Masking Semi-Structured Data with Snowflake

Snowflake recently launched dynamic data masking, an incredibly useful feature for companies and data-centric organizations that have strict security data governance requirements. This article demonstrates how we implemented data masking at Snowflake by introducing a data masking policy on a VARIANT data type field that holds data in JSON format. We implemented the policy on top of tables and views.

Sunny Bedi Explains His Many Roles At Snowflake | Rise of The Data Cloud | Part 1 | Snowflake

Sunny Bedi, CIO and CDO of Snowflake, talks about how Snowflake is a data-driven company, data security in the cloud, how to use AI to minimize data threats, and much more. Rise of the Data Cloud is brought to you by Snowflake.

Cloudera Flow Management Continuous Delivery Architecture

Having introduced the flow delivery challenges and corresponding resolutions in the first article ‘Cloudera Flow Management Continuous Delivery while Minimizing Downtime’, we will combine all the preceding solutions into an example of flow management continuous delivery architecture. DataFlow Continuous Delivery Architecture In the whole process, we can see the following steps.

Data and Customer Privacy: What Companies Need to Do

Today’s Data Privacy Day offers consumers an opportunity to learn about how companies use, collect, and share their personal information. At the same time, it gives companies a chance to focus on and highlight how they are protecting customer data. Although most businesses view data privacy practices as a way to mitigate their risk, good practices around data privacy can actually differentiate your organization from your competitors.

Minding the gaps in your cloud migration strategy

As your organization begins planning and budgeting for 2021 initiatives, it’s time to take a critical look at your cloud migration strategy. If you’re planning to move your on-premises big data workloads to the cloud this year, you’re undoubtedly faced with a number of questions and challenges.

Retailers find flexible demand forecasting models in BigQuery ML

Retail businesses understand the value of demand forecasting—using their intuition, product and market experience, and seasonal patterns and cycles to plan for future demand. Beyond the need for forecasts that are as accurate as possible, modern retailers also face the challenge of being able to perform demand planning at scale.