The Internet is full of statistics that show the growing number of data generated daily. Many datasets are now in the Petabyte and Exabyte range. Big Data analytics systems process and analyze these massive amounts of cloud migration.
Big Data is traditionally processed on-site due to its need for high processing power. However, in recent years, we have seen a growing trend to migrate workloads and Big Data operations to the cloud. Cloud scalability and flexibility work well for the amount and speed of Big Datasets.
While the combination of Big Data and the cloud can be beneficial, there are a number of challenges related to cloud migration. Read on to learn about these challenges and the best practices you can use to overcome them.
Why You Should Migrate Big Data to the Cloud
Gartner defines Big Data as data characterized by high volume, speed, or variety. This type of data requires innovative forms of processing. It is difficult to process such large volumes of Big Data using traditional database techniques. Migrating your data repository to the cloud can improve the speed, performance, and scalability of your operations.
Here are the main benefits of migrating Big Data to the cloud:
Cost-effective: The infrastructure of cloud providers usually consists of the latest processors and memory. You can get access to this top-level infrastructure at a fraction of the cost of your local infrastructure.
Flexibility: Some cloud services can integrate with local environments, allowing you to maintain a hybrid infrastructure. Cloud services typically support Windows and Linux environments. This allows data to flow seamlessly from sources to cloud processing services.
Scalability: One of the major benefits of cloud data is the ability to expand or expand during periods of heavy traffic. This makes it easier to process large amounts of data. Some platforms allow you to add new nodes on the go, while others support expanding to an extremely large capacity, such as petabytes.
The challenges of Big Data Cloud migration
The migration process in the cloud can be complicated for large data sets. Some of the challenges companies may face include:
Security: When migrating data to the cloud, one of the main concerns is data security. Companies often opt for a hybrid cloud solution. Separation of storage and calculations provides additional protection for sensitive data. Implementing role-based access control allows companies to control access to sensitive data in the cloud.
Technical skills: Migrating big data to the cloud is more than just a change. Developers need to move on-premises data repositories to the cloud. Then you need to connect your cloud-based Big Data media to your data sources. This requires knowledge of data integration practices and tools.
Cost: You can integrate each application to allow data traffic to move smoothly. However, this is a time-consuming code-writing process. You need to update your code regularly and there is a high risk of human error without automation. Data migration in the cloud requires extensive monitoring, increasing operational costs.
Best practices for big data migration
- Bring the management on board
When you are thinking of transferring Big Data to the cloud, one of the first steps is to involve management. You should involve executives, not just the CIO, in designing your cloud migration strategy. Their expectations can give you information when deciding which data to move to the cloud or not.
- Evaluate your workload
Big Data solutions usually fall into one of these categories: storage, development, and processing. Most Big Data clouds can accept a combination of all three types. Companies should assess their workload to see which category of Big Data they need most, and then design their strategy accordingly.
- Design a cloud migration strategy
You should consider the type of data and sources to determine which migration method to use. There are three main migration methods:
Lift and Shift: Data and applications are moved to the cloud “as is” without modification.
Refactoring: You may need to make changes, reuse your software, or change your data processing.
Architect: This method involves modifying your data to make it compatible with the cloud. This often involves scheduling downtime.
- Define security policies
Securing Big Data in the cloud involves monitoring and securing sensitive data. Your security policies should be clear and determined from the beginning. Security policies and practices should meet compliance requirements. In most cases, companies need to adapt their existing security controls to the cloud platform.
This often leads to the installation of new security measures and controls to meet the special requirements of cloud-managed data.
- Choose the right cloud platform
Companies should evaluate their analytics, performance, and cloud services requirements. Usage cases can range from data storage, analytical sandboxes to testing and development.
Choosing the right platform will depend on your service models, availability levels, and performance services.
- Always up to date
We’ve mentioned this above in terms of security, but it’s such an important part of working with a cloud provider that it’s worth emphasizing.
Our penultimate benefit of moving applications to cloud computing is that you don’t have to monitor and manage your data security threats. Because your cloud hosting provider does this for you, you can focus your resources on running your business.
- Encourage collaboration
Of course, your business will always be better when you have the best people working together as a team. Another of the 10 benefits of cloud migration is that the Cloud empowers your team to do this in remote work situations or when your employees are simply not local to your physical location.
Cloud-hosted software also allows multiple users to edit documents at once, then sync and save those changes to a single point of truth. So stop worrying about which document is the most up-to-date and don’t send customers the wrong file by mistake.
The cloud is the new standard, often due to its dynamic nature. Now that machine learning technologies have moved to the cloud, it has become an ideal place for Big Data. Big Data migration to the cloud has key benefits not only for large businesses but also for SMEs. Small and medium-sized businesses can also use cloud platforms to use Big Data to make data-driven decisions and improve their marketing channels.
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