BigQuery

In the previous lesson, we looked at Bigtable where we learned about BigTable which provides low latency and high performance with petabytes of data. But, BigTable is costly. So, what if you need something cost-effective for analysis of terabytes to petabytes of scale with latency in seconds?

BigQuery is the right choice for that. We have used BigQuery while creating billing exports. Let’s see BigQuery (BQ) in details in this lesson.

Introduction

BigQuery is GCP’s serverless, highly scalable, and cost-effective cloud data warehouse.

It allows for super-fast queries at petabyte scale using the processing power of Google’s infrastructure. Because there’s no infrastructure for customers to manage, they can focus on uncovering meaningful insights using familiar SQL without the need for a database administrator.

It’s economical because you pay only for the processing and storage you use.

BigQuery is part of Google Cloud’s comprehensive data analytics platform that covers the entire analytics value chain including ingesting, processing and storing data, followed by advanced analytics and collaboration.

Architecture

Creating a Data warehouse requires a lot of money along with a super administration and efficient server management. Bigquery solves this problem using a very innovative architecture.

BQ decouples the storage and compute and allows them to scale independently on demand. This structure offers both immense flexibility and cost controls for customers because they don’t need to keep their expensive compute resources up and running all the time. This allows us to ingest all sizes and types of data into BQ and start analyzing using SQL.