BigQuery is a potent tool that can be used for many reasons. In this article, we will describe the main benefits of considering BigQuery as the heart of your data analytics platform. BigQuery is serverless BigQuery is a serverless warehouse, so it gives you the resources when you need them. It means that you dont have to manage the servers or scale them. All these activities BQ will do for you. From the practitioners 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, its 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, its 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) The post 3 reasons to use BigQuery as the heart of your data analytics appeared first on Optimics.
Traditionally, obtaining user data for analysis was a complex and time-consuming task. However, Google has simplified this process with the introduction of User Data Export in BigQuery. This feature allows organizations to access valuable data, including
Migrating from Google Analytics Universal to GA4 has likely put you in a position where you have to decide which date is more accurate for your web model. Add to that different numbers in the Google Ads interface as well as BigQuery, and you enter a philo
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