Big Data architecture that works for you!
If your business currently requires to implement Big Data analytics, there are a bunch of important questions you need to answer. The first one is “what for?”. The common misconception is that Big Data is like a magic stick, and by spending some exorbitant amount of money you can get a system that predicts the best course of action for your business, discovers some hidden opportunities for profits and provides increased revenues.
This misconception is actively supported by various Big Data consulting companies that do not provide Big Data implementation services. They develop “the Big Data strategy”, collect their fees and depart for greener pastures before you understand that this strategy is of no real use.
The best way to actually employ Big Data solutions is to reduce your expenses, not to try to increase your revenues. By empowering various product features, automating infrastructure management operations and highlighting consistent patterns in your spending, Big Data analytics can greatly reduce your OPEX and TCO, while actually providing more value to your customers.
To do that, you need to answer the second question: “how?”. Selecting the best architecture for your Big Data system is crucial in ensuring its success. The point is, Big Data really is big. It is a process involving huge data sets that must be gathered in real-time from your systems, processed on the fly and the results must be securely stored in the form of a vast data warehouse.
This requires huge data storage capacities, that are best provided by on-prem servers with inexpensive disk arrays. However, such systems also require immense CPU capacities, which are best provided by cloud platforms. This poses a problem, as the throughput capacity between the on-prem servers and the cloud computing instances becomes the bottleneck that can hinder your system performance.
Therefore, the best solution is to store your data on dedicated servers as close as possible to your target audience, but ensure it is in close proximity to potent cloud availability zones to supply ample computing power and ensure timely processing of your data. This kind of Big Data architecture is actually quite hard to implement, mostly due to the aforementioned connectivity and throughput limitations.
Therefore, the next question to answer is “where?”. There really is no simple answer to this question, as the outcome depends on a multitude of factors every time. Is your product or service cloud-based or does it run on some dedicated or on-prem bare-metal servers? If it runs in the cloud, in which availability zone is it deployed and is it close geographically to the majority of your target audience? If it runs on-prem is the current system structure able to support heavy workloads that will result from Big Data usage?
Only after identifying your system specs and the available ways to improve them, it is possible to create a roadmap to Big Data implementation and select the appropriate architecture. Thus said, a thorough understanding of the ways Big Data solutions work in general is required to plan the structure of your particular future system. IT Svit has gathered such experience over 5+ years of providing Big Data analytics and we have the required DevOps expertise to implement your Big Data system correctly.
Our team is able to define the ways to improve your existing infrastructure in order to prepare it for working with Big Data workloads; design, build and integrate the Big Data solution you need; and ensure the system works as intended. If you would like to receive our help — contact us right away, we would gladly provide assistance with planning and building the best Big Data architecture for your project.
