• 👉 Overview
• 👀 What ?
• 🧐 Why ?
• ⛏️ How ?
• ⏳ When ?
• ⚙️ Technical Explanations
• 🖇️ References

👉 Overview

👀 What ?

Google Cloud Platform's BigQuery is a web service from Google that is used for handling and analyzing big data. It is a Database as a Service (DBaaS) that supports querying using SQL syntax. It is designed to be super-fast and to handle petabytes of data. BigQuery leverages Google’s infrastructure and serverless architecture to speed up queries on large datasets.

🧐 Why ?

BigQuery is significant as it allows companies to transform large datasets into actionable business insights in real time. It enables businesses to focus more on finding insights and less on managing infrastructure. It's designed for scalability, allowing companies to handle increasing volumes of data effortlessly. It delivers high-speed analysis without requiring investments in physical infrastructure and database administrators. BigQuery is also fully managed and serverless, meaning there's no infrastructure to manage, so you can focus on analyzing data to find meaningful insights.

⛏️ How ?

To use BigQuery, you'll first need a Google Cloud Platform account. You can then create a BigQuery project, and load your data into BigQuery. This data can come from various sources such as Google Cloud Storage, Google Drive, or even from an external source. Once your data is loaded, you can run SQL queries on your data to analyze it. BigQuery also integrates with other Google Cloud Platform tools like Dataflow, Dataproc, and Datalab for more complex data processing tasks.

⏳ When ?

Google launched BigQuery as a public service in 2012 after it was used internally by Google for many years. Since then, it has been widely adopted by businesses of all sizes for its speed, scalability, and ease of use.

⚙️ Technical Explanations

BigQuery is built on Google's Dremel technology and takes a different approach than traditional relational database systems. Instead of relying on a network of computers and disk storage, BigQuery executes queries using Google's infrastructure by splitting up queries and running them in parallel. This allows it to handle large datasets and deliver results quickly. BigQuery's serverless architecture means there's no infrastructure to manage. It takes care of all resource allocation, performance tuning, and backups. BigQuery also supports standard SQL syntax, making it easy for anyone familiar with SQL to start querying large datasets.

🖇️ References