Big Query - GCP

• What is BigQuery?
• How does BigQuery work?
• BigQuery administration and access
• BigQuery best practices and cost management
• Extending your data

we use the big query to Ingest, store, visulize and analyse the data,

unlock insights across the petabytes of the data.

You can ingest data into BigQuery by uploading it in one-time or scheduled batch loads or by streaming data to enable real-time insights

BigQuery is known for being a serverless, highly scalable, cost-effective cloud data warehouse.

flexibility and cost control for data analytics and data science workloads.

utilized is BigQuery – Dremel, Colossus, Borg, and Jupiter

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Dremel is the service that turns SQL queries into execution trees.

Colossus is our cluster-level file system, successor to the Google File System (GFS). ?

Borg is the service that paved the way for Kubernetes at Google

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BigQuery ML or BQML is a machine learning service that can be used to build and deploy machine learning models in BigQuery using SQL queries.

• Forecasting with linear regression models. Predict the sales of an item on a given day.
• Classification with binary and multiclass logistic regression. Determine whether a customer will make a purchase.
• Data segmentation with K-means clustering. Identify customer or user segments.
• Creating product recommendation systems using matrix factorization.
• Time series forecasts. Create forecasts able to handle seasonality and holidays.
• Identify the relationship between variables with regression models.
• Machine learning on tabular data using Vertex AI AutoML Tables.

BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse that enables businesses to analyze all their data very quickly