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A comparison of public cloud providers: AWS, Azure, and Google. Part 1

If you’re looking for enterprise-level public cloud and Infrastructure-as-a-Service (IaaS) providers, Amazon Web Services (AWS) and Microsoft Azure are likely to be among the first names to spring to mind..

If you’re looking for enterprise-level public cloud and Infrastructure-as-a-Service (IaaS) providers, Amazon Web Services (AWS) and Microsoft Azure are likely to be among the first names to spring to mind. In Gartner’s view, Google is hot on their heels – and these top-three major players are well ahead of others in the field. Here, we take a closer look at the differences between the providers – starting with AWS.

For its latest analysis “Magic Quadrant for Cloud IaaS”, American market research firm Gartner rated the performance of the leading public cloud platforms against 234 criteria. AWS came out on top, achieving 92 per cent of the defined requirements for IaaS services, closely followed by Microsoft with 88 per cent. Google Cloud Platform came in third with a score of 70 per cent, although the internet giant is edging ever-closer to a leader score.

The three main players are all expanding their portfolios, which makes it even more difficult for companies to choose between them. Our comparison might help you decide which cloud provider is in the best position to meet your needs.

Amazon Web Services

Of all the providers, AWS boasts the most mature cloud offering. As a result, it is often the first-choice partner for the majority of applications. One of the main reasons for this popularity is that, alongside its public cloud services in the Infrastructure-as-a-Service (IaaS) sector, AWS also offers a wide range of tools for customers who wish to use Platform-as-a-Service (PaaS) technology to develop, test, and launch their own applications, including DevOps tools and tools for the development of mobile services. AWS also offers Hadoop cluster, data lake, and database options.

Another of the provider’s strengths is its global cloud infrastructure. AWS currently has more than 44 availability zones in 16 regions. Each of these availability zones boasts one or more dedicated data centers; each of these data centers meets the highest security standards and achieves unprecedented levels of reliability. Last year, AWS had more computing power in its cloud than all its competitors put together.

However, the factor that really sets AWS apart from other providers is the scope and depth of its platform. AWS is continually adding new services, technologies, and functions to its offering. At the AWS Re:Invent developer conference the company recently unveiled an impressive new series of tools dedicated specifically to machine learning technology, which is set to burst onto the scene in the very near future.

These tools included the world’s first deep-learning and fully programmable video camera, and a technology that tracks people in videos and can detect and categorize their actions. The company also revealed an analysis tool capable of recognizing 100 different languages and various linguistic units (places, names, people, and more), as well as a fully managed end-to-end service for scalable machine learning models.

To make efficient and effective use of these opportunities – for example, to adapt your own applications for the cloud or to link AWS cloud services to your own IT environment – it is a smart idea to obtain professional advice. With expert support, medium-sized companies can get the guidance they need for a smooth and easy transition to the cloud.

We’ll discuss the differences between the AWS public cloud and IaaS services from Microsoft Azure and the Google Cloud Platform – plus the strengths of each solution and which options are best suited to which types of company – in the second part of this post.

Would you like to know more?

Pls contact Jan Aril Sigvartsen our Cloud Transformation Expert or fill in the form.

Where are you on the cloud journey compared to your competitors? Find out here in our Next step cloud guide!

Ready to deep dive into the data lake?

Think data lakes are just a new incarnation of data warehouses? Our resident expert Ingo Steins rates the two.

Data lakes and data warehouses only have one thing in common, and that is the fact that they are both designed to store data. Apart from that, the systems have fundamentally different applications and offer different options to users.

A data lake is a storage system or repository that gathers together enormous volumes of unstructured raw data. Like a lake, the system is fed by many different sources and data flows. Data lakes allow you to store vast quantities of highly diverse data and use it for big data analysis.

A data warehouse is a central repository for company management, so it’s quite different. Its primary role is as a component of business intelligence: it stores figures for use in process optimization planning, or for determining the strategic direction of the company. It also supports business reporting, so the data it contains must all be structured and in the same format.

Challenges with data warehouses

Data warehouses aren’t actually designed for large-scale data analysis, and when used in this way these systems will reach their structural and capacity limits very quickly. We now generate enormous volumes of unstructured data which needs to be processed quickly.

Another limitation is the fact that high-quality analyses now draw on a variety of different data sources in different formats, including social media, weblogs, sensors and mobile technology.

A data warehouse can be very expensive. Large providers such as SAP, Microsoft and Oracle offer various data warehouse models, but you generally need relatively new hardware and people with the expertise to manage the systems.

