The adoption rate of cloud computing is rising, and it is expected that by 2020, cloud computing spending will grow at a rate that will exceed six times that of traditional IT spending. An effective
data migration strategy is very important, and its importance is often overlooked in the migration process.
When we consider the scale of big data, the company's internal infrastructure operations are quite limited, both in terms of scale and capabilities. For many organizations, it is very convenient to operate on the cloud by default for big data and AI.
Several scenarios where
cloud migration may become the preferred solution:
• Speed up application execution and deployment
• The project will receive huge traffic at night
• Be cautious about the impact of data center downtime
•Managing the ever-growing database needs is becoming more and more expensive
1. The challenge of data migration
A common misconception about cloud migration is that it is a one-time journey. But the reality is that the process of migrating data infrastructure to the cloud should be done gradually and systematically, while minimizing downtime and interference to users. Mobile data is only part of the puzzle. There are other challenges in cloud migration.
Cost plays an important role in the choice of scheme. Underestimating the resources involved in cloud migration may cause costs to quickly get out of control, and cloud migration may eventually become a monster of cash supply. For big data and cloud, data security is an issue that cannot be ignored. When migrating from on-premises to the cloud, your organization’s sensitive data is at risk. If these data are leaked in the process, the company may suffer a lot of economic losses. It is important to remember that the responsibility for protecting the data lies with you, not the cloud provider. Another serious challenge is to find people with the right skills to successfully execute the cloud migration plan. Lack of understanding of changing cloud technologies and skills may lead to slow and ineffective adoption of seamless cloud migration
Before starting the migration process, we must analyze in detail the cloud dependencies, constraints, migration patterns and potential applications, as well as the advantages of infrastructure as a service (IaaS). This will effectively push you on the path that best suits your company. Depending on how different companies want to use the cloud to achieve their goals, there are three main types of cloud migration.
Two, data migration template
Classified as follows, we see that there are 3 data lake migration models from internal data centers to the cloud:
1) Forklift forklift mode
This type of migration refers to the migration of basic computing instances from local Hadoop to the cloud. This is the simplest migration model that leverages the skill set of existing employees. It only uses persistent computing instances in the IaaS aspect of the cloud, usually in local storage. In addition to infrastructure access, security is entirely the responsibility of the customer who uses the cloud, as is the creation, configuration, monitoring, and maintenance of the cluster.
2) Hadoop cloud service
Migrating from local Hadoop to using Hadoop as a service provided by a cloud provider is the second migration mode. Most of the work surrounding Hadoop cluster setup and configuration and ensuring the compatibility of Hadoop ecosystem components is left to cloud providers. The data lake management application can help and use on-demand creation and use of Hadoop clusters and cloud-native persistent storage interfaces.
3) Mixed mode
The third mode of data lake migration involves a gradual transition from Hadoop internal deployment to a hybrid architecture (internal deployment/cloud), using various cloud native storage options and services in addition to Hadoop ecosystem tools, and adopting a cloud service model Process event streams, real-time analysis and machine learning. This model presupposes that the metadata management layer can eliminate any mismatches between the underlying technologies and provide a seamless view of the data structure across all data regardless of storage location.
Depending on your choice, there are multiple migration methods:
• The above 3 migration models (Forklift, Hadoop AAS, Hybrid)
•Hadoop distribution (Cloudera, Hortonworks, MapR)
• Hadoop ecosystem tool evolution
• Cloud service provider
For the above three migration models, meaningful comparison needs to be completed in the context of specific business and technical requirements.
Three, develop an effective data migration strategy
The meaningful comparison of the above three migration models needs to be done in the context of specific business and technical requirements.
1) Target evaluation
Understanding the current software architecture, infrastructure, and database schema helps define the time frame, cost, and workload required to implement cloud migration. You can first evaluate the business use cases of the data lake, security considerations, and the priority of applications/data that need to be moved first.
2) Data subset POC
It is strongly recommended that you test before using a new cloud provider. You need to develop a proof of concept to verify network challenges, functional verification and performance comparison. At this stage, you need to effectively test workloads and understand cloud storage services, necessary security controls, and production cluster size.
3) Product construction
Since you have now verified the cloud provider and model according to your requirements, you can continue the migration process yourself and start migrating data and applications to the cloud. The phased approach consistent with the chosen migration model considers the following factors:
1) Infrastructure migration decision-storage and computing, scale, expansion, network
2) Data security and governance of data access and resource usage in the cloud
3) Extract and restructure the data and send it to the cloud data lake. The data comes from different sources of internal infrastructure
4) Detailed list of internal data lakes and mapping to cloud platforms
5) Data conversion pipeline and corresponding cloud mechanism conversion
6) Application migration — forklift vs rewrite, development process, test and production
7) Migration options for historical data
8) Data Lake Management Application
4) Post-production
Now that your data and applications have been successfully re-hosted, you can focus on automating processes in the new infrastructure and optimizing its performance. It is best to use an automated testing framework and consider the infrastructure as a code (IaC) approach to simplify the deployment process. You can also manually double-check some of the most critical aspects of the infrastructure, such as: security, compliance, performance, etc.
4. Conclusion
When migrating to the cloud, enterprises need a partner with extensive cloud migration capabilities to support a variety of technologies, regulatory requirements, operating models, and target environments. Companies today are often satisfied with what they can get now, not what they really want or need. A comprehensive risk assessment supported by skilled cloud expertise can help achieve a long-term strategic goal. On the other hand, service providers must remain flexible to adapt to changing market needs in order to take full advantage of new technologies.
Cloud computing can bring the following benefits to various types of organizations-flexibility, efficiency and strategic value. Through a comprehensive assessment, any organization can develop a reliable migration plan based on its short- and long-term business goals. As most successful companies have shown, the time and effort required for the cloud migration process is not only reflected in the rapid improvement in quality, efficiency, and technical solutions for product launches.
Still operating your business on cumbersome and outdated infrastructure? You may want to consider migrating processes to the cloud while minimizing business risks.
