1. Characteristics
   1. Google's NoSQL, big data service
   2. Delivers big data analysis and interactive data query
   3. Does not support multi-row transactions
   4. Supports SQL queries of large datasets with a pay-as-you-go model
   5. Enables users to focus on analyzing data to find meaningful insights
   6. Suitable for interactive querying in an online analytical processing system
   7. Used by all types of organizations from startups to Fortune 500 companies
   8. BigQuery users can easily read and write data using Cloud Dataflow, Hadoop and Spark
   9. With BigQuery, there is no infrastructure to manage
   10. Where expiration period property is set after a dataset is created, only new tables are deleted after the expiration period
   11. Where expiration period property is set when data-set is created, any table created in the dataset is deleted after the expiration period
2. Storage
   1. Ideal for storing a large amount of structured objects
   2. Fully manage petabyte scale, low cost analytics data warehouse
   3. Data can be easily loaded into BigQuery from cloud storage
3. Performance
   1. BigQuery users can run super fast SQL queries against terabytes of data in seconds
4. Location
   1. Google's infrastructure is global and so is BigQuery
   2. BigQuery enables users to specify the region where their data will be kept
5. Billing
   1. BigQuery enables users to pay for data storage separately from queries
   2. BigQuery users can share data sets with users in other projects
   3. The user of the dataset is responsible for the cost of their own queries
   4. Long term storage pricing is automatically applied to data residing in BigQuery
   5. BigQuery users pay for queries only when they are running
   6. When the age of data reaches 90 days in BigQuery, Google automatically drop the price of storage
   7. Storage costs and usage can be controlled and optimized by setting the default table exploration for newly created tables in a dataset
6. Security
   1. BigQuery IM roles can be used to ensure users are provided with only the permissions that align with their job function
   2. It is recommended to separate who is allowed to create and manage datasets from those who can query the datasets and process the data
   3. BigQuery authorized view can be used to limit users to see only a subset of the data
   4. The principle of least privilege should be followed when providing access to sensitive data