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 #9 with an average rating of 8.1, while Google is ranked #3 with an average rating of 8.5. Amazon Web Services (AWS) holds a 4.1% mindshare in CDW, compared to Google’s 7.4% 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

1,976 Views
1,620 Comparison Views

85% willing to recommend

BigQuery

Read 43 BigQuery reviews

3,897 Views
3,157 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: April 2026).

Buyer's Guide

AWS Lake Formation vs. BigQuery

April 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

9th

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

3rd

Average Rating

8.2

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 4.1%, down from 5.3% compared to the previous year. The mindshare of BigQuery is 7.4%, up from 6.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
BigQuery	7.4%
AWS Lake Formation	4.1%
Other	88.5%

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

"The most important advantage of using AWS Lake Formation is its ability to connect the data lake to the other technologies in AWS."

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

"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."

"There is no doubt that this place exceeded my expectations with its incredible ambiance, attentive service, and mouthwatering menu."

"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."

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

"Every solution has its pros and cons, however, for our purposes, this solution works quite well for us."

More AWS Lake Formation pros

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

"The most valuable features of this solution, in my opinion, are speed and performance, as well as cost-effectiveness."

"It's pretty stable. It's fast, and it is able to go through large quantities of data pretty quickly."

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

"The initial setup is simple."

"The most valuable features of this solution, in my opinion, are speed and performance, as well as cost-effectiveness."

"It's a stable, reliable solution."

"It is stable and reliable; there are no bugs or glitches."

More BigQuery pros

Cons

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

"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."

"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."

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

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

"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."

More AWS Lake Formation cons

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

"It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had. In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization."

"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."

"There are integration challenges, particularly with performance when exporting data to BigQuery from other tools like Qualitics."

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

"The solution should reduce its pricing."

"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."

More BigQuery cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

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

"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."

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

"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."

"The tool has competitive pricing."

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

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

More BigQuery pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Manufacturing Company

Retailer

Government

Financial Services Firm

17%

Manufacturing Company

10%

Outsourcing Company

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	10
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...

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 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...

See all answers

What is your primary use case for BigQuery?

We are mostly dealing with Google solutions such as BigQuery, NoSQL, SQL analytical database, secrets manager, and most of the serverless infrastructure as well, databases. I run SQL queries on Big...

See all answers

Comparisons

Snowflake vs AWS Lake Formation

Compared 12% of the time

Amazon Redshift vs AWS Lake Formation

Compared 12% of the time

Azure Data Factory vs AWS Lake Formation

Compared 11% of the time

Databricks vs AWS Lake Formation

Compared 11% of the time

Microsoft Azure Synapse Analytics vs AWS Lake Formation

Compared 6% of the time

More AWS Lake Formation Competitors

Oracle Autonomous Data Warehouse vs BigQuery

Compared 13% of the time

Snowflake vs BigQuery

Compared 10% of the time

Teradata vs BigQuery

Compared 10% of the time

Snowflake Analytics vs BigQuery

Compared 6% of the time

Azure Data Factory vs BigQuery

Compared 4% of the time

More BigQuery Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

May 2026

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Buyer's Guide

BigQuery

May 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

April 2026

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