The Internet is full of statistics demonstrating the ever-growing numbers of data generated daily. Many datasets are now in the Petabyte and Exabyte range. Big data analysis systems process and analyze these massive amounts of data. Big data is traditionally processed on-premises, due to its need for large processing power. However, in recent years, we have seen a growing trend to migrate big data workloads and operations to the cloud. The scalability and flexibility of the cloud works well for the amount and velocity of big datasets. While the combination of big data and the cloud can be beneficial, there are several challenges involved with 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 a high volume, velocity, or variety. This type of data requires innovative forms of processing. It is difficult to process such large volumes of big data by traditional database techniques. Migrating your data warehouse to the cloud can improve the speed, performance and scalability of your operations.
Here are the key benefits of migrating big data to the cloud:
- Cost-effective: The infrastructure of cloud vendors usually consists of the newest processors and memory. You can gain access to this top-level infrastructure at a fraction of the cost of on-premises infrastructure.
- Flexibility: Some cloud services can integrate with on-premises environments, enabling you to keep a hybrid infrastructure. Cloud services typically support Windows and Linux environments. This allows the data to stream seamlessly from sources to the cloud processing services.
- Scalability: One of the main advantages the cloud provides to big data is the ability to scale up or out during heavy traffic periods. This makes it easier to process large amounts of data. Some platforms let you add new nodes on the go, and others support scaling up to extremely large capacity, like petabytes.
Challenges of Big Data Cloud Migration
The cloud migration process can be complicated for big datasets. Some challenges companies can face include:
- Security: When migrating data to the cloud, one of the top concerns is the security of the data. Often, companies opt for a hybrid cloud solution. Separating storage and computing gives extra protection to sensitive data. Implementing role-based access control enables companies to control access to sensitive data in the cloud.
- Technical Skills: Migrating big data to the cloud is more than lift-and-shift. Developers need to move on-premise data lakes to the cloud. Then they need to connect cloud-based Big Data environments with the data sources. This requires knowledge of data integration practices and tools.
- Cost: You can integrate each application to allow the data traffic to move smoothly. However, this is a time-consuming code-writing process. You need to update the code regularly and without automation there’s a high risk of human error. Migrating data to the cloud requires extensive monitoring, increasing operational costs.
Best Practices for Migrating Big Data
1. Get Management on Board
When you think of transferring Big Data to the cloud, one of the first steps is to involve the management. You should involve C-suite executives, not only CIOs, in designing your cloud migration strategy. Their expectations can provide you with insights when deciding which data move to the cloud or not.
2. Evaluate Your Workload
Big Data solutions typically fall into one of these categories: storage, development, and processing. Most Big Data clouds can support the combination of all three types. Companies should evaluate their workloads to see which Big Data category they need the most, then design their strategy accordingly.
3. Design a Cloud Migration Strategy
You should consider the data type and sources to determine which migration method to use. There are three main migration methods:
- Lift and Shift: The data and applications are moved into the cloud “as is”, without modifications.
- Refactoring: You may need to make changes, maybe repurpose software or modify your data processing.
- Rearchitect: This method implies modifying your data, to make it cloud compatible. Often this implies planning for downtime.
4. 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 start. 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 results in installing new security measures and controls to address the special requirements of the data managed in the cloud.
5. Choose the Right Cloud Platform
Companies should assess their requirements in terms of analytics, performance, and cloud services. Use cases can vary from data warehousing, analytic sandboxes to testing and development. Choosing the right platform will depend on the service models, availability levels, and performance services.
What’s Next for Cloud Migration?
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. Migrating big data to the cloud presents key benefits not only for large enterprises but SMBs too. Small and medium companies can use cloud platforms, as well, to leverage big data for making data-driven decisions and improve their marketing funnels.