BigQuery Reviews

Organizations experienced improved performance and significant cost reductions with BigQuery, gaining a 27% performance improvement and saving 17% in costs. The system is seen as efficient for managing large datasets and a reliable choice for investment returns. Transitioning took six months of training. Some entities noted approximately 75% savings while mentioning BigQuery in comparison to competitors like AWS, highlighting its cost-effectiveness. Various departments use it for analytical purposes within corporate environments.

BigQuery pricing is generally seen as competitive, affordable, and adaptable, especially for enterprise users. Users appreciate the flexible pay-as-you-go model, where costs depend on data volume and query execution rather than time. However, some caution that the solution can become expensive without proper optimization of queries and data usage. The storage cost is low, but processing fees can accumulate, necessitating strategic planning. Google offers a $300 free trial to new users, enhancing its appeal.

• "We are above the free threshold, so we are paying around 40 euros per month for BigQuery."
• "The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."
• "The platform is inexpensive."

BigQuery is primarily utilized as a data warehouse for storing, processing, and analyzing large datasets. Users employ it for machine learning, ETL/ELT processes, and querying functionalities. Many leverage its seamless integration with Google Cloud services, especially for real-time analytics, reporting, and data transformations. Its cloud-based nature and compatibility with various data visualization tools make it suitable for wide-ranging applications, from marketing analytics to enterprise data management.

Many users report minimal interaction with BigQuery's technical support, often managing issues internally or using Google's documentation. When engaged, Google's support is described as responsive and effective, though challenges with integration persist. Higher-tier users find it more accessible. Google’s customer service scores well, but some seek external expertise for in-depth help. Comparatively, Teradata has a larger global support team. Overall satisfaction varies, with ratings mostly between seven and ten out of ten.

BigQuery's initial setup is generally simple and quick, often compared to opening a Gmail account. It typically involves logging into Google Cloud, creating projects and tables, and can be completed in under an hour without the need for installations. Some users mention needing experience for optimal implementation and handling data integration. Setups can vary from straightforward to moderately complex, depending on specific project requirements and integration needs.

BigQuery exhibits strong scalability, capable of managing vast datasets efficiently. Users note its ability to handle petabytes of data without configuration and maintain speed even with large volumes. While most emphasize its auto-scaling and dynamic resource allocation, some mention performance limitations under specific conditions. The tool's cloud-based nature enhances its scalability potential, suitable for growing demands, though it may pose challenges for smaller businesses due to cost considerations. Many rate its scalability 9-10 out of 10.

BigQuery is widely recognized for its stability, exhibiting performance reliability with few issues. Users frequently rate its stability between seven and ten out of ten, highlighting its robust nature. Google’s infrastructure ensures consistent performance, benefiting from strong support and maintenance. Feedback mentions rare minor glitches and delay in query processing, yet these do not significantly impact its perceived dependability, making it a preferred choice in cloud data analytics.