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 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 solution is quite good at handling analytics. It's done a good job at helping us centralize them."
"We use AWS Lake Formation typically for the data warehouse."
"The initial setup process is easy."
"The integrated data storage features are good."
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"It has a well-structured suite of complimentary tools for data integration and so forth."
"There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."
"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 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."
"The main thing I like about BigQuery is storage. We did an on-premise BigQuery migration with trillions of records. Usually, we have to deal with insufficient storage on-premises, but in BigQuery, we don't get that because it's like cloud storage, and we can have any number of records. That is one advantage. The next major advantage is the column length. We have some limits on column length on-premises, like 10,000, and we have to design it based on that. However, with BigQuery, we don't need to design the column length at all. It will expand or shrink based on the records it's getting. I can give you a real-life example based on our migration from on-premises to GCP. There was a dimension table with a general number of records, and when we queried that on-premises, like in Apache Spark or Teradata, it took around half an hour to get those records. In BigQuery, it was instant. As it's very fast, you can get it in two or three minutes. That was very helpful for our engineers. Usually, we have to run a query on-premises and go for a break while waiting for that query to give us the results. It's not the case with BigQuery because it instantly provides results when we run it. So, that makes the work fast, it helps a lot, and it helps save a lot of time. It also has a reasonable performance rate and smart tuning. Suppose we need to perform some joins, BigQuery has a smart tuning option, and it'll tune itself and tell us the best way a query can be done in the backend. To be frank, the performance, reliability, and everything else have improved, even the downtime. Usually, on-premise servers have some downtime, but as BigQuery is multiregional, we have storage in three different locations. So, downtime is also not getting impacted. For example, if the Atlantic ocean location has some downtime, or the server is down, we can use data that is stored in Africa or somewhere else. We have three or four storage locations, and that's the main advantage."
"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."
"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."
"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."
"AWS Lake Formation's pricing could be cheaper."
"For the end-users, it's not as user-friendly as it could be."
"So our challenge in Yemen is convincing many people to go to cloud services."
"The process of migrating from Datastore to BigQuery should be improved."
"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."
"There are many tools that you have to use with BigQuery that are different services also provided for by Google. They need to all be integrated into BigQuery to make the solution easier to use."
"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."
"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."
"Some of the queries are complex and difficult to understand."
"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."
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.
