

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 3.3%, down from 5.2% compared to the previous year. The mindshare of BigQuery is 6.9%, down from 8.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
BigQuery	6.9%
AWS Lake Formation	3.3%
Other	89.8%

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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reviewer2807520

Director at a consultancy with 11-50 employees

Data processing has become faster and large analytical queries run reliably at multi‑region scale

The features I have found most valuable in BigQuery include the query cache, which is good and scales well. We have not seen problems with scaling. It handles huge amounts of data quite well, and the multi-regions feature works well. To measure the effectiveness of BigQuery, I think it is quite good. The metrics I use to evaluate BigQuery's effectiveness include that scaling is quite good and handling of big amounts of data. We do not see any glitches, and we do not see major timeouts. Google platform in general has been quite stable, and we do not see that many outages at resource level or at overall zonal level.

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

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

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

"The main benefits that I have seen from using AWS Lake Formation are related to FinOps because you have control of your data and can track your costs since AWS Lake Formation is integrated into a unique platform, which is AWS Cloud Service."

"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."

"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."

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

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

"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

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

"The query tool is scalable and allows for petabytes of data."

"The initial setup is simple."

"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."

"We can analyze terabytes of data in a very small amount of time."

"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."

"The product is serverless. We only need to write SQL queries to analyze the data. We need to pay based on the number of queries. The retrieval time is very less. Even if you write large queries, the tool is able to bring back data in a few seconds."

"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."

More BigQuery pros

Cons

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

"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."

"AWS Lake Formation might not be easy to learn initially, especially for technical staff, due to various underlying options."

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

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

"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."

"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."

"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."

More AWS Lake Formation cons

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

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

"It would be helpful if they could provide some dashboards where you can easily view charts and information."

"The process of migrating from Datastore to BigQuery should be improved."

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

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

"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."

"We would like to be able to calibrate the solution to run on top of a raw file."

More BigQuery cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

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

"BigQuery is inexpensive."

"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."

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

"I have tried my own setup using my Gmail ID, and I think it had a $300 limit for free for a new user. That's what Google is offering, and we can register and create a project."

"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 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."

More BigQuery pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Outsourcing Company

13%

Manufacturing Company

Construction Company

Financial Services Firm

14%

Outsourcing Company

14%

Manufacturing Company

10%

Media Company

10%

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	15
Midsize Enterprise	10
Large Enterprise	21

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...

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...

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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

Comparisons

Amazon Redshift vs AWS Lake Formation

Compared 12% of the time

Snowflake vs AWS Lake Formation

Compared 11% of the time

Azure Data Factory vs AWS Lake Formation

Compared 8% of the time

Databricks vs AWS Lake Formation

Compared 8% of the time

Microsoft Azure Synapse Analytics vs AWS Lake Formation

Compared 6% of the time

More AWS Lake Formation Competitors

Snowflake vs BigQuery

Compared 11% of the time

Oracle Autonomous Data Warehouse vs BigQuery

Compared 8% of the time

Teradata vs BigQuery

Compared 8% of the time

Snowflake Analytics vs BigQuery

Compared 7% of the time

Azure Data Factory vs BigQuery

Compared 3% of the time

More BigQuery Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

October 2026

Download AWS Lake Formation product report

Buyer's Guide

BigQuery

October 2026

Download BigQuery product report

Also Known As

No data available

Overview

AWS Lake Formation streamlines data lake organization by integrating seamlessly with AWS services, ensuring robust access control, and facilitating effective data management. Its extensible framework and centralized data handling make it ideal for cloud environments that require scalability.

AWS Lake Formation offers comprehensive data synchronization with AWS, robust security through fine-grained policies, and seamless team collaboration tools. It centralizes data assets, supports analytics with Glue, and enhances data mesh structures. Its IAM integration supports efficient governance, with reliable data storage and retrieval backed by extensive documentation. While highly efficient within AWS ecosystems, Lake Formation faces integration challenges with third-party libraries, and users experience clutter in its interface. Aspects such as automation, policy management, and debugging are areas for improvement. Enhancements in orchestration, cross-account sharing, and integrations with services like SageMaker are user recommendations.

What are the key features of AWS Lake Formation?

Data Synchronization: Integrates with AWS services for seamless data management.
Security Policies: Implements fine-grained access control for secure data handling.
Team Collaboration: Facilitates cooperation through centralized data management.
Analytics Support: Enhances data processing with Glue integration.
Extensibility: IAM integration allows scalable governance.

What benefits or ROI can users expect?

Scalability: Efficiently manages expanding data volumes.
Cost Efficiency: Reduces cloud data management expenses.
Data Security: Ensures compliance with robust policies.
Comprehensive Support: Access to detailed documentation and AWS resources.

Organizations utilize AWS Lake Formation across various industries to build and manage data lakes, ensuring effective data governance and storage solutions. It helps integrate and transform data, control permissions, and support analytics. From e-commerce to healthcare, AWS Lake Formation delivers centralized data repositories for structured and unstructured data, securing information while providing scalable infrastructure and efficient resource management.

Amazon Web Services (AWS)

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

Sample Customers

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

Information Not Available

Buyer's Guide

AWS Lake Formation vs. BigQuery

September 2026

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