Today I’m going to do something different and not talk about a specific visualization as I’ve done in my last blog posts, but rather a technique that we use within visualization. How to create multiple views that each show a different part of your dataset?

We live in a digital age which is increasingly being driven by algorithms and data. All of us, whether at home or work, increasingly relate to one another via data. It’s a systemic restructuring of society, our economy and institutions the like of which we haven’t seen since the industrial revolution. In the business world we commonly refer to it as digital transformation. In this algorithmic world, data governance is becoming a major challenge.

A household name in global media analytics – let’s call them MTI – is using Unravel to support their data operations (DataOps) on Amazon EMR to establish and protect their internal service level agreements (SLAs) and get the most out of their Spark applications and pipelines. Amazon EMR was an easy choice for MTI as the platform to run all their analytics. To start with, getting up and running is simple. There is nothing to install, no configuration required etc.

Meet Dominika Dronska, head of Digital at Abbey Road Studios where the Beatles recorded almost all their albums. Using Flutter, Abbey Road Studios have built the award-winning Topline app that lets musicians record their ideas on the go and share them with each other.

It may be unclear if social shares have a direct impact on Google search rankings, but the indirect benefits of aligning both channels are indisputable.

For me, the most exciting thing about Pipeline Designer is the way that it makes working with streaming data easy. Traditionally this has required a completely different way of thinking if you have come from a "batch" world. So when Pipeline Designer was released, the first thing I wanted to do was to find a good streaming data source and do something fun and interesting with the data. Twitter was my first choice of streaming data.

Modern data apps are increasingly going to the cloud due to their elastic compute demands, skills shortages and the complexity of managing big data on premises. And while more and more organizations are taking their data apps to the cloud to leverage its flexibility, they’re also finding that it is very challenging to assess application needs and how to migrate and optimize their data to ensure performance and cost targets are not compromised.

We are halfway through 2019 and with the third release of the year, our momentum remains strong as we continue to deliver significant product innovations for our customers across the Qlik portfolio.

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