An ideal location to warehouse marketing data

My overall experience has been wonderful. It's easy to set up and use, and Google even has training for how to use it on Coursera at a pretty cheap price.

Pros

BQ was incredibly easy to set up and get going. As a beginner, the public data sets available also make practice very easy. There are a great many other softwires available in the Google Cloud that connect directly to BQ, so the whole system is set up to expand its usefulness without needing to buy more software.

Cons

I haven't run into anything about this software that I haven't liked so far. I have nothing negative to report.

Great serverless cloud data warehouse

We used BigQuery to analyze firebase events data from our mobile apps. Considering the sheer volume of this dataset, querying has been mostly very fast and reliable, albeit at a high cost.

Pros

I like the serverless nature of BigQuery. It takes away most of the maintenance costs involved in maintaining and fine-tuning a data warehouse.

Cons

I didn't really like the on-demand pricing of BigQuery. Monthly costs tend to blow up excessively. I think they have a different pricing option now to resolve this though.

Get started with BigQuery, A powerful tool for analysing Big data

Pros

Our salesforce campaigns relies heavily on data warehouse, which is the backbone of everything we do. This data set contains both row data sets and BigQuery is used to aggregate this sets by running schedule queries on it.

Cons

This platform requires strong SQL skills. Huge dependency on tech team to fix queries sometime.

BigQuery data warehousing - Excellent performance at MINIMAL cost

Made it possible to "get our feet wet" with data warehousing / dashboards / reporting, without the $$$ commitment of other tools.

Pros

Seamless integration with our data in G-sheets, Airtable, and other sources. Data visualizations / BI dashboards (using DS) at rock-bottom cost.

Cons

The number and type of data visualizations could be improved. Help system is not extensive (but with Stack Overflow, you'll be fine). The "stickiness" (persistence) and flexibility/customizability of filters needs work.

BigQuery for a Data Engineer

for 3 months our mobile applications stopped saving certain data, thanks to its integration with firebase, we were able to recover that data by querying its partitioned tables, and we were able to restore that data with its integration with python We have also built a dashboard where we make a conciliation of our clients' data, in case something goes wrong, we immediately realize what is happening

Pros

For someone like me who works on the complete cycle of the data, it turns out to be a very good tool, since it allows us to do the complete cycle, from the initial step that is to put together a good ETL, clean our data, and be able to present it in a dashboard

Cons

once the table is created it is difficult to edit the columns, so you have to delete it when you use the data stream, your data is in a cache, and you can't manipulate it until a couple of hours have passed