Google BigQuery Reviews

Can be pricey. There are ways to lower costs but they aren't always straightforward.

It can be overwhelming to non-technical users at first.

You can easily get confused as to what to do to start if not familiarized with the workflow.

Query pricing is still higher if we don't take flat pricing which is high.

Storage pricing is also counted.

It do not handle external dependencies.

Google BigQuery queries need to be optimized to avoid high costs when pulling data.

Google BigQuery needs knowledge of SQL coding to leverage its data analysis capabilities.

BigQuery can be dangerous. The charges can rack up quickly if you don't construct your queries properly. Traverse too much data too frequently and you can cost yourself some money.

Although BigQuery machine learning gives you the option to control your geographic data, it only applies to the US, Asia, and Europe. Further expansion of this option to other parts of the world would be beneficial.

You don’t need to install, provision, or set up anything with Big Query because it is managed. The downside being that you can’t use it outside of Google Cloud Platform.

Google BigQuery is great for large data sets where you need a familiar SQL interface but it is still slower than running the same SQL query on RDBMS, assuming your data is mostly structured.

It is expensive if you have a lot of data that needs to be queried each time the query is run due to the license metrics used in Google BigQuery.

Some of the SQL operations like table join are not optimized and can be slow compared to a full database.

Even though the cost is pay-per-use, it's still expensive. This may make the program impractical for companies that won't use it frequently enough or for high-powered processing as it is meant for.

Sometimes it is difficult to import data from alternate sources and manage it. The integrations between BQ and other online cloud storage aren't always a smooth transfer.

One issue with Google Cloud Storage is its price. For one to have that premium Google Cloud Storage, for the purpose of massive storage, he/she must have adequate cash. Otherwise, Google Cloud Storage is a safe and perfect online storage platform.

The only thing that can come to mind that would be annoying with this software was that sometimes when trying to share files on the Cloud with coworkers, it would just not share at all, or there would be a massive delay in when I shared them and when they received them. Other than that though, everything is perfect with this.

UI/UX is a bit scary right away.

Takes a strong learning curve to get used to.

Some of the SQL you can execute in a database is not exectuable in BigQuery which limits how much you can do right inside the platform. However, most of what you can do in a database is doable in BigQuery itself.

Charting and other data visualization working with the data inside of BigQuery could be an improvement

The legacy and non-legacy SQL was a little confusing and some of the SQL functions did not always allow us to do the things we wanted to do

Console interface is a little clunky.

Documentation should be detailed. I had a very hard time learning it. My seniors are also facing so many hurdles while using this.

No proper flow is mentioned in the docs about how to use this product. We faced so many errors at different stages.

SQL syntax is not exactly same as ANSI SQL so there is a learning curve. Traditional SQL queries cannot execute in BigQuery which limits portabiltiy of the code.

Limitation on visualization: We can improve visualization in data studio by bringing in the ability to support complex functions/formulas such as Tableau can do.

It would be awesome to have BQ be real-time. Right now it serves the OLAP use case very well, but interactive would be great too.

The user interface is not the best we've used.

We'd love to have the Standard SQL mode be on by default.

BigQuery does impose quite a few limits on the higher end queries, although they are entirely understandable. For example, very large 'GROUP BY' clauses can sometimes fail with a "Resources Exceeded" error, as the distributed computational nature of BigQuery forces all of that data to be compiled on a single machine, and when that machine runs out of memory it throws the aforementioned error. You can increase your Billing Tier to complete these queries, though.

When getting data out of BigQuery, there are also quite a few limits. For example, if you are returning a large result set, you are essentially forced to write the results to a table and then export that table to Google Cloud Storage to then be downloaded. However, during the export process, if the table is large, Google will split that table into many smaller blocks that need to be reassembled.

Performance could always be improved

If accidentally the return dataset would be humongous (you forget to LIMIT), you cannot really stop a running query, and it'll probably be billed

Documentation is not complete, sometimes not clear.

Performance is unstable occasionally.

Error message not clear.

Authorization is so simplified and hard to maintain different level of security to access to data

Though it is SQL some syntax are different but they are getting used to after you use for some time.

The legacy SQL is in beta state but can be used and you can run the query with simple SQL.

More documentation is needed for using User-defined functions in Big Query.

Integrating with data outside of Google Cloud can be slow

Data and the related queries are structured differently than in SQL Server, so there is some training necessary before broadly adopting

Understanding the pricing of various queries can be a challenge. Figuring out what makes a query more expensive than another takes time.