Automated FlexPod for SAP and SAP Applications - The Most Important Building Block for IT Transformations

Many companies think that the basis of digital transformation is in analytics platforms and applications. However, central business process systems such as SAP are just as important. It is essential to combine the worlds of real-time analytics and core systems in order to leverage the full potential of experience and expertise.

But that’s easier said than done. As dynamic and flexible as the IT systems in the area of web applications, container technologies and analytics frameworks are, the complexity of daily work with SAP (HANA) systems is increasing rapidly. SAP specialists are often missing here. So how should ERP systems be connected to analytics frameworks?

A tower of strength

You may already know: the answer is FlexPod. The converged IT architecture is one of the most widely used and valued architectures for SAP systems in Europe. It consists of NetApp Flash data systems, Cisco network and compute components, the middleware ProMS by LNW Soft and the respective integrated NetApp and Cisco system management software.

NetApp data management technologies accelerate SAP projects from testing to production. The integrated connector by NetApp (In Place Analytic) enables real-time analytics data and ERP DB data to be seamlessly combined for any IoT concept with minimal memory footprint. Automated processes minimize the risk of failure. The whole thing is future-proof thanks to seamless scaling and integrated cloud connectivity.

Time is money and competitive advantage

This is how it looks in practice: A new SAP implementation or a conversion to SAP HANA usually takes between six to 18 months. Most of the time is spent providing suitable test environments, performing functional tests, and migrating tests. FlexPod, with its data management functionality and integration into higher-value SAP tools, helps to shorten the test phase.

Maintaining control with the right management

The daily operation of the SAP environment can then be mastered by IT departments with few personnel. The Proactive Management Suite (ProMAS) from LNW Soft in conjunction with FlexPod automates and documents complex work processes in an audit-compliant manner. It ensures the high availability of databases and SAP systems with cost-efficient utilization of resources – the key to success in operations. ProMAS and SAP Landscape Management (LaMa) form a perfect symbiosis.

Moving into the cloud

SAP landscapes are not static structures and must constantly be adapted for new requirements. For example, when it comes to the cloud. With Netapp’s integrated cloud data services from the large public cloud providers, hybrid cloud scenarios can be implemented without great effort.

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The Ultimate Guide to Learning IT Security - NetApp Certifications

What is IT Security and Why Should You Learn It?

Almost half of all UK businesses reported a breach or attack in the past year, and with it taking on average 120 days for a business to notice a breach; it’s vital that all measures are put in place to prevent information getting into the wrong hands.

With an expected 100,000 unfilled cybersecurity jobs expected in the Uk by 2022; employers are in need of skilled workers in this field more than ever. As part of the GDPR, companies are also now required to put measures in place to protect any and all customer data, so the need for workers skilled in data protection is higher than ever before in the UK.

How to Get Into IT Security in the UK

Not only is IT Security one of the most secure when it comes to job security and boasts high demand across the UK; it’s also an easy industry to access no matter your background. It’s very possible to enter this field without a Degree, which is rare for a profession where senior analysts have salaries that can exceed £100,000.

While the typical pathway into this sector does vary, many entry-level IT security professionals are Certified in their chosen field, which is a short course or level 1-2 training which develops the skills needed for the workforce. Some positions, such as Digital Forensic professionals, tend to have much higher qualifications; including a Bachelor’s Degree in an area such as Computer Science, information Technology.

Choose Your Study Pathway

Vocational Courses

Vocational courses, or further education, is and training that falls outside of ‘school age’, or high school level. In the UK, further education and vocational courses are offered at Colleges, Universities throughout. Qualifications range from Certificate and Diploma at level 3-5 courses. There are plenty of National Vocational Qualifications, or NVQ level 3 and 5 courses available in IT security, which can take anywhere from a few weeks to a year to complete. Many moving into this industry, ether for the first time or looking to specialise, choose vocational qualifications for their ease, online accessibility and short duration.

IT Security Apprenticeships

Apprenticeships are a less common pathway into IT and cyber security, but are very useful. The basis of all apprenticeships are hands-on learning, combined with academic theory in a course. The benefit of this in IT security, is that you can get real-world experience at a company or IT department, while earning your qualification. So, when you finish up, you’ll have both the certification and the experience needed in the real world. Apprenticeships are offered through University and Colleges, and while you do need to manage your time wisely, it’s a great progression route into the industry.

