BigQuery is a fast, highly scalable, cost-effective, and fully-managed enterprise data warehouse for analytics at any scale. As BigQuery has grown in popularity, one question that often arises is how to copy tables across locations in an efficient and scalable manner. BigQuery has some limitations for data management, one being that the destination dataset must reside in the same location as the source dataset that contains the table being copied.

Continuing my series of blogs examining the products, services and client benefits borne out of Yellowfin’s partnerships, I’m pleased to introduce our new fintech partner, ChartIQ. ChartIQ provides HTML5 components and the Finsemble integration platform to banks, brokerages, trading platforms, and financial portals worldwide.

I’m seated here on the balcony of our rented apartment in Chamonix looking up at Mont Blanc. It’s an incredible view but its beauty is deceiving and as anyone who has climbed knows, all too often the summit is not the one you can see. In the case of Mont Blanc, the perspective is such that the Dôme du Goûter seen in the foreground often looks bigger. I can assure you it’s not.

Dozens of marketers share the data behind their average session duration as well as the strategies that work for improving it.

Developers are a fussy bunch. We sweat on the tiniest details and every aspect of our product has to be just right. We don’t like entrusting our precious designs to anyone outside our inner circle. Yet we’re increasing delegating key quality assurance (QA) tasks to robots. The market for automated testing products is expected to be worth $20 billion by 2023 – three times as much as now.

When working with tables in data warehouse environments, it is fairly common to come across a situation in which you need to generate surrogate keys. A surrogate key is a system-generated identifier that uniquely identifies a record within a table. Why do we need to use surrogate keys? Quite simply: contrary to natural keys, they persist over time (i.e. they are not tied to any business meaning) and they allow for unlimited values.

Today, Docker or Kubernetes are obvious choices. But, back in 2015, these technologies were just emerging and hoping for massive adoption. How do tech companies make the right open source technology choices early?

Visualize project management metrics from Harvest like hours tracked, billable hours, billable amount, non-billable hours, expenses (and more) in minutes.

One question that I’m often asked is ‘how do I justify funding for a BI project?’ The answer I give is that to win funding you need to stop thinking about BI as a project.

Every now and then you can't beat a bit of Meat Loaf, the singer not the food, as I've not had the pleasure to taste it. I recently found myself recalling a cult classic "Standing on the outside" not because of any failed break up but thinking about the abundance of data available externally that can be used in combination with your internal data. Unfortunately, many are still leaving this data standing on the outside.