Enterprises today often have to use different database systems to fulfil different purposes, combining data coming from different data sources is not an easy task, while moving data from one source to the other is cost-demanding and requires ETLs and offline batch processing, which are often performed at night. Existing solutions today for polyglot applications often introduces the concept of datalakes or implements a federation on top of different sources in order to provide an easy way for common access. However, all these solutions are referring to different data sources, while for federation they are using technologies like Spark, which can be very resource consuming and cannot exploit the specific capabilities of each different data store.
Insurance companies increasingly need IT data-based solutions in order to address their needs about the provision of services according to the customer “tailored” requirements. The challenge is to allow insurance companies to better develop the customer management, by providing personalized services to the customer, as well as new corporate services for the handling of the customers’ profitability. A multi-channel scenario will be developed by GFT which will facilitate data analytics-powered smart insurance, providing a 360-degree view of the customer and personalized services. GFT will collaborate with HDI Assicurazioni, part of the Talanx Group of Hannover, for its adoption.
IBM’s and Red Hat’s partnership has spanned 20 years, with IBM serving as an early supporter of Linux, collaborating with Red Hat to help develop and grow enterprise-grade Linux and more recently to bring enterprise Kubernetes and hybrid cloud solutions to customers. These innovations have become core technologies within IBM’s $19 billion hybrid cloud business. Between them, IBM and Red Hat have contributed more to the open source community than any other organization.
How to Layout Big Data in IBM Cloud Object Storage for Spark SQL
Are you familiar with storage systems like IBM Cloud Object Storage (COS) or Apache Spark SQL? Dr. Paula Ta-Shma from IBM, gives us some tips & tricks you should know to improve your daily data journey.
One of the goals of BigDataStack is to facilitate scalable data storage through a distributed storage layer. This would enable storage across different resources, while supporting data migration for application components and re-allocation of data services across the infrastructure.
We are at an age where a single jet engine creates up to one terabyte (1,000,000,000,000) of data within a single transatlantic flight. Each one of us is like one of those engines, giving off ‘data exhaust’ as we operate in our daily lives. But these aren’t just inconsequential. In fact, big data is a worldwide market with more than estimated $203 billion worth of value by 2020.
We are happy and proud to announce that our paper entitled "BigDataStack: A holistic data-driven stack for big data applications and operations", submitted to the IEEE BigData Congress 2018, affiliated with the 2018 IEEE World Congress on Services (IEEE SERVICES 2018), has been accepted for publication.
A vessel has to complete its route within a time-frame. When a part of the main engine fails unexpectedly, the ship risks staying off-hire. This can be very damaging to a shipping company, as chartering revenues decrease, while replacing a spare part immediately increases cost. Thus, identification of potential failure allows timely ordering, or even replacement of spare parts before failure.