What is a cloud data migration?
Cloud data migration is the process that moves data from legacy systems to cloud platforms. This process requires careful planning to:
- Identify what data to move.
- Extract and prepare data at the source.
- Transfer the data through online or physical options.
- Load, test, and validate the data at the destination.
All of this must happen seamlessly with complete transparency to the company’s data users.
What are the risks and challenges in cloud data migration?
For all its benefits, migrating enterprise data to the cloud creates significant risks that IT organizations must address.
Data security
Any move risks exposing sensitive data to unauthorized users or releasing data in a breach. Migration projects must mitigate data security risk to avoid the resulting reputational and financial impacts.
Eliminating duplicative or obsolete data reduces the amount of data in motion, reducing the risk of data loss. Should data go astray, end-to-end encryption will render it unusable.
Service-level agreements with your cloud providers and other migration services will ensure third parties comply with your security requirements.
Data integrity and accuracy
Issues during data extraction and transfer could send duplicate or inaccurate data into your new cloud storage platform. Some data may not make the move at all.
Thoroughly audit your old systems to understand what types of data you have, where it’s located, and how it’s formatted. Map its move from the source to the destination. Then develop data pipelines at both ends to ensure everything migrates correctly.
Compatibility and interoperability
A direct lift and shift migration is tempting. Unfortunately, your legacy Oracle databases and your new Google Cloud service are too different to make it likely.
Document the data dependencies in your applications and workflows to mitigate compatibility issues — especially in multi-cloud or hybrid-cloud infrastructures. Allocate resources for refactoring applications or new development projects that address platform differences.
Downtime and business disruption
Large-scale cloud data migrations are complex. Without a cloud migration strategy, the transfer may take longer than expected. Any delays, however, could disrupt data services or even cause downtime in critical systems.
Plans never work perfectly, so devote as much time as possible to prepare for the unexpected. How will you handle internet disruptions that slow online transfers? What if the truck carrying your offline transfers breaks down? Develop contingencies for events that could delay the project and impact your business.
Network bandwidth and performance
The volumes of data migrating to the cloud, the distance between your data center and your cloud provider, the reliability of your on-premises networks, and congestion on the public internet will conspire against your migration plans.
Unless you can complete the job over a weekend, you may have to throttle data transfers during the business day to preserve your network’s performance, latency, and bandwidth.
Alternatively, work with your ISP and network vendors to temporarily scale your network infrastructure during the migration to handle large datasets.
Data governance and compliance
Your company will require overlapping governance and compliance programs during the migration. Your existing policies will apply as long as legacy systems are running. At the same time, the governance team must create new guidelines for managing data in the cloud.
Besides running old and new policies simultaneously, you must also develop data governance policies for the migration. For example, these policies will define how to move between approaches to metadata.
Vendor lock-In
Enterprises don’t need to be very old to have experienced the pitfalls of vendor lock-in during data migrations. They spend enormous effort extracting data from proprietary systems only to drop the data into newer proprietary platforms.
Cloud vendors are a little more open. Even then, migrating data from Amazon’s AWS to Microsoft Azure is not lift-and-shift easy. One way companies future-proof their data architectures is by adopting a multi-cloud strategy. It does not eliminate lock-in risks but gives companies more flexibility.
Resource and cost management
Moving to the cloud is an investment in future productivity, but the costs are very real. Poorly planned migrations take longer to complete and must inevitably solve problems in mid-transfer. Once the migration is complete, unexpected compatibility issues will require more development work.
The critical factor in resource and cost management is thorough planning. Not only does it address contingencies, but it ensures stakeholders understand the migration’s total cost.
Specialized skills and expertise
Companies spend decades developing institutional muscle memory with their on-premises systems. The cloud requires more than new skills. It requires a different perspective.
For example, IT administrators accustomed to deciding how and where to store data find they have much less direct control over their cloud resources.
Simply replacing old-timers with new hires won’t work. The cloud data management sector suffers from a skills shortage that will take a decade to clear.
Retraining your existing staff and bringing in third-party expertise are two ways to make the transition. A third option is to adopt migration tools that streamline decentralized data management.
What are the benefits of cloud data migration?
The cloud’s many impacts on business performance make the replacement of on-premises systems worthwhile. Among the benefits of cloud data are:
Scalable storage and compute
The cloud’s capacity for storage and compute is limitless compared to your data center. What won’t change with a cloud migration is your budget. Fortunately, the cloud decouples storage from compute, allowing you to optimize each.
Your storage needs will always increase, but that predictability lets you plan future storage capacity.
Compute is more dynamic. Previously, you had to buy for the peak and let compute capacity sit idle. In the cloud, compute scalability moves with demand.
Cost efficiency and savings
Going from just-in-case to on-demand infrastructures in a service-based architecture creates significant financial benefits. More of your budget shifts from costly capital spending to predictable — and easier to justify — operational expenses.
Closing data centers frees network capacity and lowers real estate costs. In addition, retraining the staff who maintained that infrastructure lets you enhance support without increasing headcount.
Accessibility and collaboration
Migrating to the cloud helps transform your company’s culture as it pushes data-driven decision-making throughout the organization.
The cloud decouples your data infrastructure from geography. Remote and hybrid workforce models become easier to support since employees can access the data they need anywhere in the world.
As decentralized work models become commonplace, the cloud fosters new ways to share data and collaborate.
Agility and innovation
Democratized access in a data-driven decision-making culture fosters data analytics exploration, experimentation, and iteration. Your organization’s time to insight shortens, allowing executives to make better decisions sooner.
Better decision-making makes your company more agile and generates new ways to improve productivity and growth.
Disaster recovery and business continuity
Cloud technologies make business continuity easier to maintain during natural disasters or cyber-attacks. Local incidents that would once have shut down data centers have little impact when everyone can access data in the cloud.
The cloud streamlines disaster recovery as well. Saving backups regularly in cloud storage platforms allows you to restore operations quickly in the event of a malware attack.
Security and compliance
In some respects, the cloud simplifies security compliance. For example, your team won’t manage the physical security of servers in your cloud service provider’s infrastructure. Many cloud service providers comply with data security frameworks such as HIPAA, streamlining the development of your own cloud security compliance programs. You can complement your team with the expertise of your CSP’s security experts.
Starburst for cloud data migration
Starburst’s data lake analytics platform can help your organization before, during, and after your cloud data migration. We provide a virtual layer with a unified, holistic view of every data source, whether on-premises or in the cloud.
Before your migration, you can use Starburst to audit your data infrastructure, identify redundant data, and set aside unnecessary data.
During the planning phase, Starburst helps you map the analytical business logic connecting existing business intelligence and data science assets. This same tool lets you rebuild these data pipelines more efficiently within your cloud solutions.
By leveraging Trino, the open-source distributed SQL query engine, Starburst delivers lightning-fast access to data on your cloud platforms and remaining legacy systems. Letting analysts use the SQL tools they already know frees them to explore data without depending on your data engineers. They get more insights faster to help decision-makers choose the best course for the company’s future.
