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The year 2020 was a watershed moment for automation. The challenges posed by the global pandemic – in which many businesses had to move operations remotely, nearly overnight – forced CIOs to turbocharge digital transformation and start executing on their 2025 digital transformation strategies – five years earlier than many had planned.
A May 2020 report from Forrester Research asserted that the pandemic resulted in a shift in "automation psychology" that will spark an 18- to-24-month period of rapidly accelerating automation.
Overview In our digital era, businesses are overwhelmed with data, and the ways to store, process, analyze, interpret, consume it. Today, the data that we deal with is diverse. User content like blog posts, tweets, social network interactions etc. and user transactions, touchpoints are various data sources
Data Analytics addresses large, diverse, complex, longitudinal, and/or distributed datasets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future. It describes various data types and data sets covering new and unstructured data sources (NoSQL), SCADA data, M2M data, RFID data, and WSN data along with traditional (SQL RDBMS) and structured data sources.
Most of the big and small companies are on the Agile transformation journey to bring products and services to customers in the shortest possible time, while trying not lose out to their competitors. While a few years back the challenge was to make enterprises buy the idea of Scrum and Agile through implementation examples, now the major challenge is to apply scrum to larger teams.
In this webinar, we first introduce you to the practical manner in which agile at scale can be achieved, and then about how to achieve true agility and meaningful results in a shorter time. We will also walk you through the five dimensions namely, Teams, Requirements, Timebox, DevOps and Quality that every enterprise, irrespective of its size should consider while scaling agile.
If you have been in the Java EE space over the last few years, Eclipse IDE for Java Enterprise Developersis probably one of the best IDE experiences, making the creation of applications with important EE components like CDI, EJB, JPA mappings, configuration files, and good interaction with some of the important application servers (TomEE, WebLogic, Payara, Wildfly, JBoss).
In this line, Red Hat develops the Eclipse variant "CodeReady Studio," providing you with an IDE that supports Java Enterprise frameworks, Maven, HTML 5, Red Hat Fuse, and OpenShift deployments.
As organizations scale their Agile practices, they face multiple challenges related to their working culture, organization structure, solution architecture, technical infrastructure, and so on.
While there are many Agile models and frameworks offering possible ways to address these challenges, it is ultimately the leaders in an organization who have to determine what would work for them in the transformation journey.
Spring Batch is a lightweight, comprehensive batch framework that is designed for use in developing robust batch applications. In this article, you will learn:
Spring Batch is a lightweight, comprehensive batch framework designed for use in developing robust batch applications.
Quarkus, the new “supersonic, subatomic” Java framework is currently getting a lot of attention. The ideas behind this build and runtime tool are indeed more than interesting for the future of enterprise Java. What are the benefits and shortcomings of using Quarkus? Let's find out.
Quarkus takes the reasoning that most of the dynamics of an enterprise Java runtime is not really required in a containerized world. Once you build your application to a container image, the functionality is usually not supposed to change. All of the dynamics that an enterprise container brings allows for very powerful and flexible programming and deployment models, but once our applications have been started inside containers, they typically don’t change anymore.
While implementing new ideas or new features in an existing system, most organizations are unable to decide what strategy to adopt for developing their product or evaluating the idea and its feasibility. The results every organization expects are a satisfied customer, the right transformation of ideas into an actual product, and better ROI.
The three fundamental strategies that organizations go for to achieve this in an initial stage are Proof-of-Concept (PoC), Prototype, and Minimum Viable Product (MVP). The dilemma when choosing an MVP, PoC, or Prototype can at times lead towards an incorrect selection. By doing so, an organization may end up leaking their finances, wasting their effort and time, and it may affect business growth overall.
Before digging out the details of IoT, let me explain what IoT actually means and how it has evolved over the years. To begin with, let's understand what IoT is. It is a device or software that connects the physical world to the digital.
IoT allows access to things remotely; this could be any kind of gadgets or appliance, including things like TV, AC units, fans, doors, and other physical objects. At the same time, it not only helps people interact with things but also organizations and companies to maintain their business process. Importantly, as the potential of IoT enhances, a large number of business enterprises are realizing (some of them have already started using IoT applications) the need and are trying to learn how to implement this technology. According to research, more than 21 billion things will be connected through the IoT by 2020. In fact, IoT has the power to reincarnate your business. So, my aim here is to help you explore the benefits of IoT applications in business enterprises.
Throughout our ten years of working with equipment manufacturers to connect, collect, and integrate operational data with enterprise systems, we’ve seen many trends impacting industrial IoT project success. The world has finally moved beyond most of the technological limitations for building innovative solutions. All the necessary tools exist to create connected product systems that perform as expected. They work. Now there’s a new trend, and it’s not a good one. We’re seeing business teams at equipment manufacturers telling engineering managers and IT leaders to evaluate and choose an IoT platform for the company. Run an online demo. Read API documentation. Build a proof of concept. Compare prices. Most of these projects never see the commercial light of day. They get stuck. Why? They get stuck because this approach to digital transformation is completely backward.
We’ve said technology isn’t the problem. Here’s the reality. The right technology for your system is available today. When used correctly by experienced teams, it will produce your desired outcomes. This is a well-charted territory. You can have remote monitoring with predictive maintenance and integrate machine data with your business workflows. These are solved challenges.