Google BigQuery Reviews

Best thing about Bigquery is its scalability and managed service provided by GCP(Google cloud platform), it can connect seamlessly with almost all services available in the market whether it is on premises or cloud based. It is one of the largest Data warehouse which also comes up with Data Lakehouse feasibility. I also like about its security features like policy tags and authorized view.

I don't think there is anything I don't like, maybe they need to work on estimated cost feature while running any query, sometime it doesn't show the memory associated with that and as its analytical warehouse, so real time update is not possible like transactional database, maybe in future they can add those features as well

In current scenario, all our data sinks are stored in Bigquery or external tables linked with Bigquery becasue its so easy todo any analysis on top of Bigquery and also further it seamlessly connect with Looker for detailed analysis. Now days, we also started to leverage their model creation capability on the data stored in Biglake managed table or Bigquery table. Ultimately it really helps to build end to end pipeline without worrying about storage and scalling.

It's massive, scalable serverless architecture with cloud integration make large scale data analytics fast and efficient.

My experience with BigQuery was positive overall, but the interface felt unintuitive for non-technical users, making it harder to navigate without prior SQL or cloud experience.

BigQuery helps me handle large datasets efficiently for tasks like predictive analysis, forecasting, and budgeting. It gives me the confidence to analyze complex data and draw insights with greater accuracy and trust.

I use Google Cloud BigQuery for learning big data concepts and implementing chatbots. I like that all the services and products are in one place, making it easy to use BigQuery for different use cases. I appreciate its ease of access and integration with different tools. Not just BigQuery, but Google Cloud as a whole environment is very beginner-friendly and provides a sandbox at a low cost for learning. Tools like Google CloudSQL, BigQuery, APIs, and Vertex AI are very valuable for learning chatbot implementation. The initial setup of Google Cloud BigQuery was very easy.

The billing details can be clearer and more easily monitored. The option to pause and resume payments could be designed for easier UX. It would be really helpful to have the option to pause payments on weekends or provide a prompt to pause when not in use for more than 6 hours.

Google Cloud BigQuery consolidates services and products, simplifying use for various cases. Its ease of access and integration with different tools enhance my learning experiences. It's part of a beginner-friendly environment with a low-cost sandbox ideal for learning chatbot implementation.

It works similarly to other databases, and it also supports scripting in multiple languages such as shell scripts, SQL, and JavaScript. We used it to build interactive vulnerability data dashboards in Google Looker Studio by extracting data from Axonius into BigQuery and then using BigQuery as the data source.

We’ve scheduled multiple SQL jobs, and when they failed during the daily runs, we wanted to send an email notification. However, we weren’t able to configure the email setup.

Since we weren’t able to create interactive dashboards in Axonius, we extracted data from Axonius, stored it in BigQuery, and then used it as a data source in Looker Studio to build interactive dashboards.

.That I can quickly query data and share queries with my team

Integration with Google drive is unstable

Consistent data through queries

UI / UX:

The interface is clean and intuitive, especially when writing and testing queries. Features such as query history, saved queries, and inline validation make it easy to iterate quickly. Even with complex queries, the editor feels smooth and responsive, which helps reduce overall development time.

Integrations:

Storing dataHonestly, the absolute best part is how it instantly turbocharges my AppSheet apps.

When I had to handle a massive 2-lakh row upload, BigQuery handled it effortlessly.

I also love shifting my clunky Apps Script logic into secure BigQuery Stored Procedures.

It keeps the heavy data-manipulation lifting on the database side exactly where it belongs.

.Super fast querying of huge amounts of data, ondemand without having to manage servers

just that minimum query speed forr tiny amounts of data

being our source of truth for company data, letting anyone in the company query for insights in a cost effective secure manner

I like the good pricing of Google Cloud BigQuery. I also appreciate the ease of use, versatility, and the number of different data sources that can be connected. For me, it is truly a market leader today.

I like absolutely everything. I don't see any drawbacks.

Google Cloud BigQuery solves the problem of data centralization, providing versatility and ease of use. We use it for data analysis and integration of different sources, including data from our platform and payment provider.

I like the fact that it's intuitive enough for the price that it comes with. The free tier especially is useful for smaller datasets and helps to keep the cost down. Additionally, it is an enterprise tool by Google that is robust and reliable.

I believe the main downside is that cost is unpredictable especially when it comes to large queries

The primary reason I use bigquery is to store GA4 data, given GA4 interface has a limit of 14 months for event level granular data. Connecting BQ to dashboards helps with YoY comparisons without running into limitations with the GA4 connection.