French multinational automotive manufacturer Renault Group has been investing in Industry 4.0 since the early days. A primary objective of this transformation has been to leverage manufacturing and industrial equipment data through a robust and scalable platform. Renault designed an industrial data acquisition layer and connected it to Google Cloud, using optimized big data products and services that together form Renault's Industrial Data Platform.

Last week, we shared information on BigQuery APIs and how to use them, along with another blog on workload management best practices. This blog focuses on effectively monitoring BigQuery usage and related metrics to operationalize workload management we discussed so far.

In the most recent season of BigQuery Spotlight, we discussed key concepts like the BigQuery Resource hierarchy, query processing, and the reservation model. This blog focuses on extending those concepts to operationalize workload management for various scenarios.

So far in this series, we’ve been focused on generic concepts and console-based workflows. However, when you’re working with huge amounts of data or surfacing information to lots of different stakeholders, leveraging BigQuery programmatically becomes essential. In today’s post, we’re going to take a tour of BigQuery’s API landscape - so you can better understand what each API does and what types of workflows you can automate with it.

If the COVID-19 pandemic has taught us anything, it is that speed and intelligence are of the essence when it comes to making business decisions. Organizations must find ways of keeping ahead of competitors and disruptions by continually leveraging data to make smart decisions. The problem? Data may be everywhere, but it’s not always available in a form that businesses can use to generate analytics in real time.

Hopefully you’ve been following along with our BigQuery Admin series and are well on your way to getting ramped up with BigQuery. Now that you’re equipped with the fundamentals, let's talk about something that’s relevant for all data professionals - data governance.

Last week in the BigQuery reference guide, we walked through query execution and how to leverage the query plan. This week, we’re going a bit deeper - covering more advanced queries and tactical optimization techniques. Here, we’ll walk through some query concepts and describe techniques for optimizing related SQL.

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.