Replication (covered in this previous blog article) has been released for a while and is among the most used features of Apache HBase. Having clusters replicating data with different peers is a very common deployment, whether as a DR strategy or simply as a seamless way of replicating data between production/staging/development environments.
An exceptional embedded analytics offering is underpinned by the right strategy and framework - and this starts with a clear vision. To maximize the value of data assets, you may need to recognize and then address where your product may need to improve it’s BI maturity level. To do this, it’s time to focus on where your analytics development capability and tooling is today.
Unravel Data helps a lot of customers move big data operations to the cloud. Chris Santiago is Global Director of Solution Engineering here at Unravel. So Unravel, and Chris, know a lot about what can make these migrations fail. Chris and intrepid Unravel Data marketer Quoc Dang recently delivered a webinar, Reasons why your Big Data Cloud Migration Fails and Ways to Overcome. You can view the webinar now, or read on to learn more about how to overcome these failures.
Incredibly excited about today’s news that Qlik has acquired Blendr.io. Blendr.io’s easy-to-use, scalable and secure embedded integration and automation platform (iPaaS) will expand our ability to deliver on our vision of Active Intelligence, where real-time, up-to-date data triggers immediate action to accelerate business value across the entire data and analytics supply chain.
The Insurance industry is in uncharted waters and COVID-19 has taken us where no algorithm has gone before. Today’s models, norms, and averages are being re-written on the fly, with insurers forced to cope with the inevitable conflict between old standards and the new normal.
The only certainty in today’s world is change. And nowhere is that more apparent than in the way organizations consume data. A typical company might have thousands of analysts and business users accessing dashboards daily, hundreds of data scientists building and training models, and a large team of data engineers designing and running data pipelines. Each of these workloads has distinct compute and storage needs, and those needs can change significantly from hour to hour and day to day.
It’s time, again, to look at a chart that you might not be using, but that you definitively should consider using when doing data visualization. The waterfall chart is great at visualizing the cumulative effect from positive and negative changes, as, for example, you would see in a Profit and Loss (P&L) report.
Today we are introducing a new series of blog posts that will take a look at recent enhancements to Apache Impala. Many of these are performance improvements, such as the feature described below which will give anywhere from a 2x to 7x performance improvement by taking better advantage of all the CPU cores. In addition, a lot of work has also been put into ensuring that Impala runs optimally in decoupled compute scenarios, where the data lives in object storage or remote HDFS.
