GOOGLE BIG QUERY
- 1. Google Big Query DATA WAREHOUSING AND DATA MINING
- 4. • Google Big Query came into effect from 2011 • Serverless, highly scalable data warehouse. • Helps you manage and analyze your data with built-in features. • Big Query was designed for analyzing data on the orders of billions of rows, using SQL-like syntax. • Enables data scientists to build and operationalize ML models on planet- scale structured data
- 5. BigQueryArchitecture • Big Query’s serverless architecture decouples storage and compute and allows them to scale independently on demand. • This is very different from traditional node-based cloud data warehouse solutions or on-premise massively parallel processing (MPP) systems. • The main component of Big Query architecture is called Dremel
- 7. STORAGEINBIGQUERY • BigQueryResourceModel • Storage Management
- 9. BigQueryFunctions • User-defined functions • Aggregate functions • Numbering functions • Conversion functions • Mathematical functions • Navigation functions • Array functions • JSON functions
- 11. • Cost-effective and highly scalable ‘NoOps’ warehousing solution • Streamlined analytics with ETL compatibility and BI connectors • Creates and Test ML models • Focusing on innovation, not maintenance • Built-in DR and data protection for stronger data security
- 13. How does Data Warehouse drive business decisions? • 360 degree view of businesses. • Awareness of real time business events. • Reduce time to insights and make them available to business users. • Secure their data and govern its use.
- 15. • Google Big Query fits corporations with varied workloads and provides opportunities for querying large data sets. • What we like the most is that you don’t have to worry about any infrastructure problems and maintenance.