AWS Lake Formation vs Snowflake Analytics comparison

Amazon Web Services (AWS) and Snowflake Computing are both solutions in the Cloud Data Warehouse category. Amazon Web Services (AWS) is ranked #8 with an average rating of 8.1, while Snowflake Computing is ranked #12 with an average rating of 8.5. Amazon Web Services (AWS) holds a 3.8% mindshare in CDW, compared to Snowflake Computing’s 3.3% mindshare. Additionally, 85% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Snowflake Computing users who would recommend it.

AWS Lake Formation

Read 21 AWS Lake Formation reviews

2,153 Views
1,744 Comparison Views

85% willing to recommend

Snowflake Analytics

Read 44 Snowflake Analytics reviews

2,633 Views
1,132 Comparison Views

100% willing to recommend

AWS Lake Formation

Snowflake Analytics

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 18, 2024

AWS Lake Formation and Snowflake Analytics are both designed for data management and analytics tasks. Snowflake Analytics seems to have the upper hand due to its performance and scalability.

Features: AWS Lake Formation offers granular data access control, a comprehensive data catalog, and secure data sharing options. Snowflake Analytics provides multi-cluster data warehousing, seamless data sharing, and real-time analytics capabilities.

Room for Improvement: AWS Lake Formation could improve its deployment complexity, integration compatibility with non-AWS services, and expand its analytics capabilities. Snowflake might enhance its cost management for smaller businesses, improve integration with some data sources, and further simplify its user interface for non-technical users.

Ease of Deployment and Customer Service: Snowflake Analytics features a straightforward deployment model with a cloud-native architecture and a user-friendly setup process. It is often praised for responsive customer service. AWS Lake Formation's deployment is more complex due to its integration within the AWS ecosystem, which can be cumbersome for some users.

Pricing and ROI: AWS Lake Formation offers lower initial costs for AWS service users due to seamless integration. In contrast, Snowflake Analytics, despite its higher upfront pricing, often delivers better ROI with its powerful analytics and efficient handling of large data volumes. Snowflake's scalable nature and performance capabilities justify its cost for companies needing extensive data solutions.

To learn more, read our detailed AWS Lake Formation vs. Snowflake Analytics Report (Updated: June 2026).

Buyer's Guide

AWS Lake Formation vs. Snowflake Analytics

June 2026

Download the complete report

Helped 902,417 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

8th

Average Rating

8.0

Reviews Sentiment

5.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Snowflake Analytics

Ranking in Cloud Data Warehouse

12th

Average Rating

8.4

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Web Analytics (2nd)

Mindshare comparison

As of June 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 3.8%, down from 5.4% compared to the previous year. The mindshare of Snowflake Analytics is 3.3%, up from 0.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
AWS Lake Formation	3.8%
Snowflake Analytics	3.3%
Other	92.9%

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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Garima Goel

Associate Principal Engineer at Nagarro

Have created secure cloud-based data lakes and improved real-time data processing using integrated AI features

There are many capabilities which Snowflake Analytics offers that I find valuable, such as the storage and compute engine that allows working with any cloud system such as AWS or Azure, alongside its efficiencies in storage computation and cost-effectiveness, which saves money compared to on-premise systems. We also have features such as pre-cached results, Time Travel, and fail-safe, which are very useful for restoring data if deleted accidentally, and the streams and data pipes that facilitate real-time ingestion are great features as well. Snowflake Analytics offers multiple new connectors, allowing me to connect it with Kafka, and with Snowpark, I can work with any programming language such as Python, Java, or Scala for data processing and analysis. The data sharing feature offered by Snowflake Analytics is good because it allows sharing specific sets of data to end customers or users from different Snowflake Analytics accounts without exposing the entire dataset for data security reasons. Snowflake Analytics' support for machine learning models and real-time insights has enhanced significantly. Originally, it wasn't strong in AI/ML, but now it has multiple models and forecasting capabilities, providing good competition to tools such as Databricks and Spark. In BI, I have worked majorly with Microsoft Power BI, and the integration with Snowflake Analytics is very easy. The way we integrate Snowflake Analytics with other on-premise systems just requires the warehouse details, username, passwords, and the account name, along with multiple options such as client ID and credentials for logging in and creating a session. The end-to-end encryption provided by Snowflake Analytics is very important because, in my previous firm, working in finance and investment management, data encryption is necessary due to the sensitive nature of customer data and the involvement of people's money. It's crucial to have encryption in transit and at rest, along with data masking features which Snowflake Analytics offers.

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

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

"AWS Lake Formation has several valuable features that enhance data management, and one particularly beneficial aspect is how it facilitates better collaboration within data teams."

"We have observed measurable benefits in terms of cost savings, time savings, resource savings, and efficiency improvements in our workflows."

"The integration of AWS Lake Formation with the IAM for authentication and authorization is very good; I didn't have any problems in the setup and thought it was simple."

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

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

"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."

More AWS Lake Formation pros

"The solution auto-scales and provides concurrency, you can have different virtual warehouses without them impacting each other, and it's flexible and infinitely scalable because it's on the cloud."

"Snowflake Analytics is flexible in nature, allowing for the addition of more data tables without affecting the main structure."

"The advanced features like time travel, zero copy cloning and scalability have been most useful. Snowflake requires zero maintenance for Data Warehousing on the cloud system."

