Cloud data migration is on the rise, with cloud adoption expected to nearly double in the next five years. It’s no surprise that cloud data migration is increasing, as many business benefits exist.
In this piece, we’ll explore why organizations are moving to the cloud, different migration strategies, the steps involved, how long cloud migration takes, and the everyday challenges businesses face in their cloud migration strategies.
Why Organizations Are Migrating Data to the Cloud
Newton’s first law of motion tells us that an object in motion stays in motion while an object at rest stays at rest unless acted upon by an unbalanced force. So there must be some unbalanced force causing so many organizations to move their data to the cloud. Let’s explore the key reasons behind the unbalanced force of cloud migration:
Reduced Costs
When organizations migrate to the cloud, they don’t have to spend as much time acquiring and maintaining hardware and software licenses. Nor do they have the sole responsibility for ensuring infrastructure security, upgrades, and service availability. Put simply, by migrating to the cloud, organizations avoid excess costs involved with infrastructure setup and management.
Centralized Data Management
Cloud data warehouses or data lakes can provide centralized management for the organization, which makes data more accessible and easier to manage. This data centralization and ease of access make it faster to acquire insights from data, among other benefits.
Scalability and Flexibility
With cloud migrations, organizations can quickly and easily scale resources up and down based on need. Also, cloud data solutions are flexible and can integrate with third-party libraries, which helps improve functionality and promotes business innovation.
Time-Efficiency of Operations
Having your data in the cloud means team members spend more time innovating and solving problems and less time setting up and managing infrastructure. Cloud services remove the burden of setup, management, upgrades, and security, leaving team members more time to build services.
Disaster Recovery
Most cloud data solutions copy your data to strategically located data centers. In case of disaster, these data centers ensure no data loss and the continued availability of services.
However, in some cases, keeping some or all of the organization’s data on-premises may make sense strategically. For example, organizations working with large datasets requiring high performance may choose to locate their data on-premises. Also, data that must be kept highly secure may be better suited for on-premise storage.
5 Different Types of Cloud Data Migration Strategies
Cloud data migration is often expensive and time-consuming. Ensuring no data loss and minimal downtime during a migration requires highly skilled data architects and engineers. It also requires a sound data migration strategy based on your organization’s application architecture, business objectives, and staff proficiency.
Here are five cloud data migration strategies and the different business scenarios they’re most suited for:
- Rehost – Rehosting is a straightforward migration method, also known as lift-and-shift. It involves migrating on-premises workloads to the cloud as-is, with no rearchitecting necessary. It is ideal for low-impact workloads, organizations looking to get to the cloud very quickly, and for organizations with limited resources. Unfortunately, organizations will miss out on some of the benefits of cloud computing without rearchitecting workloads from on-premises architecture to cloud-friendly architecture.
- Refactoring – This strategy aims to take full advantage of what the cloud provides, including autoscaling and serverless computing, and is hence more suited for organizations with some knowledge of cloud solutions. The Refactoring approach is a lift, tinker, shift model that uses a Platform as a Service (PaaS) to help optimize its service performance. The ‘tinker’ includes improving the data landscape before migration.
- Revise – This migration strategy involves making significant data infrastructure adjustments to maximize the cloud’s full benefits. Organizations scale down on-prem applications and services in favor of net new cloud services. This is ideal for organizations with outdated legacy infrastructure that no longer meets business requirements, and that have significant IT resources.
- Rebuild – Rebuild is an extreme migration strategy that organizations use when current solutions don’t meet business needs. This strategy is ideal for legacy and outdated applications. This migration strategy involves discarding the whole data infrastructure and rebuilding a new one. This approach can be time-consuming and requires team members to learn and upgrade their existing skills.
- Replace – This approach is similar to the Rebuild approach but utilizes a third-party cloud provider like Software as a Service (SaaS) to build its full services. With this approach, companies only need to migrate their existing data to their new infrastructure.
Cloud Data Migration Steps
After deciding on a cloud data migration approach, the next step involves planning for the migration process. Cloud data migration usually happens in 3 stages:
- Planning stage: This phase occurs before the migration process and includes budgeting and setting timelines. The planning stage also involves:
- Inspecting source and target systems to evaluate stability and prepare for any potential issues that may arise. Data inspection is also essential to check for anomalies or duplications to ensure quality.
- Deciding on either a big-bang or trickle migration approach.
- Backing up data to avoid data loss. This step is crucial for rehosting, which involves a complete lift and shift between source and target systems.
- Testing: Continuous testing and validation of migration processes before proceeding is critical.
