BigQuery is capable of some truly impressive feats, be it scanning billions of rows based on a regular expression, joining large tables, or completing complex ETL tasks with just a SQL query. One advantage of BigQuery (and SQL in general), is it’s declarative nature. Your SQL indicates your requirements, but the system is responsible for figuring out how to satisfy that request. However, this approach also has its flaws - namely the problem of understanding intent.

Over the past few years, many organizations have experienced the benefits of migrating their SAP solutions to Google Cloud. But this migration can do more than reduce IT maintenance costs and make data more secure. By leveraging BigQuery, SAP customers can complement their SAP investments and gain fresh insights by consolidating enterprise data and easily extending it with powerful datasets and machine learning from Google.

Chronicle, Google Cloud’s security analytics platform, is built on Google’s infrastructure to help security teams run security operations at unprecedented speed and scale. Today, we’re excited to announce that we’re bringing more industry-leading Google technology to security teams by integrating Chronicle with Looker and BigQuery.

At Crux Informatics, our mission is to get data flowing by removing obstacles in the delivery and ingestion of data at scale. We want to remove any friction across the data supply chain that stops companies from getting the most value out of data, so they can make smarter business decisions. But as you may know, if you’re in the business of data, this industry never stands still. It’s constantly evolving and changing.

Last week in our BigQuery Reference Guide series, we spoke about the BigQuery resource hierarchy - specifically digging into project and dataset structures. This week, we’re going one level deeper and talking through some of the resources within datasets. In this post, we’ll talk through the different types of tables available inside of BigQuery, and how to leverage routines for data transformation.

When it comes to anomaly detection, one of the key challenges that many organizations face is that it can be difficult to know how to define what an anomaly is. How do you define and anticipate unusual network intrusions, manufacturing defects, or insurance fraud? If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML.