A single-cloud strategy involves relying on a single cloud provider for all computing needs. This approach typically entails using one cloud platform, such as AWS, Azure, or GCP, exclusively for hosting applications, storing data, and running various services. Keeping your data in one cloud computing environment and trying to utilize services from another is fraught with challenges.
Challenges That Pushes for Data Migration and Consolidation into Cloud Platform
1. Increased Management Complexity
Managing multiple cloud providers introduces significant complexity in terms of operations, monitoring, and governance. Each provider has its own management tools, APIs, and interfaces, making it difficult for IT teams to maintain visibility across all platforms. This complexity can lead to inefficiencies and increased operational overhead as teams must coordinate between different systems and processes.
2. Data Integration Difficulties
Integrating data across multiple cloud environments can be cumbersome and may require custom solutions to ensure seamless data flow. Organizations often struggle with data silos, where data is trapped within specific cloud services, making it challenging to achieve a unified view of their data landscape. This fragmentation complicates analytics and reporting efforts.
3. Cost Management Issues
While multi-cloud strategies can initially offer cost benefits through competitive pricing, they can also lead to unexpected expenses. Organizations may face higher costs due to data transfer fees, management of multiple subscriptions, and the need for specialized skills to handle various platforms. The complexity of billing across different providers can make it difficult to track and optimize overall cloud spending.
4. Security and Compliance Challenges
Each cloud provider has its own security protocols and compliance requirements, which can create gaps in security architecture when using multiple clouds. This increases the risk of vulnerabilities and complicates compliance with regulations such as GDPR or HIPAA, as organizations must ensure that they meet varied standards across different environments. A single cloud platform can simplify security management by providing a consistent set of tools and policies.
5. Performance Issues
Performance can vary significantly between different cloud providers based on their infrastructure and geographic locations. This variability may lead to latency issues when transferring data between clouds or when accessing applications hosted on different platforms. A single cloud environment can help standardize performance expectations and reduce latency by keeping resources within one ecosystem.
6. Skills Shortage
Managing a multi-cloud environment requires expertise in various platforms, which can strain existing IT resources. The shortage of skilled professionals who are proficient in multiple cloud technologies makes it challenging for organizations to effectively manage their multi-cloud strategies without incurring additional hiring or training costs
The solution often is to move data into a common single cloud provider platform for storage and computing. Typically, the approach to move data is through rehosting or the ‘Lift and Shift’ method. Following are the steps for a successful data migration between two cloud productivity suites while minimizing downtime and maintaining migration fidelity.
Steps to Migrating Data Between Cloud Platforms
1. Assess Your Data
The data stored in the source cloud storage may have inconsistencies, errors, or gaps. Run data quality checks. Scrutinize relationships and dependencies within the data. Recognize foreign key relations, hierarchies, and linked records to ensure seamless migration. Check if the source data format is compatible with the destination cloud or if there is a need for reformatting before migration. Understand how changes in one field will affect related fields post-migration. Assess the volume of data that needs migration from each source. Identify any limitations in terms of storage capacity, network bandwidth, or infrastructure.
2. Prepare All Your Data
Remove anomalies, improve data quality, and duplicate files to prepare the actual workload that is to be migrated. Data migration is also your chance to clean data and raise its quality standard before transmission to prevent inheriting inferior quality data with old problems. Create a mapping document that outlines how fields will be mapped from the source to the target system to serve as a reference during the migration process. It is recommended to keep a backup copy of the entire database to prevent being permanently lost from a failed migration attempt.
3. Plan the Cloud Data Migration Process
Define your project objectives and expectations from this migration attempt. Create a project delivery structure with well-defined roles and responsibilities for every member involved. Identify who will lead the project, manage data preparation, perform testing, and handle communication with stakeholders. Define processes for tracking progress, reporting issues, and escalating critical tasks. Define project phases for data cleansing, mapping, testing, and go-live activities. Plan for a suitable time for migration keeping all the stakeholders in the loop. Select an off-peak hour or a public holiday to create minimum disruption of services. Involve an ETL developer or a data architect in designing the migration pipeline.
4. Inform Customers
Communicate with all relevant parties, such as customers, project teams, executive leadership, and any partners or vendors. Keep customers informed about the migration plans, including any timeline changes or challenges. Decide on the best way to communicate, such as email, Slack, or public-facing communication. Inform your customers about possible downtimes to prevent them from being affected by disruption of services during the transition process. This will establish your customer’s trust.
5. Execute Data Migration
Start data migration via pre-established ETL (Extract, Transform, Load) pipelines and preferred data migration tools. Seamlessly load the transformed data into the destination system, considering techniques like batch or stream processing. Set up logging systems to track the progress of data migration. Keep all relevant stakeholders informed of the progress and any potential delays. Monitor key metrics such as data transfer rates, error logs, and system downtime to identify any potential problems. Use monitoring tools or scripts to track progress, identify bottlenecks, and ensure timely completion of the migration.
6. Validate and Test
Once all data has been migrated, compare the migrated data with the original source data to ensure completeness and accuracy. Confirm that all required fields were successfully transferred, and that the data is complete and consistent by running queries on relevant fields. Deploy comparison tools or scripts to identify any discrepancies or missing records and address them promptly.
7. Grant Access and Monitor
Initially allow a small group of users and stakeholders to work on the newly migrated data and continuously monitor the performance and functionality in the new environment. Provide support to your users so that they can be adaptive to new environments. Gather feedback from stakeholders to refine future migration processes. Conduct user acceptance tests (UAT) to ensure that the data meets all business requirements and functions correctly in the new system. Finally, open access to the migrated data for all users.
8. Post Migration Planning
Regularly review and update data quality measures to ensure the accuracy, completeness, and consistency of the migrated data. Seek feedback from end-users and stakeholders to identify areas for improvement and optimization. Document the data mapping process and any transformations applied to achieve migration success. Optimize your cloud setup for best performance, cost-effectiveness, and security.
Conclusion
Planning your cloud data migration strategy is fundamental to your success. Equally important is selecting the right cloud data platform provider. A reliable provider will have the necessary experience, comprehensive plans for managing internal data infrastructure issues, and robust support for cloud data ecosystems. You may consider hiring an expert professional or a managed team to sail through the complexity and technicalities of a successful data migration.