Work Experience in IT Security

It is possible to apply directly to a position as a trainee or an intern and develop your skills this way. While it’s not as common, much like an apprenticeship, you can work on your knowledge in a real-world setting, which is invaluable. This is an option for someone already working in IT who may want to move into an IT security role, or a recent school-leaver.

University Degree

For the more intricate iT roles, such as IT forensic specialists and Systems Engineers, a Bachelor degree is very common in the sector. Many professionals have qualifications in Computer Science or Information Technology, which can take anywhere between 3-4 years to complete. While a postgraduate course isn’t always necessary, some areas of IT security seem to prefer this when hiring for open positions.

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Understanding the Concepts of Artificial Intelligence (AI) - NetApp Certifications

A short swing down to the depths of neuronal nets

In artificial intelligence (AI) there is sometimes a confusion of language that can be easily prevented. AI in the narrower sense (also called “Strong AI”) aims to develop machines that act as intelligently as humans – but this is an academic vision out of reach. Instead, let’s focus on the “Weak AI” – Machine Learning (ML). Most AI developers would rather describe their specialty as ML. Others may have Reasoning, Natural Language Processing (NLP) or Planning (automated planning) in their email signature. Like ML, these terms can also be understood as AI subareas. How closely the technologies are interwoven is illustrated by the highly acclaimed Google Translator: since the end of 2016, the system has achieved surprisingly good results for some of the 103 supported languages because it translates entire sentences context-sensitively. This is due to the use of neural networks, which have generally improved speech recognition.

Neural networks play in their own league

Artificial neural networks, as I have already reported on in a previous post, are the heart of ML systems. This is a mathematical abstraction of information processing, similar to how it takes place in the brain. A neuron is modeled as a function (algorithm), including input, parameters and output. Photos, text, videos or audio files are used as data input. During learning, the parameters are changed. By modifying the weighting, the model learns what is important and what is not, independently recognizes patterns and thus increasingly delivers better results. After the learning phase, the system can also evaluate unknown elements.

In contrast to neural networks, expert systems – another AI subarea – do not teach themselves anything. They process large amounts of data and are connected to databases or datalakes. For data access, experts usually have to program filters. The fact that expert systems also have a learning capacity was demonstrated, for example, in chess: in 1997, the Deep Blue system developed by IBM defeated the legendary chess world champion Garri Kasparow in six games. In the much more complex board game Go, neuronal nets had to be used. It was only thanks to self-learning processes that the AlphaGo system was able to beat Lee Sedol, probably the world’s best Go player, four to one in five games in March 2016.

From Machine Learning to Deep Learning

In the past, ML systems used to work with an upstream feature recognition system called Feature Engineering. While the task was to recognize a face, the system initially searched for indispensable features such as eyes, nose or mouth. Today’s Deep Learning (DL), on which the German computer scientist Jürgen Schmidhuber, among others, worked, opens up completely new dimensions. Such a neural network consists of different layers with artificial neurons. From input to output, a query in each layer usually goes through a very simple operation, for example the application of a filter. The neurons may identify image features, as was the case with feature engineering in the past. However, the model is largely left to itself. It can therefore decide for itself which type of elements it best analyzes or extracts to predict the content of the image as well as possible. The layers give the neural networks a greater depth.

Great computers are available for ML today, but until recently the limitation was in reading and writing data (I/O) from storage media. With the introduction of the NVMe mass storage interface and 100 Gigabit Ethernet in 2017/2018, this hurdle fell as well. NVIDIA and NetApp have demonstrated what is now possible by combining a DGX supercomputer with the AFF A800 all-flash storage system as a “converged infrastructure”. The figures for the ONTAP AI Proven Architecture solution are also impressive: in addition to a latency of less than 500 microseconds, users can achieve a throughput of up to 25 GB/s. This allows a 24 node cluster to analyze more than 60,000 training images per second (ResNet50 with Tensor Core). The solution can be an option, innovation or vision. These terms do not all interpret equally, which is a good thing.

Success Secrets: How you can Pass NetApp Certification Exam in first attempt