From airline tickets going through the roof during holiday seasons to Uber and other ride-sharing services charging higher prices in rush hour, we have become accustomed to paying different prices for the same services. Traditionally, dynamic pricing was a tool reserved for industry giants like Amazon because of its implementation complexity and price tag.

Marketing data integration is the process of combining marketing data from different sources to create a unified and consistent view. If you’re running marketing campaigns on multiple platforms—Facebook, Instagram, TikTok, email—you need marketing data integration. Why? Because being able to assimilate data from different channels and across multiple marketing touchpoints gives you visibility into the overall impact of a campaign, event, or another marketing effort.

Built with BigQuery: How Tamr delivers Master Data Management at scale and what this means for a data product strategy

Do you look at spreadsheets with analysis paralysis? This article will help you simplify the entire process — forever.

There are few topics in the world right now that are hotter than data and its related fields. As technology, machine learning, and computer algorithms continue to expand, so does the way that companies can use this information to benefit their business. Extract, Transform, and Load (ETL) remains one of the most important processes in the area of big data. This area is absolutely booming, and so is the demand to learn more about its various processes and components.

Multivariate time series forecasting allows BigQuery users to use external covariate along with target metric for forecasting.

I just got an email from a venture capitalist. For about the hundredth time, the venture capitalist told me they were anxious to invest money in us. The only qualification was that we needed to already have at least $10 million in sales. If we had $10 million in sales, we wouldn’t need to be talking with the venture capitalist. How stupid is that? I suggested to the venture capitalist that they go invest in IBM or ATT because they do have $10 million in sales.

Do you know where your data is? Most organizations store data in various destinations (in-house databases, SaaS locations, cloud-based apps, etc.), which makes running analytics far more complicated. Imagine pulling data from all these destinations into one data warehouse or data lake. Life would be so much easier... "But doesn't this require a lot of code?" you may ask. Not necessarily.

Google Cloud customers who want app-level encryption in hybrid cloud data warehouses can encrypt and decrypt that data outside BigQuery. Here’s how to do that securely.