The first step in tackling a Big Data project is to choose and define very well how we are going to do it. Through what method and what kind of platform. mount the On-Premise infrastructure or do it in the Cloud? It is then that key questions arise such as which option is more efficient at an economic level, which is the most stable and secure option or which is the most scalable.
In this post we are going to talk about it. Of the pros and cons of each alternative.
Big data on-premise
If we talk about On-Premise, we are referring to having Big Data in our facilities, that is, in the company’s own hardware. This means that we are going to have hardware dedicated to Big Data information processing and, therefore, prepared to respond to a multitude of processes at the same time. This scenario carries risks. The difficulty of properly measuring the scope of the project and its needs, by excess or by default, in an initial phase is one of them. Also the possibility that in 2 years the installed infrastructure will no longer serve us, due to a change in technology or due to a change in project requirements. Let’s see, however, the advantages and disadvantages of setting up a cluster on-premise:
- Everything can be kept local without third parties having access to data.
- With good knowledge, it can be successfully developed.
- Integration with other cloud platforms is possible, which is known as a hybrid cloud.
- The hardware cost can be extremely high.
- Extensive knowledge of Big Data processes, environments and ecosystem is required.
- System crash and/or incidents caused by external problems that are not under our control (electricity failure, for example).
- Risk of underuse of the hardware once we have already made the investment.
- Scalability, if necessary, implies additional investment in hardware.
- If the project does not perform well enough or is eventually abandoned, almost all of the investment is lost.
In short, setting up and managing Big Data on-premise requires a lot of experience and the risk of failure in terms of efficiency and optimization is significant.
Big Data in the Cloud
If we upload to the Cloud, partially or totally, we begin to enjoy infinite functionalities, extensions and, importantly, we make data management easier.
We highlight some of the advantages of keeping Big Data hosted in the Cloud:
- It is very easy to start, even with a small budget.
- The hardware investment is almost nil.
- Increases the speed of data processing.
- It is perfectly automated and can be customized.
- We can easily add and remove nodes.
- It is 100% secure and has multiple forms of access.
- You can migrate to another instance without losing data.
- If the extensions are correct, they never fail.
- Possible data control to third parties.
- Moving out of a public cloud environment comes with significant costs.
- Platform vendor dependency.
What are we left with? Big Data On-Premise or Big Data in the Cloud?
We have seen it. Each option has its advantages, but if we have a good service provider and the necessary expertise, our recommendation is to opt for a Cloud or Hybrid solution.
Selecting your cloud platform provider well is the key to minimizing or even eliminating these disadvantages. From our experience in many projects and use cases, we 100% recommend the binomial: Big Data Cloudera Platform (COP) integrating on Google CloudPlatform (GCP).
The final choice, in any case, does not depend on a single variable. The business objectives you have, the requirements and scope of the project, the future projection and how your IT infrastructure is, are some of the factors that will determine the most appropriate way to set up your Big Data cluster.
Regarding the economic part, we can say that the cloud platform is much more efficient. In On-Premise the initial investment is much higher with the drawback that you can only project a maximum of two years. After that time, it is possible that the installed infrastructure no longer serves and/or does not support the project. On the contrary, if you install in the Cloud, the initial cost is much lower and the performance you get in return is much higher.
With regard to the use, treatment and exploitation of data, both ways work. However, the On-Premise does not give us scalability, which reduces, from minute zero, the possibilities of our project.
If we talk about the knowledge required for the success of a Big Data project, we have already discussed it previously. The expertise that is needed in the case of On-Premise is greater. Modifying nodes, for example, is significantly easier on a Cloud platform.
The On-Premise solution has its point of advantage in relation to the control, location and storage of data, all of which are critical aspects in a Big Data project. But as we have already mentioned in this post, having this covered with a guarantee on a Cloud platform only involves selecting our service provider well.
How can we help you from PUE?
We accompany companies that want to undertake a digital transformation orienting to Big Data and Cloud through innovative technologies and solutions that seek to increase performance, efficiency, agility and results.
PUE is Official Google Cloud Partner in training authorized by said multinational to provide official training in Google Cloud technologies, and has obtained specialization in Infrastructure and Data Analytics. In turn, he is accredited and recognized to perform consulting and mentoring services in the implementation of Google Cloud solutions in the business field, with the consequent added value in the practical and business approach of the knowledge that is transferred in his official courses.
Also, as Cloudera’s first Gold Partner Integrator in EMEA and Authorized Training Partner, our services and expertise include both consulting and official training in Cloudera technologies.
Links of interest
Big Data and Cloud facing the new paradigms caused by COVID-19
Official Google Cloud training and certification
Official Cloudera training and certification
firstname.lastname@example.org for official training in benchmark technologies.
email@example.com for official certification in reference technologies.
firstname.lastname@example.org for professional services in Big Data and Cloud.