Introducing BigQuery ML for building predictive models with SQL
One key to efficient data analysis of big data is to do the computations where the data lives. In some cases, that means running R, Python, Java, or Scala programs in a database such as SQL Server or in a big data environment such as Spark. But that takes some fairly technical programming and data science skills not often found among business analysts or SQL programmers. In addition, if you have to extract, transform, and load your datasets from your data warehouse to another data store for machine learning, you introduce delays in the process.
To that end, Google has announced a beta release of BigQuery ML, a SQL-based extension to its enterprise data warehouse service, for building and deploying machine learning models. This is important because it lowers the barriers to training certain kinds of models and deploying predictive analytic services, both reducing the time required and bringing it within reach of data analysts and statisticians.
