AWS Lake Formation vs BigQuery comparison

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

"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 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."

"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."

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

"AWS Lake Formation lets you see all your data and tables on one screen."

"The interface is what I find particularly valuable."

"I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."

"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."

"BigQuery's querying capabilities are very optimized for large datasets."

"It's straightforward to set up."

"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 setup is simple."

"BigQuery has a very nice interface that you can easily learn if you know SQL."

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

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

"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."

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

"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."

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

"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."

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

"The processing capability can be an area of improvement."

"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."

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

"We'd like to have more integrations with other technologies."

"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."

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

"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."

"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."

"AWS Lake Formation is a bit expensive."

"The tool has competitive pricing."

"The pricing is good and there are no additional costs involved."

"Its cost structure operates on a pay-as-you-go model."

"The platform is inexpensive."

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

"The pricing is adaptable, ensuring that organizations can tailor their usage and costs based on their specific requirements and configurations within the Google Cloud Platform."

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