What is BigQuery?

Before you start diving into GA4 data in BigQuery, let's take a look at what BigQuery is. If you already know what BigQuery is then you can ignore this and wait for the next email but If you are not then continue reading.

In simple terms, BigQuery is a way to store a large amount of data.

You might be familiar with the term “database”. If you are then essentially BigQuery is a database.

When most people talk about a database, they talk about a storage system for transactional data, the data that is used to power an application such as a website, offline store checkout system, customer support system, etc. Such databases are also called OLTP (online transaction processing) databases.

However, BigQuery is a different flavor of a database, called a Data Warehouse.

A Data Warehouse stores data for analyzing and reporting purposes.

While the data in an OLPT database is organized for faster queries of individual data points, the data in a warehouse is organized and optimized for faster analysis and reporting.

To access, read, and manipulate the data stored in any type of database (including data warehouses), you use a language called “SQL” short for “Structured Query Language”. The command that you write in SQL is called SQL Query.

SQL is a universal language for most of the database systems provided e.g. Microsoft SQL Server, Oracle, BigQuery, etc. Once you learn SQL for one system, you can easily learn SQL for another system. Most of the syntax is common with a few differences.

Features and Benefits of BigQuery

  1. Fully managed -Google takes care of all the upgrades etc.
  2. Serverless - Your organization does not have to purchase any machines and install them.
  3. A scalable and distributed - You can query terabytes of data in seconds.
  4. Visual interface - Cloud console allows marketing analysts to run SQL and access the data with ease.
  5. Access via Programming Languages and drivers - Technical folks, developers, data scientists, etc. can use command line tools, use programming including Python, Java, JavaScript, etc. as well as using APIs to access and manage data in BigQuery. There are also integrations and drivers available to integrate the data with other third-party tools and applications.

I hope this clarifies what BigQuery is. Next, we will start to dive into the organization of the data.

If you have any questions then do not hesitate to send those to me.

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