3 reasons to use BigQuery as the heart of your data analytics

BigQuery is serverless

BigQuery is a serverless warehouse, so it gives you the resources when you need them. It means that you don’t have to manage the servers or scale them. All these activities BQ will do for you. From the practitioner’s perspective, BQ has processing automatically distributed over a large number of machines that work in parallel.

The main benefit is that you can focus on gaining insights from the data.

BigQuery ML

Traditionally, if you want to deploy a new machine-learning model, it’s usually time-consuming and requires a lot of sources.

BigQuery ML allows you to deploy machine learning models inside BigQuery easily. It means that if you have data already stored in BigQuery, you can use BigQuery ML to deploy ML models where your data lives.

BigQuery ML currently supports these models:

• Internally trained models:

  • Linear regression

  • Logistic regression

  • K-means clustering

  • Matrix factorization

  • Principal component analysis

  • Time series

• Externally trained models (these ones are trained in Vertex AI):

  • Deep neural network

  • Wide & Deep

  • Autoencoder

  • Boosted Tree

  • Random forecast

  • Vertex AI AutoML Tables

• Imported models

  • Open Neural Network Exchange

  • TensorFlow

  • TensorFlow Lite

  • XGBoost

Near real-time data streaming

There are a lot of businesses that need to make decisions based on real-time data. For this purpose, it’s important to create a data pipeline that businesses can rely on. BigQuery, as a part of Google Cloud, can be combined with other services co-creating real-time data streaming, which is pretty easy if you know what to use.

In these cases, BQ is one of the best options because BQ is designed for real-time data streaming.

If you need to build real-time data streaming, you can use these tools like this

Pub/Sub (messaging queue) > Dataflow or Dataform (data transformation) > BigQuery (analyze data)