Big Data, Cloud & DevOps

As the technology gets easier to deploy, and the Cloud Vendor data services mature, it becomes much easier to build data-centric applications and provide data and tools to the enterprise.  This article is aimed at helping big data systems leaders moving from on-premise or native IaaS (compute, storage, and networking) deployments understand the current Cloud Vendor offerings. Those readers new to big data, or Cloud Vendor services, will get a high-level understanding of big data system architecture, components, and offerings.

Though immersive technologies are still in their infancy, they have come a very long way already. This gives credence to the fact that AR and VR in the finance industry will have incandescent adolescence. Looking at the current advances, AR and VR seem to be the undeniable future game changers for the financial sectors. With its immersive experiences, AR and VR in the finance industry will allow various institutions to offer the ultimate customer experience, thus enabling them to thrive amidst cutthroat competition.

Data analytics democratized the data product chain. With evolving BI landscape, its strategies are going to be more customized. Data analytical tools are a boon to this data-driven business world. The future trend is using intelligent business analytical tools for the effective decision-making process. To fire any business in this technological world, fuel in the form of data analytics and business intelligence is essential. Here are 6 of the best trends that could add fuel to the fire of future business development and growth.

If you haven’t been paying attention to the world of enterprise IT infrastructure, you may have missed the sudden rise of Kubernetes to a position of absolute domination. We can attribute this rapid ascent, in fact, to a confluence of trends. Perhaps the most predictable of these is the maturation of the public cloud. The second trend that contributed to Kubernetes’ victory: DevOps. Bridging the maturation of cloud best practice and the dual roles of DevOps is perhaps the most important trend of all: cloud-native architecture.

Introduction to Big Data provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. A solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects. Many datasets are too large to fit on a single machine. Unstructured data may not be easy to insert into a database. Distributed file systems store data across a large number of servers.

Data initiatives often take too long to get off the ground, which can cause businesses to give up or change tack. In addition, the promise of value of data warehousing projects is often lost due to other factors, including the fact that planning for the required hardware based on the estimated load and usage, is often a ‘thumb-suck’ exercise and requires a significant upfront capital investment. Finding and retaining the appropriate database administration skills to guarantee that data is readily available when needed, and indexed accordingly, is challenging and expensive.

Everyone that talks about scaling should understand that they are referring to the ability of their work – whether a system, a tool or some other innovation – to cope and perform under an increased or expanded workload. Something that scales well will be able to maintain, or even increase, its performance or efficiency when tested by larger operational demands. How do I increase the impact of my work? There are typically five steps needed to scale one’s work.

The plethora of automation tools available out there can be extremely confusing for organisations wanting to embark on a digital transformation process. Two-thirds of global service organisations were engaged in digital transformation, with 16% claiming to have already completed the process. it is important for organisations to take a holistic view of what they are hoping to achieve, before deciding on which automation approach to take. This is not always easy, as enterprise architects have to choose from a confusing range of process automation options as a foundation for the transformation journey.

Natural Language Processing helps business users sort through integrated data sources (internal and external) to answer a question in the way the user can understand and will provide a foundation to simplify and speed the decision process with fact-based, data-driven analysis. The enterprise can find and use information using natural language queries, rather than complex queries, so business users can achieve results without the assistance of IT or business analysts. NLP presents results through Smart Visualization and contextual information delivered in natural language.

 Practically speaking, it’s impossible for a data scientist to have all the skills listed in this article. But these skills are what make a rockstar data scientist different from a good data scientist, in my opinion. By the end of this article, I hope you’ll find these skills helpful throughout your career path as a data scientist. It’s perfectly fine if you’re overwhelmed by the skills needed. At the end of the day, these skills are not a must to be a data scientist, but they certainly make you different compared to other typical data scientists.

Data visualization provides an important suite of tools and techniques for gaining a qualitative understanding.  To choose the most appropriate visualization technique you need to understand the data, its type and composition, what information you are trying to convey to your audience, and how viewers process visual information. Sometimes, a simple line plot can do the task saving time and effort spent on trying to plot the data using advanced Big Data techniques. Understand your data — and it will open its hidden values to you.