From managing security concerns to potential downtime, the complexities of a data warehouse migration require careful planning and strategy.
This guide covers all the essentials for a successful data warehouse migration, including tools, best practices, and a stepwise approach to help you avoid the most common challenges.
What is data warehouse migration?
A data warehouse migration is the process of moving data from a data warehouse to a different storage system. The data could be transferred from one warehouse to another or to a different kind of storage, like a data lake.
The most common data warehouse migration is from an on-premises to a cloud-based data warehouse. But migrations can also be spurred by the desire to change vendors. In these cases, the migration is from one cloud or on-prem warehouse to another.
Why migrate your data warehouse to the cloud?
Migrating your data warehouse to the cloud can reduce licensing and maintenance costs, enhance performance, improve scalability, and enable more advanced use cases.
These improvements are possible because of a few key features of cloud data warehouses:
- On-demand and flexible scaling of compute and storage resources.
- Outsourced infrastructure management and pay-as-you-go pricing models.
- Configurations designed to handle large-scale, advanced workloads.
- Integrations with AI/ML tools.
The caveat, of course, is that the data warehouse migration to the cloud has to be done correctly. Moving data to the cloud doesn’t automatically unlock the benefits of the cloud; the system has to be reconfigured to work effectively in the cloud.
What to know about each cloud data warehouse
Each cloud data warehouse has unique attributes, the usefulness of which depends on your organization’s needs and goals and your existing technology stack.
Because of this, it’s impossible to say which warehouse is ideal for your organization. Instead, the following section briefly summarizes each platform’s core strengths to help you decide which options are worth researching further based on your needs.
Amazon Redshift
As you might expect, Amazon Redshift is deeply integrated within the Amazon Web Services (AWS) ecosystem. So, if you’re already using AWS services, it’s worth considering their cloud data warehouse.
Beyond that, Redshift’s RA3 nodes offer uniquely flexible control over how you scale your compute and storage resources. Redshift Spectrum enables direct querying of data stored in Amazon S3. And with Concurrency Scaling, Redshift automatically adds commute capacity to handle spikes in user demand, which helps handle fluctuating workloads.
Redshift is best for organizations that:
- Are heavily invested in AWS
- Need scalability for variable workloads
- Have a hybrid setup that requires data lake integration
Google BigQuery
Google BigQuery is fully managed and designed for fast, cost-effective querying with pay-as-you-go pricing. Because BigQuery is fully managed, you don’t have to worry about infrastructure management and can focus on using your data. BigQuery also supports real-time analytics and ML integrations with Google’s AI tools.
It’s worth noting that while the pay-as-you-go-pricing model can be cost-effective, it’s not guaranteed. When usage spikes or if you have to run complex queries, costs can accumulate quickly.
BigQuery is best for organizations that:
- Prefer a hands-off solution
- Are invested in using Google Cloud services
- Require a scalable warehouse with support for real-time analytics
Snowflake
Snowflake offers multi-cloud support, which means it can run on AWS, Microsoft Azure, and Google Cloud and integrate data across these platforms. Like Redshift’s RA3 node type, Snowflake separates compute and storage resources. However, unlike Redshift, compute and storage are decoupled across all instances of Snowflake, regardless of your configuration.
Snowflake’s flexibility makes it well-suited for dealing with diverse data types without significant performance sacrifices. This makes it a good choice when you require combinations of diverse data types, machine-learning applications, and real-time analytics.
Snowflake is best for organizations that:
- Require robust data-sharing capabilities
- Have a multi-cloud strategy
- Need flexible handling of structured and semi-structured data
Microsoft Azure Synapse Analytics / Microsoft Fabric
Azure Synapse is an analytics solution with tight integrations throughout the Azure ecosystem that supports SQL-based cloud data warehousing and big data processing.
Because Azure offers traditional data warehousing and big data processing capabilities, it’s useful for organizations that need to handle diverse data types and manage big data processing in the same environment that they manage data warehousing.
Azure Synapse is best for organizations that:
- Already use Microsoft products or Azure
- Prefer the flexibility of choosing between dedicated and serverless modes
- Need a solution that combines traditional data warehousing with big data capabilities
Although Microsoft has no current plans to retire Synapse, it believes that Microsoft Fabric will “redefine the current analytics landscape,” with the next versions of big data analytics products a core part of Fabric. Check out this article to understand Microsoft Fabric if you already use Synapse.
A note on Intellias’ cloud-agnostic approach
At Intellias, we believe in a cloud-agnostic approach that puts your needs first. And we can execute this approach because of our deep expertise across all cloud providers and platforms.
Put simply, we can help you find the providers that ensure your migration project reduces costs, improves scalability, and unlocks modern capabilities.
Plan your data warehouse migration strategy
Planning your data warehouse migration strategy is as much about deciding what you will do as it is deciding what you won’t do. A plan helps you stay focused on your business objectives as you navigate these decisions.
Here’s how to start forming your plan:
Define success
Creating a plan for your data warehouse migration strategy starts with identifying a concrete destination. To do that, you need to choose measurable key performance indicators (KPIs) such as:
- Cost reduction
- Performance benchmarks
- Data accessibility
- Data latency
- User satisfaction
Based on your chosen KPIs, you can articulate a measurable, time-bound goal aligned with a long-term strategic objective. For example, your goal might look something like:
“Migrate from on-premises to a cloud data warehouse within 12 months (time-bound) to reduce data storage costs by 30%, improve query performance by 50% (measurable), and enable access to new machine learning capabilities (long-term strategic objective), with no more than 2 hours of downtime during the transition.”
