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What is BigQuery & Data Warehouse?
A comprehensive guide to BigQuery and Data Warehousing
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Overview
Let’s explore BigQuery, Google’s fully managed serverless data warehouse service, and understand its key features and benefits.
What is BigQuery?
BigQuery is Google’s fully managed serverless enterprise data warehouse service.
It comes with a built-in query engine that makes it easy to execute SQL queries and helps organizations analyze large amounts of data to find meaningful insights.
What is a Data Warehouse?
A Data Warehouse (DW) is a large centralized data repository that supports business intelligence (BI) activities, including data analysis, reporting, and decision-making.
Data is structured and organized to support business intelligence activities such as data mining, analytics, and reporting.
Why Do We Need a Data Warehouse?
Although you could create analysis-specific database copies, this process can be cumbersome and inefficient.
Modern enterprises need:
- Efficient analysis through enterprise-wide data integration
- Quick and accurate decision-making capabilities
- Effective handling of rapidly growing data volumes
- Efficient data access for users across different organizational levels
Key Features and Benefits of BigQuery
1. Fully Managed Serverless
- Google manages the underlying infrastructure
- No need to worry about server setup or management
- Enables seamless scalability and simplified operations
2. High-Speed Real-Time Analysis
- Can scan billions of rows in seconds
- Designed for fast and cost-effective SQL queries
- Provides real-time analytics through streaming capabilities
3. Cost-Effective
Pricing Structure:
- Free: First 10GB storage per month
- Storage: $0.02 per GB after free tier
- Queries: First 1TB per month free, then $5 per TB
- Charges based on:
- Storage used
- Amount of data processed by queries
- Not charged for query runtime or compute resources
4. Security & Integration
- Strong security, privacy, and compliance measures
- Data encryption and access control
- Seamless integration with:
- ETL tools
- Data visualization tools
- Other Google Cloud services (Dataflow, Pub/Sub)
5. Machine Learning & Data Sharing
- BigQuery ML enables building ML models using SQL
- Easy sharing of insights with team members
- Collaborative features for datasets, queries, and views
Summary
In the next posting, we will explain the Data ETL process using BigQuery and DataFlow.
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