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 #13 with an average rating of 7.3, while Google is ranked #4 with an average rating of 8.3. Amazon Web Services (AWS) holds a 5.2% mindshare in CDW, compared to Google’s 7.0% mindshare. Additionally, 71% 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 8 AWS Lake Formation reviews

2,300 Views
1,944 Comparison Views

71% willing to recommend

BigQuery

Read 40 BigQuery reviews

3,303 Views
2,698 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 2025).

Buyer's Guide

AWS Lake Formation vs. BigQuery

April 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

13th

Average Rating

7.6

Reviews Sentiment

6.9

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

7.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 5.2%, down from 6.0% compared to the previous year. The mindshare of BigQuery is 7.0%, down from 8.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse

Featured Reviews

Ramesh Raghavan

US South Lead, Accenture AWS Business Group at Accenture

Centralized repository, offers various cataloging mechanisms for quick data retrieval but data governance capabilities could be better

There are a couple of areas for improvement with Lake Formation. One of the main challenges, especially when dealing with rich media content, like in MarTech (Marketing Technology) or ad agencies, is its versatility. Some clients feel that Lake Formation doesn’t meet their needs and they tend to prefer competitor products for those specific use cases. The second area for improvement is in data governance. Specifically, Lake Formation could enhance its capabilities in audit logs, real-time monitoring, and advanced data governance. This includes managing the entire data lineage—where the data originated, how it moves, and where it’s currently stored. The visibility of the data as it evolves is crucial, and that’s where more advanced governance capabilities would be beneficial.

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VikashKumar1

Tech lead at Ford Motor Company

Easy to maintain and provides high availability

Since I used BigQuery over the GCP cloud environment, I'm not sure whether we can go through internal IDEAs like IntelliJ or DBeaver that we use to connect with databases. Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process. Sometimes, we face some issues, bugs, and defects. We must first connect with a VPN to check data issues while working from home. Then, it allows you to connect to the cloud. After logging into the cloud, it searches for the service we are looking for, and then we go to BigQuery. This is a long process. After that, we analyze the issues in a table. Instead, it would be very helpful if it could provide a tool that we can install on our MacBook or Windows system. Once we open this tool, we can connect directly to the BigQuery server and easily perform tasks.

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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."

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

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

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

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

"AWS Lake Formation works hand in hand with other products."

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

"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 setup is simple."

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

"The solution's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements."

"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."

"BigQuery can be used for any type of company. It has the capability of building applications and storing data. It can be used for OLTP or OLAP. It has many other products within the Google space."

"The most valuable aspect of BigQuery is its ability to handle high data workloads without causing friction with our online systems."

"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."

"It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution."

More BigQuery pros

Cons

"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."

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

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

"If I could improve AWS Lake Formation, I would add more integrations with SageMaker."

"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."

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

"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."

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

"When I execute a query, the dashboard doesn't always present the output seamlessly. Troubleshooting requires opening each pipeline individually, which is time-consuming."

"When I execute a query, the dashboard doesn't always present the output seamlessly."

"The product could benefit from improvements in user-friendliness, particularly in terms of the user interface."

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

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

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

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

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

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

"The tool has competitive pricing."

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

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

More BigQuery pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

21%

Computer Software Company

14%

Manufacturing Company

Government

Computer Software Company

18%

Financial Services Firm

14%

Manufacturing Company

11%

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about AWS Lake Formation?

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

See all answers

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

The pricing is expensive compared to OpenStack, but cheaper than other cloud environments. It's middle-of-the-road for regular storage yet very cost-effective when using Amazon Glacier for data.

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What needs improvement with AWS Lake Formation?

If I could improve AWS Lake Formation, I would add more integrations with SageMaker. I would have built-in functions that provide statistics for the data when using the GUI, such as SageMaker Insig...

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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?

The price is perceived as expensive, rated at eight out of ten in terms of costliness. Still, it offers significant cost savings.

See all answers

What needs improvement with BigQuery?

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. In general, if I know SQL and start playing around, it will star...

See all answers

Comparisons

Snowflake vs AWS Lake Formation

Compared 37% of the time

Amazon Redshift vs AWS Lake Formation

Compared 18% of the time

Azure Data Factory vs AWS Lake Formation

Compared 18% of the time

Microsoft Azure Synapse Analytics vs AWS Lake Formation

Compared 12% of the time

Amazon EMR vs AWS Lake Formation

Compared 5% of the time

More AWS Lake Formation Competitors

Snowflake vs BigQuery

Compared 25% of the time

Oracle Autonomous Data Warehouse vs BigQuery

Compared 16% of the time

Teradata vs BigQuery

Compared 15% of the time

Vertica vs BigQuery

Compared 14% of the time

Dremio vs BigQuery

Compared 5% of the time

More BigQuery Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

April 2025

Download AWS Lake Formation product report

Buyer's Guide

BigQuery

April 2025

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

April 2025

Free Report: AWS Lake Formation vs. BigQuery

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