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.