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Introduction to BigQuery
- A database is an organized collection of data that allows users to store, retrieve, and manage data efficiently, serving as the foundation of most software applications.
- Characteristics of databases include structured data storage, using DBMS, supporting CRUD operations, and ensuring ACID compliance for reliability.
- Types of databases include Relational Databases (RDBMS) and NoSQL databases with subcategories like document-based, key-value stores, columnar stores, and graph databases.
- A Data Warehouse is designed for reporting and data analysis, consolidating large volumes of structured data from multiple sources for analytical queries and historical data storage.
- Data Warehouses differ from databases in purpose, data type, normalization, query type, and user focus.
- Types of Data Warehouses include Enterprise Data Warehouse (EDW), Operational Data Store (ODS), and Data Marts, each serving specific organizational needs.
- Examples of Data Warehouses are Teradata, Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse Analytics.
- BigQuery is a fully managed, serverless, scalable, and cost-effective cloud data warehouse by Google Cloud Platform, designed for business agility and handling massive datasets efficiently.
- Key features of BigQuery include high scalability, super-fast query processing, support for batch and streaming data ingestion, built-in Machine Learning with BigQuery ML, serverless management, and pay-as-you-go pricing.
- BigQuery's out-of-the-box features include GIS analysis, auto backup, integration with other Google Cloud services, support for BI capabilities, programmatic interaction via APIs, enterprise-grade security and compliance, monitoring, logging, alerting, federated queries, running data science workloads, and access to public datasets.
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