Google BigQuery Reviews and Ratings

Use Cases and Deployment Scope

We have activated the BigQuery export in GA360, and our data flows from GA360 into BigQuery. A Python script has been created to clean the data and store it in a new table within BigQuery. Power BI is connected to BigQuery, where a dashboard has been built. The dashboard updates automatically on a daily basis.

Likelihood to Recommend

Handles petabytes of clickstream data. With BigQuery ML, analysts can train ML models using SQL. Cheap storage + pay-per-query model makes archiving and analysis cost-efficient. Integrates with BI tools (Looker Studio, Power BI) for dashboards. BigQuery ML supports basic ML models but not complex architectures. BigQuery has limited cross-cloud query federation compared to Snowflake. BigQuery is best for: large-scale analytics, digital + transactional data blending, marketing attribution, ML on structured data, and real-time dashboards.

BigQuery is less suitable for high-frequency transactional systems, frequent updates, highly sensitive data governance without additional tooling, advanced deep learning, and multi-cloud setups.

Use Cases and Deployment Scope

For Datalack ,Analytics , LLM Training, report generation etc

Likelihood to Recommend

Report Generation based on raw data is easy.

LLM training is good

Use Cases and Deployment Scope

We mostly use Google BigQuery to collect and filter data that's flowing in from multiple streams like GA4 and SFMC which is vital since now we're able to integrate, "clean", and centralise data. The greatest problem it addresses is the accuracy of data; we can run a sql script on Google BigQuery and connect it to our dashboards which we have been doing since adoption. The auto-scheduling option as well is a great feature, as the update runs automatically daily at 11am. My main scope of work is to analyse campaign performance and purchase behaviours on our dashboards, and this is done by the big help of Google BigQuery.

Likelihood to Recommend

Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).

Use Cases and Deployment Scope

I used Google BigQuery to setup the monitoring of my application health by logging all problems are crashes, this helps get the full picture of my operations.

Likelihood to Recommend

I also use the Google BigQuery to save insights about user behavior to delivery better products. By last I use Google BigQuery as DataLake for all my Iot device telemetries, that make on of the main features of my product.

Use Cases and Deployment Scope

We use Google BigQuery as our company's primary data warehouse to store and analyze large volumes of structured data. We leverage the scheduling feature to run stored queries automatically, which helps keep our data pipelines efficient and up to date. Additionally, we connect Google BigQuery to Google Sheets, enabling us to run SQL queries on spreadsheet data for quick and collaborative data analysis across teams. This setup allows us to perform both advanced analytics and lightweight reporting from a single platform.

Likelihood to Recommend

Google BigQuery is great when working with large datasets and running complex SQL queries fast. We use it as our main data warehouse and also for automating regular reports using scheduled queries.

However, it might not be ideal for small projects or real-time data needs

Use Cases and Deployment Scope

The Google BigQuery is used widely as the storage for Big Data ETL pipelines. Google BigQuery tables contains all the processed data from the ETL pipelines. These tables are then queried by downstream teams or business analytics team to get the relevant information. It act as a data lake. The data partition capabilites based on timestamp is really good which allows large data ingestion seamlessly.

Likelihood to Recommend

Google query is well suited for ETL jobs where the final destination of the ETL pipelines can be datalakes and other teams want to access the nice, transformed data for business usecases. It is very easy to query via console and export the data out.

It is less appropriate if you have different system of data lake and want to ingest data from Google BigQuery to other system. The partition key being restricted which makes extra cautious design decisions for such usecases and handling additional logic.

Use Cases and Deployment Scope

We use Google BigQuery as the company's data warehouse. We also have some stored queries that levarage the scheduling feature of Google BigQuery. And we use it to connect to some google sheets files we have online so that we can make queries over them using SQL and perform som data analysis

Likelihood to Recommend

overall, Google BigQuery is a powerful tool for large scale data analysis and warehouse management, specially when you use a lot of products from google cloud's products. However, it is not well suited when it comes to real time processing or transactional workloads. In such cases, firebase or cloud sql might be more appropriate

Use Cases and Deployment Scope

we are using Store all data that we have. It has proven to be a very good product where we have been easily able to migrate to from our Excel sheets and create a combined database where we store all our historical data across clients and different teams and provide a place where they can access the data easily.

Likelihood to Recommend

if the data is not huge and we don’t need a lot of cloud storage features such as high processing, immediate retrieval of data, et cetera. Then they might be the solutions which are better

Use Cases and Deployment Scope

We have UI survey reporting database in Google BigQuery.

It is meant to give insights of how the users or sales people like the user experience.

We recieve files which finally gets loaded in gcp env.

Querying tables In Google BigQuery gives fast insights with comparatively less time than other cloud dbs.

Likelihood to Recommend

I feel like Choosing it when we have Streaming data with pub sub playing a big role.

Though streaming analytics can be a lil.challenging when you have real time insights needed fast.

Much suited for micro batches or batch data.

You can create a big data warehouse store history.

Batch is where I would prefer

Use Cases and Deployment Scope

Google BigQuery is very powerful tool for Processing & Retrieving the data. millions of records you can retrieve & fetch, do the joins operations and all in just seconds. As our company is having data in millions of records so to perform retrieval, update, delete & process operations. It also provides pay as you go service. So, it is great tool to save time as well as cost. We can also run AI/ML Models directly in Google BigQuery studio no need to build the models explicitly. UI is very easy to understand even non-techie's also understood it easily.

Likelihood to Recommend

If you want to save time as well as cost then go for Google BigQuery studio in terms of Computation & Processing speed.

If your client/vendors uploading your transactional data in GCS buckets then for transformations we have fetch that data to Google BigQuery studio then we have to write DAGs for it even if you have to fetch 1 single file still the process is same. There they can do some improvements if number of files we have to fetch from buckets to studio is less or one time only with lesser size.