Organizations are increasingly investing in modern cloud warehouses and data lake solutions to augment analytics environments and improve business decisions. The business value of such repositories increases as customer relationship data is loaded and additional insights are generated.

Over the past few weeks, we have been publishing videos and blogs that walk through the fundamentals of architecting and administering your BigQuery data warehouse. Throughout this series, we have focused on teaching foundational concepts and applying best practices observed directly from customers. Below, you can find links to each week’s content: Query Processing : Ever wonder what happens when you click “run” on a new BigQuery query?

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.