Google BigQuery

What differentiates Google BigQuery from other similar products?

Google BigQuery provides many functionalities to detect query data scan and query execution time before execution , allowing you to set policies to prevent poorly written queries from getting executed. It is very east to setup and performant

What is your favorite aspect of this product?

The east of use with google bigquery is very high, it also allows to connect google sheet to google big query via Connected Sheets feature

What do you dislike most about this product?

the pricing is not very competitive, it has compute and storage cost , because of the storage cost component you are charged for data which is loaded in big query

What recommendations would you give to someone considering this product?

For any team working in google cloud , using google bigquery is a simple solution to setup a data warehouse which is performant when you have a smaller team and want to get results faster

What differentiates Google BigQuery from other similar products?

Google BigQuery sets itself apart from other similar products through its exceptional speed and scalability in processing large-scale datasets. The platform's serverless architecture eliminates the need for infrastructure management, enabling users to focus solely on querying and analyzing data. Its unique billing model, based on on-demand pricing, ensures cost-effectiveness by charging only for the resources used during queries, making it a flexible and cost-efficient solution. Additionally, seamless integration with other Google Cloud services and robust support for real-time data

What is your favorite aspect of this product?

As a data engineer, what truly impresses me about Google BigQuery is its lightning-fast processing speed and hassle-free scalability. The way it effortlessly handles large datasets, allowing me to focus on data engineering tasks without worrying about infrastructure management, is a game-changer. The serverless architecture and pay-as-you-go pricing model make it not only efficient but also cost-effective. Moreover, the seamless integration with other Google Cloud services adds an extra layer of convenience to my workflow. Google BigQuery, with its speed, scalability, and ecosystem integration, has become my go-to tool for efficient pipeline

What do you dislike most about this product?

While Google BigQuery excels in many aspects, one area that can be challenging is the cost, especially for large and complex queries. The on-demand pricing model, while flexible, can lead to unpredictability in expenses, and users need to be mindful of optimizing queries to manage costs effectively. Additionally, there might be occasional limitations in terms of advanced data preparation features within the platform, requiring users to perform some tasks outside of BigQuery.

What recommendations would you give to someone considering this product?

For those considering Google BigQuery, I would recommend carefully assessing your data engineering needs and ensuring compatibility with the platform's strengths in speed and scalability. Take the time to understand the on-demand pricing model and optimize your queries to manage costs effectively. Leverage the seamless integration with other Google Cloud services to maximize the platform's potential within your ecosystem. Additionally, stay updated on new features and enhancements, as Google Cloud regularly introduces improvements to the platform.

What differentiates Google BigQuery from other similar products?

BigQuery stands out because it handles massive datasets without requiring any infrastructure work. The serverless model means you can focus on writing SQL rather than managing clusters or worrying about scaling. Its speed on large analytical queries is also a big advantage compared to traditional warehouses.

What is your favorite aspect of this product?

My favorite part is how easy it is to run complex queries quickly. Even large joins or detailed analytics run smoothly, and the pay-as-you-go model makes it simple to experiment without committing to heavy infrastructure.

What do you dislike most about this product?

The cost can be unpredictable, especially if a query isn’t optimized or someone accidentally scans a large dataset. Permissions can also feel confusing at times because they’re tied to multiple Google Cloud layers.

What recommendations would you give to someone considering this product?

I’d suggest spending time learning how to structure queries efficiently and setting up usage limits early. BigQuery performs extremely well, but managing costs and access controls from the start makes the experience much smoother.