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.
Today, companies can measure every aspect of business health, except the health of their data which drives business decisions. Data is vital to inform critical decisions such as identifying new routes to market, systems to support business agility, and more resilient supply chains. As Harvard Business Review puts it, “Your organization’s data is the source of both the opportunity and the challenges to your innovation.
At Google Cloud, we’re invested in building data analytics products with a customer-first mindset. Our engineering team is thrilled to share recent feature enhancements and product updates that we’ve made to help you get even more value out of BigQuery, Google Cloud’s enterprise data warehouse.
We live in a Fourth Industrial Revolution, where data is the lifeblood of business. Those of us who harness the power of artificial intelligence, machine learning and augmented analytics to uncover insights from data are the ones who will be able to find better ways of driving efficiency, productivity and superior business outcomes.
The data age has been marked by numerous “hype cycles.” First, we heard how Big Data, Data Science, Machine Learning (ML) and Advanced Analytics would have the honor to be the technologies that would cure cancer, end world hunger and solve the world’s biggest challenges. Then came third-generation Artificial Intelligence (AI), Blockchain and soon Quantum Computing, with each one seeking that honor.
Data Discovery and Exploration (DDE) was recently released in tech preview in Cloudera Data Platform in public cloud. In this blog we will go through the process of indexing data from S3 into Solr in DDE with the help of NiFi in Data Flow. The scenario is the same as it was in the previous blog but the ingest pipeline differs. Spark as the ingest pipeline tool for Search (i.e.
The new normal has changed the way we work and the way we conduct business. More and more employees are working from home, customers are shopping online, and everyone’s phone is still attached to their ears. Bottom line: everything we’re doing in business and in our personal lives is leaving a digital trail. In fact, now devices are getting in the game and creating more data than people, 277 times more, according to Cisco.
