The cloud is a technology that can help you implement big data across your enterprise. However, as powerful as these technologies are, most organizations that attempt to combine them by moving their big data workloads to the cloud fail.
In this article, we’ll cover why you should consider moving your big data workloads to the cloud, what challenges you might face and provide you with the steps that can help you make this transition as successful as it can be.
Why You Should Migrate Your Big Data Workloads to the Cloud
Migrating your big data to the cloud will allow you to take advantage of a highly available environment, powerful computing capabilities to process your data, dynamic on-the-fly scaling options and potentially save a significant amount of money in the process. On top of that, starting directly from the cloud will make the initial investment for equipment, systems, and software much more accessible.
The Challenges of Big Data Cloud Migration
Nowadays, Information Technology (IT) is managing modern big data apps and infrastructures via siloes. The problem the existing monitoring solutions is they lack full-stack compatibility, require complex apparatus, or do not provide complete support for big data environments.
For example, the learning curve for changing the configurations of your applications and components is relatively steep. On top of that, current monitoring solutions cannot deliver the level of agility that organizations require for their big data.
5 Steps for a Successful Big Data Cloud Migration
Now that you understand some of the challenges you might face while migrating your big data workloads to the cloud, it is time to go over 5 steps that can help you make the best cloud migration strategy:
#1. Define your migration goals
The first step of every big project is deciding what results will satisfy you and how to quantify them. In other words, deciding the project’s goals.
When you start a big data project without a clear strategy and objectives, you may end up wasting significant amounts of time, effort, company funds, and resources. In fact, many enterprises had to learn this lesson the hard way, with over 85% of all big data projects failing, starting this journey without a clear strategy is definitely ill-advised.
What drives companies to fail is not a lack of expertise as one might think, but rather the lack of knowing what to do with all the amount of data and how to make it into actionable information and use it to drive their businesses forward. Thus, if you wish to succeed in this endeavor and achieve your big data project’s goals, you first must understand what they are.
Here are some of the questions you need to know the answers for before executing your big data cloud mitigation plan:
- What would you like to achieve? How your big data plans will help your business?
- How would you know that you achieved that? How will business look after the migration?
- What steps stand between you and your goals?
#2. Determine your infrastructure and storage needs
In this step, you should be able to determine the type of storage and database infrastructure that you need to store and analyze your data.
Here are some factors to consider for determining if your analysis requirements are met:
- What type of data you store and analyze
- The amount of data you are going to handle
- How fast you require your analysis results
#3. Finding the right big data tools for you
After you finish assessing how to store and manage your data, you are ready to decide which tools fit your big data requirements and will deliver the best results for your analytical needs so you can make the most out of your data.
#4. Understand compliance and security requirements
Big data is a double-edged sword in many aspects. For example, the more data you collect, the better the insight you can gain into your user’s habits and improve your business model and increase your earnings.
However, in 2019, when cyber crimes have become one of the most serious global risks, the average data breach is estimated to cost more than $3.92 million. Thus, organizations must invest significant resources into security and be highly protective of their users’ private data.
If you deal with big data, you have to ensure that your security teams follow strict security policies. Additionally, big data has unique security requirements due to its sheer volume and variety of data types such as structured VS unstructured and dispersed storage like the cloud or on-premise among other unique elements.
Furthermore, if you use a cloud service, you need to find a service provider you can trust to store and handle your data and be able to negotiate with the provider until you are satisfied with the Service Level Agreement (SLA).
#5. Choose the right cloud model for you
This decision is based on your previous decision; what is the right cloud model for your needs.
Typically, you will need to decide between these three mainstream options:
- Public cloud—in this model, the cloud infrastructure is owned by a third-party provider such as Amazon (AWS), Google (GCP), and Microsoft (Azure), who is in charge of operating the service. The biggest benefits of this model for your business are the access to cutting-edge technologies like high flexibility and computing resources, which allow you to scale your workloads on-the-fly to meet current demands.
- Private cloud—this option is much more personalized than a public cloud and allows you to set up a cloud service around the features that your organization requires and have full control and knowledge over the computing environments and resources. A private cloud is usually better in terms of security and compliance but is much more expensive than a public cloud.
- Hybrid cloud—a hybrid cloud combines lets you enjoy the benefits of private and public clouds in terms of customizing, scaling, security and cost-efficiency.
Now that you know about some of the challenges you might face and have learned some of the steps that might help you prepare to make the transition, you can further your research into the cloud offerings available, and see which deployment model may be the best for your organization. You should now be better prepared to move your big data workloads to the cloud, if you decide to do so.
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