Decoding the Language of BigQuery: A Guide to Key Terms
In the ever-expanding realm of data analytics, Google BigQuery stands out as a powerhouse. However, navigating the platform efficiently requires familiarity with its key terminologies. In this article, we’ll decode the language of BigQuery, knowing about essential terms that pave the way for effective data exploration and analysis.
Project
A project is a top-level container in Google Cloud that encompasses various resources, including BigQuery datasets. Projects help in organizing and managing cloud resources.
Dataset
A dataset in BigQuery is a container for tables and views, providing a way to organize and control access to various data components. Datasets are top-level structures within a project.
Table
Tables in BigQuery are structured collections of data, similar to a spreadsheet. They consist of rows and columns, where each column has a specified data type, and each row represents a record.
Query
A query is a request for data or computation issued to a database. In BigQuery, users write SQL queries to retrieve, manipulate, and analyze data stored in tables.
Partitioning
Partitioning is the process of organizing large datasets into smaller, more manageable parts. In BigQuery, partitioning is used to improve query performance by reducing the amount of data scanned.
Clustering
Clustering is a feature in BigQuery that further optimizes query performance by arranging data within partitions based on one or more columns. It helps reduce the amount of data that needs to be scanned during a query.
View
A view is a virtual table based on the result of a SELECT query. It allows users to create a logical representation of the data, simplifying complex queries.
Materialized View
A materialized view is a physical storage structure that contains precomputed results. It provides a way to store and retrieve aggregated or computed data for improved query performance.
Schema
A blueprint or structure that defines the organization of data in a table, specifying the columns, data types, and constraints.
Stored Procedure
A set of SQL statements that can be saved and executed on demand. BigQuery supports scripting using stored procedures.
Streaming Inserts
Streaming inserts in BigQuery enable real-time data ingestion. Data is added to tables immediately as it becomes available, allowing for up-to-the-minute analytics.
Metadata
Metadata in BigQuery includes information about the structure and properties of datasets and tables. It provides details such as column names, data types, and other characteristics.
Wildcard Tables
Wildcard tables allow queries to run across multiple tables with similar names using wildcard characters. This is useful when working with partitioned tables or tables with a common prefix.
Slot
A slot in BigQuery represents a unit of computational capacity required to execute a query. Query processing in BigQuery is managed through a slot-based system.
Reservation
A reservation in BigQuery is a way to allocate dedicated resources to a BigQuery project. Reservations ensure consistent performance by providing a set amount of computational capacity.
On-Demand (Pricing)
On-Demand pricing in BigQuery refers to a flexible billing model where users pay only for the amount of data processed by their queries. There are no upfront costs or ongoing commitments. This pricing model is suitable for users with unpredictable query workloads.
Logical Storage
Logical storage in BigQuery represents the amount of data that users query or interact with through SQL statements. It includes the uncompressed size of the data and is used for calculating query costs. Logical storage is separate from physical storage.
Physical Storage
Physical storage in BigQuery refers to the actual disk space occupied by tables and their associated metadata. It includes the compressed size of the data stored in the system. Physical storage costs are associated with the storage of data in BigQuery, both active and long-term.
Active Storage
Active storage in BigQuery refers to the storage used by tables that have been modified or queried within the last 90 days. Storage costs are associated with active storage, which includes both the logical and physical storage of the data.
Long Term Storage
Long-term storage in BigQuery pertains to the storage used by tables that haven’t been modified or queried for at least 90 days. While active storage incurs higher costs, long-term storage is more cost-effective, making it suitable for archival or less frequently accessed data.
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