BigQuery is two services, a front-end service that does analysis, and a back-end service that does storage. It offers near real time analysis of massive datasets. The data storage is durable and inexpensive, and you could connect and work with different datasets to drive new insights and business value. BigQuery uses SQL for queries, so it's immediately usable by many data analysts. BigQuery is fast, but how fast is fast? Well, if you're using it with structure data for analytics, it can take a few seconds. BigQuery connects to many services for flexible ingest and output. And it supports nested and repeated fields for efficiency, and user-defined functions for extensibility. Exempt it, access control, and BigQuery is at the project and the data set level. Here is a major design tip, separate, compute, and processing from storage and database enables serverless operations. BigQuery has its own analytic SQL Query front-end available in console and from the command line with BQ. It's just a query engine, the back-end data warehouse part of BigQuery stores data in tables. But BigQuery also has a connector to Cloud Storage, this is commonly used to work directly with CSV files. BigQuery has a connector to Cloud Bigtable as well, if you need more capabilities than a query engine, consider Cloud Dataproc or or Cloud Dataflow. What makes this all possible is the cloud network with petabit speeds. It means that storing data in a service like cloud storage can be almost as fast and in some cases faster than storing the data locally, where it will be processed. In other words, the network turns the concept of Hadoop and HDFS upside down. It's more efficient, once again, to store the data separate from the processing resources. Now we're starting to explore how all these platform parts fit together to create really flexible and robust solutions. Cloud Dataproc can use cloud storage in place of HDFS for persistent data. If you use cloud storage you can, a, shut down the cluster when it's not actually processing data. And, b, start up a cluster per job or per category of work so you don't have to tune the cluster to encompass different kinds of jobs. Cloud Bigtable is a drop-in replacement for Hbase, again separating state from the cluster so the cluster can be shut down when not in use and startup to run a specific kind of job. Cloud Dataproc and Cloud Dataflow can output separate files as CSV files in Cloud Storage. In other words, you can have a distributed set of nodes or servers processing the data in parallel and writing the results out in separate small files. This is an easy way to accumulate distributed results for later collating. Access any storage service from any data processing service. Cloud Dataflow is an excellent ETL solution for BigQuery. Use Cloud Dataflow to aggregate data in support of common queries.