A Well-Aligned Plan for Data Warehouse Migration & How Do We De-Risk It

• Business owner
• Project manager (with agile methodology experience such as Scrum)
• Project Coordinator
• Cloud engineer
• Database administrator
• Data modelers
• ETL developers
• Data virtualization specialist (possibly a database administrator)
• Testing engineer
• Business analysts (to help test BI tool queries, reports, and analyses)

Make sure that members of your migration team are familiar with and trained in various migration tools.

This includes,

• Amazon Web Services (AWS) database migration service (DMS)
• Google Cloud’s big query data transfer service
• Azure Data Factory
• Snowflake’s data sharing and snowpipe
• Databricks Lakehouse

The more understanding of the existing data warehouse, the more it’ll help you in the migration process.

Because a detailed knowledge of the system helps to communicate and cover all bases.

Moreover, it also helps in navigating strengths and weaknesses in the current setup.

To prepare for on-premises data migration to a cloud platform, it’s essential to assess your current setup and ensure your team is ready for the transition.

Take into account factors like data volume and network speed to choose the most suitable migration method.

Additionally, leverage data ingestion tools to facilitate the seamless transfer of data, ensuring all relevant datasets are captured and moved without errors.

Install any necessary integration runtime software and obtain approvals if required.

This will help streamline the migration process and minimize potential delays.

In any strategy, there needs to be a set of goals that should be defined to achieve success.

Targets can then be set to achieve these goals and people are given responsibility for reaching them.

Here are the examples of migration goals.

• Improve overall performance
• Run at a lower cost
• Operate with better availability and service levels
• Reduce administrative headcount

There are several different approaches to data warehouse migration.

The choice depends on factors such as the specific requirements, the size and complexity of your data, and the platform’s capabilities.

Here are some common approaches.

• Rehosting (Lift and Shift)
• Replatforming (Lift, Tinker, and Shift)
• Rearchitecting (Redesign)
• Repurchase (Buy a New Solution)
• Retire (Eliminate the Data Warehouse)
• Hybrid Approaches

Whatever strategy you choose, you should clearly define the scope of the migration.

For instance, what will be migrated and whether you’ll migrate incrementally or all at once.

This is crucial for planning, resource allocation, risk assessment, and overall project success.

Make an inventory of everything that needs to be migrated.

This includes data, schema, ETL processes (pipelines), users, authorization privileges, BI tool sematic access layers, and analytic applications.

To ensure a successful data warehouse migration to the cloud, it’s important to have a well-defined control plan.

It mainly includes tasks such as inventory and assessment, ETL process mapping, security and access control, performance optimization, validation and testing, etc.

The first thing you need for migration testing is to define a series of tests and a set of required outcomes for each test.

It’s crucial to ensure that all aspects are tested to indicate migration success.

Here are some types of testing that are commonly performed.

• Data integrity testing
• ETL process testing
• Performance testing
• Data quality testing
• Metadata testing
• Security and access control testing
• Regression testing
• Backup and recovery testing
• Scalability and load testing
• User acceptance testing
• Failover and high availability testing

Data validation and QA are essential steps to guarantee the integrity of migrated data.

By conducting data profiling, you can gain insights into data structures and patterns to identify inconsistencies.

Once done, implement data cleansing processes to make sure any inaccuracies discovered during validation are addressed.

Furthermore, perform regression testing on key reports and analytics to ensure that the migration did not introduce errors.

This will provide you confidence in the accuracy of data post-migration.

Performance tuning – one of the paramount steps to achieve optimal data warehouse operations.

This involves,

• Analyzing and optimizing SQL queries to enhance efficiency.
• Monitoring hardware resources for configurations in order to improve performance.
• Implementing workload management strategies to prioritize and allocate resources based on the criticality of different workloads.

The reliability of the data warehouse post-migration is contingent on a robust backup and disaster recovery plan.

You have to focus on regular testing of backup and restore procedures to guarantee the data can be recovered in the event of a disaster.

Furthermore, ensure backups are stored securely in an offsite location to protect against physical damage or loss at the primary site.

Proactive monitoring and alerting mechanisms are vital for identifying and addressing issues in real time.

Utilize automated alerts to notify administrators of any deviations from normal system behavior.

And remember, don’t forget to set performance thresholds and establish baseline performance metrics to identify trends and patterns over time.