BigQuery Reviews

Organizations experienced cost savings of roughly 17% and noted a 27% improvement in query performance after adopting BigQuery. Integration involved initial training for about six months, resulting in noticeable returns within a year. Many highlighted BigQuery as efficient for managing large datasets, offering approximately 75% savings compared to previous expenses. Users from multiple organizational levels found it advantageous for handling Google Analytics statistics, praising its robust and efficient capabilities.

BigQuery pricing is scalable with a focus on storage and compute. Organizations find it economical, with storage costing around $20 to $22 per terabyte monthly. Pricing operates on a pay-as-you-go model, emphasizing data usage rather than time. Some find costs competitive, while others suggest a need for optimization to prevent expense escalation. Google provides flexible pricing models that cater to unique client needs, making BigQuery adaptable for diverse enterprise requirements.

• "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."

Organizations utilize BigQuery primarily for data warehousing and analytics. They employ it to store, process, and analyze large datasets, integrate with Google Analytics, and enable convenient querying and data transformations. Companies engage in machine learning and ETL processes, accessing BigQuery's cloud-based scalability, data lake operations, and seamless integration with external tools. It is pivotal in diverse industries, from conversational commerce to banking, enhancing insights and supporting decision-making in real-time and non-real-time contexts.

Customer service for BigQuery is mixed. Users find Google's support responsive but note occasional delays unless on a higher-tier plan. Many rely on internal resources or documentation instead of direct contact with support. While engineering and integration challenges exist, Google's wider support network, including partners, is typically praised. However, some users find support limited and not as extensive as competitors like Teradata. Access through community resources is common for assistance.

BigQuery's initial setup is straightforward and cloud-based, requiring no installation. Users find it easy and intuitive, similar to creating a Gmail account. It generally takes less than an hour, although complexity can increase with more extensive data integration. Expertise in Google Cloud and SQL skills aids the process. Setup is manageable with a small team, often involving customization for specific project requirements. Users enjoy the quick deployment and stress-free experience.

BigQuery is praised for impressive scalability, handling vast data efficiently. Users report seamless scaling with large datasets and high user volume, aided by auto-scaling capabilities. Concerns include limitations in query processing for very large datasets and potential expense for smaller operations. Ratings often reflect high satisfaction with its scalability, ranging from seven to ten out of ten. Users appreciate its cloud-based nature, allowing dynamic resource adjustments without needing additional infrastructure.

BigQuery exhibits high stability as mentioned by many, rating it between eight and ten out of ten. It efficiently handles large data volumes without crashing or freezing, and experiences few bugs or glitches. Google's infrastructure and native abilities contribute to its strong reliability. Some report occasional query delays, especially in specific regions like Indonesia, but consider it stable as they face minimal downtime or significant issues. Stability improvements can still enhance performance.