New year, and time for the annual trends webinar. It’s the third time I have the honor to present it. As always, it takes a village to create this, and I’m thankful for all the people who chipped in. I’d like to highlight in particular Murray Grigo McMahon. His blog on data as an ecosystem, as well as great discussions, was a big source of inspiration, and will feature in the trends. This year, the title of the webinar is “The Dawn of Postmodern Analytics”.
Google BigQuery is a serverless enterprise data warehouse tool that’s designed for scalability. We built BigQuery to be highly scalable and let you focus on data analysis without having to take care of the underlying infrastructure. We know BigQuery users like its capability to query petabyte-scale datasets without the need to provision anything. You just upload the data and start playing with it.
Some products you build because you want to. Others you build because you have to. In the case of Bugfender’s brand-new plug-in for React Native, it was definitely the latter. Like the rest of the development world, we’ve come to rely on this cross-platform framework to build killer apps across Android, iOS and web. But that’s not why we’ve built our plug-in. No, it’s because our users were building their own versions!
Earlier this year, I attended the Pacific Northwest BI & Analytics Summit. It’s a relatively small industry event that brings together some thought leaders, industry analysts and representatives from major vendors who are all intensely passionate about the BI space. Some of the people who were there include Donald Farmer, Doc Searls, Jill Dyche, Claudia Imhoff, Mike Ferguson and Shaun Rodgers.
Chris Kappen joins the Agency Spotlight to share how Epicosity’s client reporting process has evolved since implementing Databox.
Today at KubeCon, we announced the launch of Kong Cloud – a fully managed version of Kong Enterprise designed to accelerate large organizations’ digital transformation initiatives. With Kong Cloud, customers can instantly start building cloud native services and connect all their services across different environments, vendors and platforms.
When it comes to Data Matching, there is no ‘one size fits all menu’. Different matching routines, different algorithms and different tuning parameters will all apply to different datasets. You generally can’t take one matching setup used to match data from one distinct data set and apply it to another. This proves especially true when matching datasets from different regions or countries. Let me explain.
As enterprises move towards massively scaled interconnected software systems, they are embracing the cloud like never before. Very few would dispute the notion that the cloud has become one of the biggest drivers of change in the enterprise IT landscape and that the cloud has provided IT a powerful way to deploy services in a timely and cost-effective manner.
Can you believe it’s almost 2019? We can’t either, but we’re excited it’ll be another big year for developers. According to the U.S. Bureau of Labor Statistics, due to increased demand for software solutions, developer employment is forecasted to increase 24% from 2016 to 2026 (Qlik’s contributing with these worldwide openings). To compare, the average growth rate for all occupations is 7%.
The challenge today for big data is that 85% of on-premises based Big Data projects fail to meet expectations and over 2/3 of Big Data potential is not being realized by organizations. Why is that you ask? Well, simply put on-premises “Big Data” programs are not that easy.
