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Introduction to Datasets in BigQuery
- BigQuery is a serverless data warehouse from Google Cloud for running fast SQL queries on large datasets.
- A Dataset in BigQuery is a logical container holding tables, views, and models within a Google Cloud project.
- Benefits of using datasets include organization, access control, and defining geographical location for data.
- Creating a dataset in BigQuery involves steps like logging in, accessing BigQuery Studio, and defining dataset settings.
- Choosing the right location type for a dataset (Region or Multi-Region) impacts data storage and processing.
- Google Managed Encryption keys and Customer-managed encryption keys are options for securing data in BigQuery.
- Case Insensitive and Default Collation settings in BigQuery affect how string values are sorted and compared.
- Storage billing models in BigQuery include Logical Bytes and Physical Bytes, impacting how storage costs are calculated.
- Time Travel Window in BigQuery allows accessing previous data versions for recovery within a specified timeframe.
- Creating and managing datasets effectively in BigQuery is essential for data organization and access control.
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