How to Use BigQuery for Data Analysis and Machine Learning

BigQuery is a fully managed and serverless enterprise data warehouse that allows you to store and analyze large amounts of data with speed and scalability. BigQuery supports all data types, works across clouds, and has built-in features like machine learning, geospatial analysis, and business intelligence. In this blog post, we will show you how to use BigQuery for data analysis and machine learning, using some examples and best practices.

Data analysis with BigQuery

To perform data analysis with BigQuery, you need to load your data into BigQuery tables, write SQL queries to explore and transform your data, and visualize or export your results. You can use the Google Cloud console, the BigQuery command-line tool, or various client libraries to interact with BigQuery. You can also use third-party tools and utilities that support ODBC or JDBC drivers.

BigQuery offers several ways to load your data, depending on the source, format, and size of your data. You can stream data in real-time with the Storage Write API, batch-load data from local files or Cloud Storage using formats like CSV, JSON, Avro, Parquet, etc., or query data from external sources like Cloud SQL, Cloud Firestore, or Google Sheets using federated queries. You can also use Dataflow or Dataprep to create pipelines that ingest and process your data before loading it into BigQuery.

Once your data is loaded into BigQuery tables, you can write standard SQL queries to analyze it. BigQuery supports a rich set of SQL functions and operators, including window functions, array functions, user-defined functions, and scripting. You can also use BigQuery ML to create and apply machine learning models using SQL. BigQuery has a powerful query engine that can handle terabytes of data in seconds and petabytes of data in minutes. You can optimize your queries by using best practices like partitioning, clustering, materialized views, caching, etc.

To visualize or export your query results, you can use various options provided by BigQuery or Google Cloud. For example, you can use BigQuery BI Engine to enable fast and interactive analysis with tools like Looker Studio or Google Data Studio. You can also use Connected Sheets to analyze BigQuery data in Google Sheets. Alternatively, you can export your query results to Cloud Storage or other destinations using jobs or transfers.

Machine learning with BigQuery

BigQuery ML is a feature of BigQuery that allows you to create and apply machine learning models using SQL. You can use BigQuery ML to perform tasks like regression, classification, clustering, anomaly detection, time series forecasting, natural language processing, computer vision, etc. You can also use BigQuery ML to train custom models using TensorFlow or AutoML Tables.

To use BigQuery ML, you need to have your training data in a BigQuery table or view. You can then use the CREATE MODEL statement to define your model type, parameters, and options. You can also specify a validation strategy to evaluate your model performance. After creating your model, you can use the ML.EVALUATE function to get metrics like accuracy, precision, recall, etc. You can also use the ML.EXPLAIN function to get feature importance scores for your model.

To apply your model to new data, you can use the ML.PREDICT function to get predictions for each row of a query result. You can also export your model for online prediction using Vertex AI or your own serving layer. To manage your models, you can use the SHOW MODEL statement to get information about your model properties and training statistics. You can also use the DROP MODEL statement to delete your model when you no longer need it.


BigQuery is a powerful tool for data analysis and machine learning that offers many benefits like scalability, performance, flexibility, and simplicity. By using BigQuery for your data needs, you can save time and resources while gaining insights from your data. If you want to learn more about BigQuery and its features, you can check out the official documentation or follow some tutorials online.

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