Digging into SQL with BigQuery. In this article, I’m digging into how I got started using SQL with Google’s BigQuery tool.
Working with SQL and understanding the data that is all around you is very important to be successful in today’s data-oriented business world.
BigQuery is available via a web-based UI so you can access your data and run queries via your browser and all you need is an internet connection. BigQuery does support other methods of access as well — the bq command-line tool and API access with a variety of client libraries. For the purposes of this article, I’ll stick to the web-based UI.
Another huge benefit of just getting starting with SQL is that BigQuery offers free public datasets (with 1TB of querying included) which you can utilize to hone your skills.
For our purposes, we will look at the Google Analytics Sample dataset. It just so happens as an added benefit Google offers sample queries in conjunction with this dataset.
SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Working with SQL on nested data in BigQuery can be very performant. But what if your data comes in flat tables like CSV’s?
What I wish I knew when getting started with SQL. Structured Query Language or SQL — “sequel” — is one of the most important tools in the shed of today's data-oriented business.
As a Google Cloud consultant focused on the Data & Analytics space, I spend a lot of time with BigQuery, Google Cloud’s petabyte-scale data warehouse-as-a-service, and for the most part, I love it.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.