What is BigQuery, advantages & Disadvantages?

Abhishek Pratap Singh
3 min readJan 16


BigQuery is a fully managed, cloud-native data warehousing platform that enables super-fast SQL queries using the processing power of Google’s infrastructure. It is a serverless, highly scalable, and cost-effective data storage and analytics service that allows you to analyze large and complex datasets quickly and efficiently. BigQuery is designed to handle large volumes of data, both structured and unstructured, and can process up to trillions of rows per second. It is a popular choice for data warehousing, analytics, and machine learning applications, and is often used to power business intelligence dashboards and reports. BigQuery is fully integrated with other Google Cloud Platform (GCP) services, making it easy to analyze data in conjunction with other GCP resources and tools.

BigQuery offers a number of features that make it a powerful and flexible data warehousing platform:

  • Serverless: BigQuery is a fully managed service, which means that you don’t have to worry about infrastructure, capacity planning, or maintenance.
  • Scalability: BigQuery is designed to handle very large datasets and can scale to petabyte-sized data warehouses.
  • High performance: BigQuery uses a columnar storage format and advanced query optimization techniques to achieve fast query performance on large datasets.
  • Integration with other GCP services: BigQuery is fully integrated with other GCP services, such as Cloud Storage, Cloud Functions, and Data Studio, making it easy to analyze data in conjunction with other GCP resources and tools.
  • SQL support: BigQuery supports a variant of the standard SQL language, making it easy to query data using familiar SQL syntax.
  • Data streaming: BigQuery allows you to stream data in real-time, enabling real-time analysis of streaming data.
  • Machine learning: BigQuery provides built-in machine learning functions and integration with TensorFlow, making it easy to build and deploy machine learning models on large datasets.
  • Data security: BigQuery offers a number of security features, including encryption of data at rest and in transit, access control, and compliance with industry standards such as GDPR and HIPAA.

There are a few potential disadvantages to using BigQuery:

  • Cost: While BigQuery is generally a cost-effective data warehousing solution, it can be expensive for certain types of workloads, such as those with a high volume of small queries or those that require a lot of data processing.
  • Limited customization: As a fully managed service, BigQuery does not offer as much flexibility or customization as on-premises data warehousing solutions.
  • Dependence on GCP: Because BigQuery is a part of the Google Cloud Platform (GCP), you will need to be comfortable using GCP and relying on it for your data warehousing needs.
  • Limited integrations: While BigQuery is fully integrated with other GCP services, it may not have as many integrations with non-GCP tools and services as some other data warehousing solutions.
  • Complexity: BigQuery can be complex to use, especially for users who are new to data warehousing or SQL. It may require a significant learning curve for users who are not familiar with these technologies.

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Abhishek Pratap Singh

Software Engineer || Co- Founder || B-Plan contest finalist at IIT Kharagpur || 1st Rank on SQL- HackerRank