What is the Relationship between Big Data and Cloud Computing - 【2020】
big data and cloud computing

What is the Relationship between Big Data and Cloud Computing

Knowledge is a triumph. Because only with a solid knowledge of one’s own optimization potential and tomorrow’s economic trends can one surpass the competition. Therefore, future-oriented Big Data technologies are the key to success for many companies. But not all Big Data are the same and not all solutions are suitable for all users. Anyone who wants to successfully rely on this technology must set the course from the beginning.

Big data and cloud computing go hand in hand

Marketing, sales, risk management, operations management, customer service and much more: thanks to mass data analysis with cloud computing, companies from different business areas get the decisive insight – in all industries and independently. Cloud computing allows you to store mass data and make it available at any time. It is therefore both a driver and a solution technology for dealing with large data.

But it is the real-time analysis of the data collected that creates added value for the business. It enables companies to obtain specific information and therefore to react much faster to market and customer needs and to design internal company processes more efficiently. For example, diagnostic data from the automotive industry provides early indications of inconsistencies, so that workshops can inform drivers proactively and offer them an inspection appointment.

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The manufacturer itself also benefits from the analysis of diagnostic data and can avoid recalls or optimize vehicle production. Information and communication technology is thus becoming a decisive factor for success and profitability. However, this poses a challenge that should not be underestimated, especially in the case of in-house IT, because when dealing with large data, most databases quickly reach their limits.

The right analysis comes from the cloud

Due to the abundance of requirements, more and more companies are outsourcing their infrastructure – either partially or fully – to external service providers. To keep data truly available, secure and usable, comprehensive measures and state-of-the-art technologies are required. Requirements that most companies can rarely meet, especially not 24 hours a day. Therefore, moving your own data and applications to a cloud is in many cases the most efficient approach.

However, it must be remembered that data security and availability are guaranteed. A company must have great confidence in the service provider when it gets its infrastructures from the cloud. The provider, in turn, must have the right technical expertise and, above all, future-proof technologies to ensure that the flexible infrastructure really does deliver the desired performance.

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At T Systems, we follow an approach we call Twin Core: If one data center fails, a twin data center takes over without interruption. This ensures high availability of business data. This concept represents the core of a strict zero-error strategy. Only in combination with such an IT environment is it possible to effectively use powerful data analysis procedures and keep critical information permanently available.

Another neglected security factor is the risk of data misuse and loss of control. Due to the uncontrollable proliferation of information – especially in the area of Web 2.0 – providers have to be extremely sensitive to ensure that the evaluation is carried out exclusively on an anonymous basis and can withstand a review by federal or state data protection officers at any time. It must not be possible at any time to assign the corresponding data to an individual. For this reason, data protection must already be included in the design of the solution.

In-Memory: Real-time analysis

As mentioned at the beginning, there is a wide range of solution technologies that make different sense depending on the area of application. For example, if the focus is on the speed of data retrieval, such as in the stock market or investment banking, then the use of in-memory technology is the right choice.

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Although the concept is not the only high-performance technology on the market, according to IDC’s 2012 study “Big Business thanks to Big Data” is currently the most widely used approach when it comes to handling Big Data. The information is already available in the computer’s main memory and is therefore available in record time.

This is precisely where the great strength lies: complex analyses of structured mass data can be performed almost in real time. SAP HANA, for example, processes data stocks up to 3600 times faster than conventional databases. However, as the capacities of the main memory are limited, the same applies to the amount of data. Although this is much greater than the volume that can be captured with legacy BI solutions, for example, the limit is typically reached at 16 terabytes.

Understanding Tomorrow’s Trends with Hadoop

If data analysis is primarily focused on understanding volumes of raw data, there is no way to avoid a solution that will master even the greatest flood of information. In this case, a Hadoop cluster is the ideal solution, complementary to other large data applications. The server cluster can be expanded at will, allowing almost infinite storage capacity. In addition, the software framework also captures heterogeneous data sets from different sources.

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This is a decisive feature, because in addition to structured data from the company’s own operations, the volume of unstructured information in particular is increasing thanks to various social media platforms. Facebook alone generates more than 500 terabytes of new data every day. With Twitter, the figure is 12 terabytes per day. Much of this data is critical to companies because it provides them with the information that is crucial to their business: What do my customers think about me and the competition? What products and services do they want? And what are the market trends in the future?

A Hadoop group, in combination with analysis software, allows you to evaluate all the information not only retrospectively but also with foresight. By identifying patterns, the solution provides users with conclusions about future developments. Such forecasts provide a knowledge advantage over the competition. They allow an early assessment of possible courses of action and form a reliable information basis for strategic decisions of companies.

Conclusion

Those who want to remain competitive tomorrow depend on the professional evaluation of their business data. Therefore, the use of an appropriate solution is a basic requirement for many companies in order to keep up with the competition. However, for the entry into business analysis to be perfect, the right decisions must be made from the beginning: This concerns the data to be analyzed and the purpose of the evaluation.

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In addition, the user must define together with the provider whether he depends on real-time applications, on an evaluation of unstructured data or even on a combination of both. The service provider to be considered will also depend largely on whether it meets the requirements for security and high availability.

Once these requirements have been sufficiently clarified, there is nothing to stop the implementation of a suitable application – ideally with a reliable partner that can accompany the introduction of a large data strategy from planning to implementation and beyond. With a powerful architectural concept for storing and analyzing information, Big Data becomes a clear competitive advantage.

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