"While we've done many complex implementations, Snowflake always has a way of simplifying things."

"It can run complex workloads with varied compute."

"The most valuable feature of Snowflake Analytics is the ability to control and manage the cost."

"The solution auto-scales and it provides concurrency."

"It's cloud-based technology, so users can spin it up a lot faster"

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

"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."

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

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

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

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

More AWS Lake Formation cons

"Integration into different Python and Jupyter notebooks needs to be improved."

"When we insert data into a large table, it takes a lot of time."

"The solution's high price can be an area of concern that needs improvement."

"Snowflake Analytics should probably have more built-in tools for master data management."

"Moving data from legacy systems to Snowflake is not that easy. There are some cases where processors are not actually compatible with Snowflake."

"The solution needs to consider including some updates in the future."

"Navigating the user console can be challenging, particularly when looking for details like the account ID."

"Improvements are needed in importing and exporting large datasets to and from reporting tools."

More Snowflake Analytics cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

"It's not costly if you configure it properly to ensure optimal performance. People don't configure it properly, which is why costs go up."

"The product's pricing is subjective."

"I rate the product price a seven on a scale of one to ten, where one is low price, and ten is high price."

"When using Snowflake, you pay based on your usage. They calculate how much CPU has been used. If you use excess warehouse storage, you are charged one credit per hour. If you are in Asia, you are charged $3 per credit. If you have 10 users running parallel with the same excess, you will be charged $30."

"Snowflake Analytics is not an expensive solution, and its pricing is average."

"Snowflake Analytics is a little more costly than Azure."

"I have been using free trial version."

"It is an expensive solution, but the kind of usability and flexibility it proactively provides for the organizations justify the price."

More Snowflake Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

21%

Manufacturing Company

Construction Company

Computer Software Company

Construction Company

17%

Financial Services Firm

Computer Software Company

Marketing Services Firm

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	11
Midsize Enterprise	13
Large Enterprise	23

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 Snowflake Analytics?

The pricing for Snowflake Analytics is reasonable, but as I am not part of the management team, I am not certain about our organization's exact costs. Overall, the return on investment for this too...

See all answers

What needs improvement with Snowflake Analytics?

One improvement Snowflake Analytics could benefit from is in cost, particularly during peak hours. Sometimes, due to its automatic scalability, we think it has scaled up, but it does not always hap...

See all answers

What is your primary use case for Snowflake Analytics?

I am using Snowflake Analytics because we are already using Snowflake for data engineering and data warehousing tasks, and we are using it for analytics as well as business intelligence reporting. ...

See all answers

Comparisons

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Compared 13% of the time

Snowflake vs AWS Lake Formation

Compared 13% of the time

Azure Data Factory vs AWS Lake Formation

Compared 10% of the time

Databricks vs AWS Lake Formation

Compared 9% of the time

Microsoft Analytics Platform System vs AWS Lake Formation

Compared 5% of the time

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BigQuery vs Snowflake Analytics

Compared 9% of the time

Amplitude vs Snowflake Analytics

Compared 8% of the time

Keen IO vs Snowflake Analytics

Compared 6% of the time

Adobe Analytics vs Snowflake Analytics

Compared 5% of the time

Amazon EMR vs Snowflake Analytics

Compared 3% of the time

More Snowflake Analytics Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

June 2026

Download AWS Lake Formation product report

Buyer's Guide

Snowflake Analytics

May 2026

Download Snowflake Analytics product report

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)

Snowflake Analytics offers advanced capabilities in data warehousing and cloud data migration, with support for machine learning and business intelligence tasks. Its scalable architecture supports large data volumes while enhancing cost efficiency through decoupled computation and storage.

As a flexible, managed environment, Snowflake Analytics enhances data sharing and integration across multiple cloud platforms. It allows seamless data pipeline creation, supports advanced analytics, and facilitates reporting and visualization. Despite facing integration challenges with legacy systems and complex queries, Snowflake's continuous improvements aim to address these issues, making it a reliable choice for organizations transitioning to the cloud.

What features define Snowflake Analytics?

Time Travel: Enables users to access historical data effortlessly.
Zero-Copy Cloning: Allows cloning of data without storage costs.
Scalability: Easily handles large data volumes.
Data Sharing: Facilitates seamless data sharing across platforms.
Decoupled Architecture: Enhances cost efficiency by separating computation and storage.

What benefits should businesses look for in Snowflake reviews?

Cost Efficiency: Optimized costs through advanced storage management.
Performance: High-speed computations and real-time analytics.
Security: End-to-end encryption ensuring data security.
Flexibility: Supports multiple cloud environments for diverse needs.

Enterprises across industries utilize Snowflake Analytics for its robust data handling and cloud integration capabilities. It serves sectors in need of efficient data warehousing, real-time analytics, and machine learning support, making it suitable for cloud migration and enhancing business intelligence operations.

Snowflake Computing

Sample Customers

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

Lionsgate, Adobe, Sony, Capital One, Akamai, Deliveroo, Snagajob, Logitech, University of Notre Dame, Runkeeper

Buyer's Guide

AWS Lake Formation vs. Snowflake Analytics

June 2026

Free Report: AWS Lake Formation vs. Snowflake Analytics

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