BigQuery is Google's flagship data analytics offering, enabling companies of all sizes to execute analytical workloads. To get the most out of BigQuery, it’s important to understand and monitor your workloads to keep your applications running reliably. Luckily, with Google’s INFORMATION_SCHEMA views, monitoring your organization’s use at scale has never been easier. Today, we’ll walk through how to monitor your BigQuery reservation and optimize performance.

Chances are, your data contains information about geographic locations in some form, whether it’s addresses, postal codes, GPS coordinates, or regions that are meaningful to your business. Are you putting this data to work to understand your key metrics from every angle? In the past, you might’ve needed specialized Geographic Information System (GIS) software, but today, these capabilities are built into Google BigQuery.

A quick and easy way to speed up small queries in BigQuery (such as to populate interactive applications or dashboards) is to use BI Engine. The New York Times, for example, uses the SQL interface to BI Engine to speed up their Data Reporting Engine. To Illustrate, I’ll use three representative queries on tables between 100 MB and 3 GB — tables that are typically considered smallish by BigQuery standards.

When looking at data, business decision makers are often blocked by an intermediate question of "What should I take away from this data?" Beyond putting together the numbers and building the results, data analysts and data scientists play a critical role in helping answer this question. Organizations big and small depend on data analysts and data scientists to help “translate from words to numbers, and then back to words” as sports analytics pioneer Dean Oliver once said.

Spring is here. Clocks move forward. The Sakura (cherry blossom) festival in Japan marks the celebration of the new season. In India, the holi festival of colors ushers in the new harvest season. It’s a time for renewal and new ways of doing things. This month, we are pleased to debut our newest set of SQL features in BigQuery to help our analysts and data engineers spring forward.

When doing analytics at scale with BigQuery, understanding what is happening and being able to take action in real-time is critical. To that end, we are happy to announce Resource Charts for BigQuery Administrator. Resources Charts provide a native, out-of-the-box experience for real-time monitoring and troubleshooting of your BigQuery environments.

We are thrilled to announce that Google has been named a Leader in The Forrester Wave™: Cloud Data Warehouse, Q1 2021 report. For more than a decade, BigQuery, our petabyte-scale cloud data warehouse, has been in a class of its own. We're excited to share this recognition and we want to thank our strong community of customers and partners for voicing their opinion. We believe this report validates the alignment of our strategy with our customers’ analytics needs.

The Google Cloud Public Datasets program recently published the Python Package Index (PyPI) dataset into the marketplace. PyPI is the standard repository for Python packages. If you’ve written code in Python before, you’ve probably downloaded packages from PyPI using pip or pipenv. This dataset provides statistics for all package downloads, along with metadata for each distribution. You can learn more about the underlying data and table schemas here.

Many people think of Cloud Run just as a way of hosting websites. Cloud Run is great at that, but there's so much more you can do with it. Here we'll explore how you can use Cloud Run and BigQuery together to create an inventory management system. I'm using a subset of the Iowa Liquor Control Board data set to create a smaller inventory file for my fictional store. In my inventory management scenario we get a csv file dropped into Cloud Storage to bulk load new inventory.

Today, we are excited to announce the general availability (GA) of BigQuery materialized views. Materialized views (MV’s) are precomputed views that periodically cache results of a query to provide customers increased performance and efficiency.