We performed a comparison between AWS Lake Formation and BigQuery based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
"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."
"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."
"We use AWS Lake Formation typically for the data warehouse."
"Even non-coders can review the data in BigQuery."
"There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."
"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."
"As a cloud solution, it's easy to set up."
"The query tool is scalable and allows for petabytes of data."
"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."
"It's similar to a Hadoop cluster, except it's managed by Google."
"The solution's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements."
"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 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's pricing could be cheaper."
"For the end-users, it's not as user-friendly as it could be."
"With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support."
"The solution should reduce its pricing."
"We'd like to have more integrations with other technologies."
"When it comes to queries or the code being executed in the data warehouse, the management of this code, like integration with the GitHub repository or the GitLab repository, is kind of complicated, and it's not so direct."
"I rate BigQuery six out of 10 for affordability. It could be cheaper."
"I would like to see version-based implementation and a fallback arrangement for data stored in BigQuery storage. These are some features I'm interested in."
"I noticed recently it's more expensive now."
"The initial setup could be improved making it easier to deploy."
AWS Lake Formation is ranked 12th in Cloud Data Warehouse with 5 reviews while BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews. AWS Lake Formation is rated 7.6, while BigQuery is rated 8.2. The top reviewer of AWS Lake Formation writes "Strategically aligning data management in a multi-cloud environment with significant reporting challenges". On the other hand, the top reviewer of BigQuery writes "Expandable and easy to set up but needs more local data residency". AWS Lake Formation is most compared with Snowflake, Azure Data Factory, Amazon Redshift, Microsoft Azure Synapse Analytics and Amazon EMR, whereas BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Vertica and Oracle Exadata. See our AWS Lake Formation vs. BigQuery report.
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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.
