Big Data, Cloud & DevOps

When creating the product team, ensure you listen to feedback from the individuals likely to be impacted, don’t just do a paper exercise of moving people into new organisational structures. The teams on the ground know what skills and resources they need in their product teams, they know the issues they face and will have good ideas on how to address them, the key aim is to maximise the flow of valuable work into the team. 

While DevOps offers immense value for software deployment, the adherence to best practices is essential to reduce risk and assure security. Each organization is different and has different security postures.  This blog enumerates best practices for security across nine pillars of DevOps: Leadership, Collaborative Culture, Design for DevOps, Continuous Integration, Continuous Testing, Continuous Monitoring, Elastic Infrastructure, Continuous Delivery/Deployment and Continuous Security. Examples of best practices for each pillar are listed. These practices can be used to assess an organization’s maturity within the journey to Continuous Security, often referred to as DevSecOps.

Multi-model databases (MMDB) have rapidly gained importance in the market. However, is MMDB the right choice for your project or enterprise? While there are plenty of advantages, multi-model is not the ultimate solution for every situation. It is not a way to force developers to use a variety of data models, nor can one layered or native multi-model database integrate every data model efficiently. It’s more about enabling developers to leverage the advantages of different models for different aspects of their applications.

As the frequency of execution increases, dev teams find themselves between a rock and a hard place. While the growing demand for faster turnarounds isn’t poised to slow down anytime soon, teams struggle to integrate a set of tools into an efficient pipeline to get the job done within the time allotted. With demand on the rise, how can teams work together to fast-track their release cycles? With spring cleaning season upon us, dev teams across industries should take time this season to tune up agile processes and continue the work of advancing their shift toward DevOps.

New agile digital disruptors have realised the importance of centring their business objectives on value and technology, making it vital for traditional incumbents to follow suit. If waterfall organisations ignore this challenge, their ability to innovate, and react quickly to market changes and new customer demands is threatened. To change and succeed, the active involvement from the C-suite is essential to instil a cultural shift, placing value and people – employees and customers – at the centre of any transformation.

Technology is tucked inside every nook and corner of modern business. Smart leaders use data generated to understand what’s happening in the organization, and this analysis helps them bring agility to their business. In fact, data-driven organizations that harness insights to create a competitive advantage are growing rapidly. Data should be an influence to any major business decision. And so, data visualization is a powerful tool to bring dramatic changes to organizations. It can provide answers to many key questions, and make the data visually appealing, 

Companies regularly turn down applicants who would have been amazing technical performers. This leads to a huge number of confused, directionless aspiring data scientists. But here’s some good news: there aren’t actually that many reasons why applicants get turned down from data science roles, and there’s a lot you can do to cover those bases. And those reasons — the technical and nontechnical skills that most applicants don’t have but that companies most badly want — are what this post is all about.

Complexity can plague the success of DevOps within an organization. Complexity cannot be avoided, as DevOps is complex and will likely continue to be. However, the key to avoiding failure through your DevOps journey is to engage the complexity by using DevOps tenets to implement DevOps. Do not try to boil the ocean. Instead, at each leg in the journey, take inventory of where you are in terms of current goals, state, and best practices. Fine-tune your direction and build your solution using proven continuous delivery methods.

Since DevOps involves a collection of team members from all parts of the software delivery lifecycle (SDLC) process, the central platform needs to meet the needs of all team members. As you work to build your next test analysis toolbox, consider the five features to efficiently evaluate the data, act upon it and deliver iterations and features with confidence. Let’s explore these five essential tools that enable DevOps teams to quickly and efficiently analyze data, triage issues and act upon failures with the best possible insights.

Every piece of the IT organization has their own little area to deal with. You don’t typically play outside your area and work with the other teams unless you have to. By eliminating IT silos, organizations can then enable new intelligence, more effective decision-making, and informed automation to optimize network performance management (NPM). That is to say, if siloed walls are broken down and the synergies between tasks and technologies can be better coordinated, everyone benefits. The question is how to get there without going broke or crazy.

Today’s IT landscape is dominated by cloud, edge computing, IoT, AI and other disruptive technologies, and the datacentre remains at the heart of the organisation. Its role is key to delivering IT services and providing storage and networking to an increasing number of networked devices, users, and business processes. The explosions of data, as well as businesses embracing digital transformation, are all factors that play a part in not only storage strategies, but also the evolution of the datacentre.