## Introduction
[BigQuery](https://cloud.google.com/bigquery) is Google Cloud's fully managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google's infrastructure. It is designed to handle massive datasets, making it ideal for real-time analytics and business intelligence applications.
![[Tooling BigQuery.png]]
*BigQuery datawarehouses showing a demonstration project and underlying datasets*
## Features
- **Serverless Architecture**: No need to manage infrastructure, scaling automatically to meet demand.
- **High-Speed Processing**: Uses Google's Dremel technology for distributed query execution.
- **Standard SQL Support**: Compatible with ANSI SQL for ease of use.
- **Seamless Integration**: Works with various Google Cloud services and external tools like Looker, Tableau, and Power BI.
- **Security & Compliance**: Built-in security features like IAM roles, encryption, and data governance.
- **Real-Time Analytics**: Supports streaming data ingestion through Pub/Sub.
- **Cost Efficiency**: Pay-as-you-go model with optimized storage pricing and query caching.
## Applications
- **Business Intelligence**: Enables deep insights by integrating with visualization tools.
- **Real-Time Analytics**: Processes live data streams for quick decision-making.
- **Data Warehousing**: Consolidates structured and semi-structured data in a scalable way.
- **Machine Learning**: Native integration with BigQuery ML for predictive analytics.
- **IoT Analytics**: Handles high-velocity data from IoT devices efficiently.
## Best Practices
- **Partitioning & Clustering**: Optimize query performance and reduce costs by logically organizing data.
- **Query Optimization**: Use `EXPLAIN` and `dry-run` queries to analyze costs before execution.
- **Data Governance**: Implement IAM roles and column-level security for controlled access.
- **Storage Efficiency**: Leverage automatic table expiration for cost control.
- **Streaming vs. Batch Processing**: Choose the right ingestion method based on data latency needs.
- **Monitoring & Auditing**: Utilize Stackdriver Logging and Audit Logs for tracking performance and security events.
## Pricing
Google BigQuery offers flexible [pricing models](https://cloud.google.com/bigquery/pricing) to accommodate various data analytics needs:
1. **On-Demand Pricing**: Charges are based on the amount of data processed by each query, at a rate of $6.25 per terabyte (TB). The first 1 TB of query data processed each month is free.
2. **Flat-Rate Pricing**: Ideal for organizations seeking predictable costs, this model allows the purchase of dedicated query processing capacity, known as slots. Pricing varies based on commitment duration:
- **Monthly**: $2,000 for 100 slots.
- **Annual**: $1,700 per month for 100 slots.
- **Flex Slots**: $0.04 per slot hour, with a minimum commitment of 60 seconds.
3. **Storage Costs**: Data storage is billed at $0.02 per gigabyte (GB) per month for active storage (tables modified in the last 90 days) and $0.01 per GB per month for long-term storage (tables not modified for over 90 days). The first 10 GB of storage each month is free.
4. **Data Ingestion and Extraction**:
- **Loading Data**: Batch loading from sources like Google Cloud Storage is free.
- **Streaming Inserts**: Ingesting data via streaming costs $0.01 per 200 MB.
- **Data Export**: Exporting data to Google Cloud Storage is free.
## Usage
The BigQuery serverless datawarehouse is used in almost all the data architectures, due to its ease of use, low-costs with low processing loads and fast response times. The raw data is loaded into the BigQuery datawarehouse using [[Hevo]] and/or [[Tooling/Extraction/Airbyte]], with the downstream semantics models in [[Cube]] and [[PowerBI]] running query-requests whenever a data-consumer demands an insight or asks a question through an [[Analytics Agent]].