Data warehouses also suffer from performance weaknesses. Their loading processes are complex and take hours, the implementation of changes is a slow and laborious process, and there are several steps to go through before you can generate even a simple analysis or report.

Virtually limitless data lakes

Data lakes, on the other hand, are virtually limitless. They aren’t products in the same way that data warehouses are, but are more of a concept that is put together individually and can be expanded infinitely.

Data lakes can store infinite different data formats in very high volumes for indefinite periods of time. Because they are built using standard software, the memory is comparatively cost-effective too.

Data lakes can store huge volumes of data, but need no complex formatting or maintenance. The system doesn’t impose any limits on processes or processing speeds – in fact, it actually opens up new ways to exploit the data you have, and can therefore help companies more generally in the process of digitalization.

Put on your swim suit

All you really need to start a data lake is a suitable database. This is relatively easy to set up with a solution like Hadoop. Companies who want to access a wide range of data and process it effectively in real time to answer highly specialized and complex questions will find that the data lake is the perfect infrastructure to realize this goal.

Ingo Steins

Ingo Steins is Unbelievable Machine’s Deputy Director of Operations, heading up the applications division from our base in Berlin. He has years of experience in software, data development and managing large teams, and now runs three such teams distributed across our sites. Ingo joined The Unbelievable Machine Company in January 2016.

Ingo Steins, Deputy Director of Operations, The Unbelievable Machine Company, part of Basefarm Group since June 2017

Big data Olympics

Four gold medals and one silver medal during the 2018 Winter Olympics are proof that Jac Orie is a successful speed skating coach. Why? It all has to do with data!

In the ice skating world, the name of Jac Orie is well established. He is the man behind the biggest successes of many Dutch speed skaters. Gerard van Velde in 2002, Marianne Timmer in 2006, Marc Tuitert in 2010 and Stefan Groothuis in 2014: they all won Olympic gold working with Orie. Apart from a mountain of medals, these skaters have left something valuable: a huge amount of data. Advanced analytics on almost two decades worth of data has helped Orie to train his team even more smartly in the run-up to the 2018 Winter Olympic Games in Pyeongchang, South Korea.

Data science

The results of Orie’s big data project have been astounding so far. Millions of viewers all over the world saw Sven Kramer (men’s 5,000 metres), Carlijn Achtereekte (women’s 3,000 metres) and Kjeld Nuis (men’s 1,000 and 1,5000 metres) skating to gold. And Patrick Roest (men’s 5,000 metres) won silver. Less visible is what exactly lies behind these successes. For many years, Orie has been using test data generated by skaters to calculate speed and stamina. For Pyeongchang however, he went one step further and collaborated with Leiden-based data scientist Arno Knobbe.

The big data approach, whereby computing power is used to perform calculations on big volumes of data has led to many useful insights. These include the relation between the type of training and the moment, duration and intensity of the training. A skater who has profited hugely from this is Kjeld Nuis. Data showed that stamina training in the morning proved ineffective for him, leading to an improvement in his training programme – and two gold medals in Pyeongchang.

Supercompensation

For Orie, Knobbe and the skating sport in general, the big data journey is just beginning. For example, the phenomenon of ‘supercompensation’ still needs to be figured out. Supercompensation is what happens when an athlete temporarily lowers the training intensity, leading to recovery of the body and an increase in racing performance. Obviously, this effect needs to be timed perfectly in the run-up to an important race. It’s a complex equation, with the results of training sessions sometimes showing up months later and with training types having different effects on performance for sprinting distances (especially the 500 and 1,000 metres), on the one hand, and longer distances (1,500 metres and above), on the other.

Golden opportunities – everywhere

It is certainly not an exaggeration to say that the 2018 Winter Olympics have become the first big data Olympics. As a best practice, the example set by the Dutch skaters will be followed by other athletes looking to optimize their performance. And it’s not just in sporting events that data thinking is making such an impact. Many companies are becoming more data-driven. At

Basefarm, we work together with some of these companies to explore their existing wealth of unexplored data and find new use cases. In the manufacturing, service and maintenance industries, for instance, the use of predictive maintenance saves companies millions of euros every year. And this is only just the beginning. Undoubtedly, big data will shape the next Olympic games as well as the business world of tomorrow. Our question to you: will you be a contender for gold?

About Ronald Tensen

Ronald Tensen is Marketing Manager at Basefarm in the Netherlands. He has a broad experience in the internet and IT industry (B2B and B2C), successful at developing and launching new consumer services and brands, strong customer focus and of course he is a great team player!