Introduction to Google BigQuery

1. Google BigQuery is a serverless, highly scalable data warehouse that comes with a built-in query engine.2)
2. With this Power packed Query Engine we can run SQL queries on Terabytes and Petabytes of data in a matter of seconds and minutes respectively

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3. Technology like Biquery is a boon to Startup because they can now work on Software development with out having to worry about Infrastucture and Clusters

4. In BigQuery, data is organized as Tables, Rows, and Columns.

5. BigQuery uses columnar storage to achieve high compression ratio and is efficient in executing ad hoc queries where as traditional OLTP databases are bad at ad hoc queries

6.The hierarchy in BigQuery is Project | Datasets | Tables. Under a project, datasets can be created. Datasets are containers for tables

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As you can see stone-composite-27290 is my Project . We have to create a dataset that can hold tables . So I have created dataset called My_First_dataset using CREATE DATASET option. Under that I have created a table using CREATE TABLE option . Below are the various options I have selected to upload data in to my newly created table

7. The best thing about BigQuery is that it will show the amount of data it will process before hand itself based on the SELECT query written with out actually running it as highlighted in Green below

8. We can use BigQuery and run SQL queries against files that are stored on Google cloud Storage as well with out ingesting in to Bigquery native storage

9. BigQuery supports geospatial data and hierarchical data

10. BigQuery supports the ingest both of batch data and of streaming data

11. Queries in Bigquery are automatically scaled to thousands of machines and executed in parallel. You don’t need to do anything special to enable this massive parallelization.

12. BigQuery operations are ACID