We’re announcing a key capability to help organizations govern their data in Google Cloud. Our new BigQuery table-level access controls (table ACLs) are an important step that enables you to control your data and share it at an even finer granularity. Table ACLs also bring closer compatibility with other data warehouse systems where the base security primitives include tables—allowing migration of security policies more easily.

Banking and financial services organizations are all about customer relationships. By connecting with customers and assisting alongside their financial journeys, these organizations become trusted partners. Building trust and confidence increases the share of wallet and lifetime value. To achieve that on a global scale, you need to leverage big data and predictive analytics. As customers navigate their personal finances, they are looking for a bank they can trust.

Clive Humby stated, as far back as 2006, “data is the new oil.” The quote really took off following this 2017 report from The Economist. As a former chemical process engineer, oil immediately makes me think of refining it. Today’s analytics platform for the complete data lifecycle does the same for data as the refinery distillation columns does for crude oil: distilling value.

Snowflake customer, Merkle Inc., has created a new set of COVID-19 interactive dashboards for businesses to use for free to determine which counties in the U.S. will most likely experience an economic recovery first. As economies reopen, states hit hardest by COVID-19, or states that relax social distancing measures sooner rather than later, will not reveal local market opportunities as they emerge.

In Part 1, we covered the high-level objectives and methods for attacking service accounts. In Part 2 we discuss defense-in-depth mitigations to those methods. By the end of this blog, you will be able to apply secure-by-default mitigations to threats impacting Snowflake service accounts. The following table from Part 1 highlights the objectives and methods we want to mitigate: These secure-by-default mitigations help prevent and constrain credential misuse from theft and guessing attacks.

There’s a lot to track when training your ML models, and there’s no way around it; reviews and comparisons for best performance are virtually impossible without logging each experiment in detail. Yes, building models and experimenting with them is exciting work, but let’s agree that all that documentation can be laborious and error-prone – especially when you are essentially doing data entry grunt work, manually, using Excel spreadsheets.

If you are a software engineer, there's a good chance that deep learning will inevitably become part of your job in the future. Even if you're not building the models that directly use CNNs, you might have to collaborate with data scientists or help business partners better understand what is going on under the hood. In this article, Julie Kent dives into the world of convolutional neural networks and explains it all in a not-so-scary way.