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.
For the last few years, microservices have been gaining popularity as the software architecture pattern of the day. But even as enterprises grapple with how they can undergo “digital transformation,” some startups are looking back to their monolithic roots. Software Engineer Alexandra Noonan topped Hacker News in July with a blog post about Segment’s journey to microservices and back again.
When I attended the Pacific Northwest BI & Analytics Summit this summer, we had a great discussion about data interpretation and the way organizations consume data. One of the things that came up was how the industry has been so focused on using dashboards as the delivery mechanism for analytics that we’ve lost the art of long-form analysis.
