BigQuery is a tool designed for thinking at scale, making it ideal for organizations seeking high performance, scalability, and ease of use without the hassle of DevOps management. As a data warehouse, it genuinely delivers a cloud-native experience. Review collected by and hosted on G2.com.

One thing to be aware of with Google Cloud BigQuery is that costs can rise rapidly if queries are not properly optimized or if you frequently scan large datasets. Real-time streaming inserts also tend to be expensive and may experience slight delays. Furthermore, debugging complex queries can be difficult because there is limited visibility into the execution details. Review collected by and hosted on G2.com.

I love how fast BigQuery is. No matter how big the data is, it usually gets the job done quickly. I’ve used it for marketing and sales reports, and it’s super helpful for pulling insights without waiting forever. I also like that it’s easy to share results with my team, especially with dashboards. It saves time and effort every day. Review collected by and hosted on G2.com.

The only real downside is that you can accidentally spend more money than expected if you don’t know how to write efficient queries. It's not always clear how much your query will cost until after it runs. Also, setting up permissions for different team members can be a little confusing. Review collected by and hosted on G2.com.

What I like most is how easy it is to run queries on massive amounts of data and get results quickly. I don’t have to manage servers or tune performance — BigQuery handles all that in the background. The integration with other Google Cloud tools makes building pipelines and dashboards straightforward, and I can focus on the actual analysis rather than worrying about storage or scaling issues. Review collected by and hosted on G2.com.

The main challenge is cost management. Since pricing depends on the amount of data scanned, a single unoptimized query can get expensive, especially when dealing with very large datasets. There’s also a learning curve for new users to understand query optimization and table partitioning. I wish the console had better built-in tools for monitoring query performance and troubleshooting errors. Review collected by and hosted on G2.com.

I like best about Google BigQuery is how it lets me run complex SQL queries on massive datasets in seconds without worrying about servers on performance tuning. its like having a high-speed data warehouse at your fingertips. the best part is that I only pay for what i query, which makes it super cost-efficient for exploratory analysis and quick. Review collected by and hosted on G2.com.

One thing i dislike about BigQuery is that its pricing can be unpredictable if you are not careful especially with on demand queries over large datasets. There also a learning curve around optimizing queries and understanding how data is billed. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I like absolutely everything. I don't see any drawbacks. Review collected by and hosted on G2.com.

BigQuery is incredibly easy to use, especially for anyone familiar with SQL. I use it daily for querying and analyzing large datasets, and its performance remains consistently fast and reliable. The serverless architecture removes the hassle of provisioning and scaling infrastructure, allowing analysts to focus purely on data. Implementation is straightforward, especially within the Google Cloud ecosystem. It supports fast querying even on huge datasets, and integration with tools like Looker, Data Studio, and Google Sheets makes building dashboards effortless. The feature set is robust — from partitioned tables to user-defined functions — and performance is consistently strong. Review collected by and hosted on G2.com.

While powerful, BigQuery's pricing model can be confusing and risky for new users — costs can spike if queries aren't optimized. Customer support can sometimes be slow unless you're on a premium support plan. Additionally, setting up service accounts and IAM permissions can be a bit complex, especially for teams new to cloud environments. There are also a few limitations around real-time data processing and version control within queries that could be improved. Review collected by and hosted on G2.com.

Honestly, what I appreciate most about BigQuery is how it just works with really massive datasets. We’ve thrown some pretty heavy queries at it we're talking billions of rows and it still performs really well. The speed is impressive, even when things get complex. I also like how smoothly it ties in with the rest of the Google Cloud ecosystem. It makes building full data pipelines a lot more straightforward. Plus, since it uses regular SQL, our team could dive in right away without having to learn anything new. It’s solid, scales easily and has pretty much become the core of our analytics setup. Review collected by and hosted on G2.com.

To be honest, the pricing model threw us off in the beginning. We ran a few heavy queries thinking “this should be fine” and then got hit with unexpected costs. It’s not super clear how much something will cost until after you run it, which can be stressful when you're working with large datasets. Also, the interface feels a bit plain. It does the job, but when you're deep into debugging or trying to explore data, it could definitely be more intuitive or visually helpful. Review collected by and hosted on G2.com.

1. Fully manged serverless service.

2. Handles semistructured data very well and scales well for huge volumes of data.

3. Has Machine learning capabilities built in. Review collected by and hosted on G2.com.

1. Optimization issues can cause unpredictable costs.

2. Locked in to single vendor.

3. Lacks visualizations tool despite being OLAP. Review collected by and hosted on G2.com.

Google Cloud BigQuery is a fully managed, serverless data warehouse that enables fast, SQL-based analysis of massive datasets with no infrastructure setup. It offers high performance, pay-as-you-go pricing, and seamless integration with Google services. BigQuery supports real-time analytics, federated queries, and even machine learning with BigQuery ML—all while ensuring strong security and scalability, making it ideal for modern data analytics and BI use cases. Review collected by and hosted on G2.com.

Google Cloud BigQuery is powerful for analytics, it has some limitations such as unpredictable costs due to its pay-per-query model, especially if queries are not optimized. It’s not well-suited for transactional workloads, as updates and deletes are less efficient. Real-time data ingestion can experience slight delays, and users may face query quotas or regional restrictions for certain features. Additionally, while SQL is accessible, mastering cost-efficient query design and performance tuning can involve a learning curve. Review collected by and hosted on G2.com.

The best thing about Google Cloud BigQuery is its speed in handling large datasets. It processes queries very fast and integrates well with tools like Power BI and Google Data Studio. The main upside is that I don’t need heavy infrastructure, and still I can easily analyze and manage big data for my MBA projects and analytics work. Review collected by and hosted on G2.com.

Sometimes the pricing can be a little confusing because it charges based on the amount of data processed. Also, for someone new, the learning curve feels a bit tough in the beginning. Apart from that, I don’t find major issues. Review collected by and hosted on G2.com.