- Migration stage: This stage involves ETL (Extract, Transform, Load), monitoring, and auditing. Although it is challenging to predict migration duration time, businesses should consider the need for availability, network latency, and data size before proceeding with the migration. The monitoring ensures processes proceed according to set procedures and validates the quality of the migration process.
- Post-migration verification: This process runs continuous parallel checks of the source and target systems to evaluate their functionality. Any deviation may point to a migration error. Verification after the migration is considered a best practice for efficient cloud data migration.
How Long Does a Typical Cloud Data Migration Take?
Underestimating the time it takes to migrate organizational data to a new infrastructure is a common and costly mistake. Estimating the time for migration helps organizations plan around the cost and availability of their services during the migration.
However, calculating migration duration isn’t straightforward and depends on factors like business data size, bandwidth, network, downtimes, IT proficiency, and the age of current systems.
AWS recommends using the following formula for a best-case scenario:
Number of days = (Total Bytes)/(Megabits per second * 125 * 1000 * Network Utilization * 60 seconds * 60 minutes * 24 hours)
For example, if you have a T1 connection (1.544Mbps) and 1TB (1024? bytes) to move in or out of AWS, the theoretical minimum time it would take to load over your network connection at 80% network utilization is 82 days.
Challenges Involved in Cloud Data Migration
Cloud data migration isn’t an easy task. Here are some common challenges businesses may encounter during their migration process:
- Costs – Although organizations migrate their workloads to the cloud to save on expenses like hardware, licensing, and infrastructure, in the long run, the upfront cost of the migration process isn’t cheap. The cost of planning, training, hiring, training, and investing in tools and technologies to maximize the benefits of the cloud climb quickly. To avoid exceeding the budget, companies should establish a clear migration strategy, decide on the type of cloud services they want, and prepare the budget accordingly. Applying cost-effective methods like batch processing may also help reduce migration costs.
- Lack of Clarity – A clear cloud data migration strategy details the entire migration process and provides information on costs, downtime, necessary tools, and backup plans for unforeseen issues. Without a clear strategy upfront, the already-cumbersome process of migrating to the cloud will be more costly and error-prone. For instance, an organization that hasn’t thought through its strategy might choose to rehost its environment without considering the impact this migration method will have on critical workloads serving business applications.
- Migration Resistance – Change is not always welcome. Yes, an organization may decide to migrate to improve its services, but team members may resist or be unable to adapt to the changes cloud migration might bring. For instance, in the rebuild migration approach, team members skilled in the old infrastructure would need to learn a new technology, which may add to their present workload. To curb this, organizations should employ a user-friendly cloud service that makes the cloud adoption process more straightforward. Organizations should also educate team members on the benefits of the change.
- Vendor Lock-Ins – Sometimes, an organization may adopt a cloud provider and later decide to switch providers. However, switching between cloud providers can be costly, and migration between cloud providers is problematic, resulting in vendor lock-in. To prevent this, before adopting a cloud provider, organizations need to conduct extensive research, prepare an exit strategy in case things go awry, and make their software more flexible to prevent vendor lock-ins in the future.
- Skill Gaps – Cloud data migrations can be scary for on-premises staff due to fear of replacement. Most organizations with intentions to migrate to the cloud have a skill gap to fill and may need retraining of present staff. Cloud migration requires expertise in designing and implementing successful data migration strategies. Organizations can take a proactive, fun, and collaborative approach to training staff by setting up challenges and giving certification discounts and rewards for newly certified staff.
- Security and Compliance Risks – Data security remains a barrier to cloud data migration, even with robust systems. Organizations fear handing over their sensitive data to third-party vendors and cloud providers. To mitigate these risks, businesses should ask cloud providers about their cloud security measures like encryption, storage methods, and security policies.
Cloud Data Migration Tools and StreamSets
A successful cloud data migration strategy involves many rigorous processes like ensuring data compatibility with target systems, workload transformation, and thorough testing, which can be time-consuming and rigorous to achieve without effective tools.
A data integration platform like StreamSets ensures seamless and efficient data migration between cloud environments or from an on-premise to the cloud. However, before deciding on a tool for cloud migration, organizations should make sure the solution provides end-to-end support throughout the migration and compatibility with the cloud destination systems. Modern platform choices like StreamSets do exactly that by providing a single platform to build and monitor data migration pipelines.
StreamSets makes it easy for organizations to migrate between different cloud providers. Our user-friendly interface allows organizations to easily change or improve migration strategies with the click of a button and without writing any code. StreamSets unlocks the potential for organizations to build robust multi-cloud environments. With StreamSets there is no need to be constrained by a single cloud provider. Navigate cloud providers as easily and as often as your organization requires.
Finally, organizations can build innovative migration pipelines that use active data drift detection and multi-table updates to reduce the workload in recreating the schema on the new platform.
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