The ability to discover insights from past events, transactions and interactions is how many customers currently utilize Qlik. Qlik’s unique approach to Business Intelligence (BI) using an in-memory engine and intuitive interface has democratized BI for typical business users, who usually have little to no technical savvy. But, for many years, organizations have only been able to analyze metrics or KPIs of “what has happened” (i.e., descriptive analytics).

There are lessons to be learned from the brick and mortar or pure-play digital retailers that have been successful in the Covid-19 chaos. As the pandemic’s stress test of e-commerce, in-store insights, supply chain visibility, and fulfillment capabilities have revealed shortcomings, and long-lasting consumer experiences— it has also allowed many companies to pivot to very successful strategies built on enterprise data and the digitization efforts that accompany it.

Apache Hadoop Distributed File System (HDFS) is the most popular file system in the big data world. The Apache Hadoop File System interface has provided integration to many other popular storage systems like Apache Ozone, S3, Azure Data Lake Storage etc. Some HDFS users want to extend the HDFS Namenode capacity by configuring Federation of Namenodes. Other users prefer other alternative file systems like Apache Ozone or S3 due to their scaling benefit.

Augmented analytics uses emerging technologies like automation, artificial intelligence (AI), machine learning (ML) and natural language generation (NLG) to automate data manipulation, monitoring and analysis tasks and enhance data literacy. In our previous blog, we covered what augmented analytics actually is and what it really means for modern business intelligence.

This is the first installment in a short series of blog posts about security in Apache Kafka. In this article we will explain how to configure clients to authenticate with clusters using different authentication mechanisms.

Although the issue of silos in IT and data management are well known, companies appear to be falling back into this trap by not distributing their artificial intelligence (AI) and machine learning (ML) capabilities across their business. New research from Qlik and IDC revealed that just 20 percent of businesses widely distribute these capabilities across the organization.