Create a realistic budget
Budgetary constraints impact every aspect of your data warehouse migration, from what data you move to where, when, and how you move it.
Creating a realistic budget greatly simplifies the many decisions you’ll need to make as you formulate your data migration strategy. So, it should be among your first steps.
Not only that, your budget could make or break your ability to garner internal support for your project. So, you should structure your budget plan based on your finance team’s preferences.
Get the right people in the room
Even the most straightforward data warehouse migration project is a team sport. Who you choose to involve in your data warehouse migration strategy is critical. Following Amazon’s Two-Pizza Rule, you’ll want to limit your project team size to no more people than could be fed by two pizzas.
That means choosing representatives from teams that serve the following functions:
- Leadership: Defining strategic business priorities, controlling the budget, and securing buy-in.
- IT and Infrastructure: Administer data infrastructure and data storage.
- Data Governance: Set and enforce data privacy, security, quality, and regulatory requirements.
You’ll also want to include a representative from each distinct group of people who use and rely on the data from your data warehouse. These groups may include data teams, business analysts, data scientists, and other end users.
Types of data warehouse migration
Understanding different types of data warehouse migrations will help you align your approach to the unique demands of your project.
To that end, below are seven types of data warehouse migrations, classified based on the environment being migrated to and the technology architecture involved.
Environment-Based Migrations
- On-Premises to Cloud Data Warehouse: Migrating from a traditional on-premises data warehouse (e.g., Teradata, Oracle, SAP, Netezza) to a cloud-based data warehouse (e.g., Amazon Redshift, Snowflake, etc.).
- Cloud-to-Cloud Migration: Moving from one cloud-based data warehouse to another.
- On-Premises to On-Premises: Migrating an on-prem data warehouse such as SQL Server to another type of on-prem data warehouse.
- Hybrid Migration: Moving an on-premise or cloud-based data warehouse to an environment where data is partially in the cloud and partially on-premises.
Architecture-Based Migrations
- Data Warehouse to Data Lake Migration: Migrating a data warehouse (cloud or on-premise) to an unstructured storage system, such as DB2 Mainframe to AWS S3.
- Data Warehouse to Data Lakehouse Migration: Moving from a data warehouse to a data lake house that supports both structured and unstructured data, like Snowflake or Databricks.
13 steps for a successful data warehouse migration
These 13 steps cover everything from pre-migration preparation to post-migration optimization to provide a clear path for a smooth transition. Follow these steps to stay on track and set your data infrastructure up for long-term success.
Pre-migration preparation
1. Define clear objectives and requirements
Like any other project that requires time and resources, defining objectives, establishing requirements, and building a business case is the first step. Without a strong business case, you can’t get the funding and organizational buy-in you’ll need. And without clearly defined objectives and requirements, your project will inevitably fall victim to scope creep.
Don’t rush this step. While it might feel good to jump right in, gaining clarity on objectives and requirements provides a North Star for decision-making throughout the migration process.
2. Assemble your team
Every organization is different, so the departments you need to involve will be unique to your business.
But it’s critical to assemble a team that can provide the necessary perspective on the data infrastructure, the existing data warehouse’s architecture and design, data flow, data security and compliance, and data governance.
You’ll also need to include at least one representative for each distinct group of end-users (e.g., business analysts and data scientists) as well as executive leadership.
3. Audit and assess your data and its quality
You may have a general sense of the state of your organization’s data, but you need to get granular to ensure a seamless migration. This means identifying all data sources, classifying data by type, usage, format, and sensitivity, documenting metadata, and assessing the data for completeness, consistency, accuracy, relevance, and redundancy.
4. Document your findings and make necessary adjustments
After auditing your data, you’ll likely have found plenty of issues. This is an ideal time to update governance policies to address these issues and identify data that can be left out of the migration or cleaned before it’s moved.
Depending on what you find in your audit, you may also need to revisit your project budget and timeline to ensure they align with the work that needs to be done.
5. Plan for schema mapping and data transformation
The point of planning for schema mapping and data transformation is to ensure that the data in your source system is accurately migrated to your destination system. That means aligning your old and new schema, transforming data as needed, and preserving data relationships.
Fortunately, half the job is done already if you’ve done a good job auditing your data. Next, you’ll need to identify what data needs to be transformed or can be migrated directly and how the source schema maps to the target schema.
Finalize migration strategy and execute
6. Determine your migration approach
The right migration approach helps you manage operational impact, cost, and timeline. To determine the right approach, consider factors such as data volume, downtime tolerance, compliance and security requirements, and operational dependencies.
Common data warehouse migration options include the lift-and-shift, re-architecting, rehosting, refactoring, and re-platforming approaches. You can also mix and match these methods to align with your business needs and goals.
7. Set up the migration infrastructure and tools
The right migration infrastructure gives you the sandbox, staging, and testing environment you need to validate your migration strategy.
And tools help you simplify and accelerate the migration. Depending on your target data warehouse, there are likely pre-built tools such as Redshift Data Transfer Service, Google Cloud Dataflow, or Snowpipe for Snowflake.
8. Run a pilot
With a pilot migration on a representative dataset, you can test your migration setup and uncover potential issues before you start the full migration.
Migrate a subset of your source data warehouse’s data to the target system to validate schema mappings, transformations, and loading processes. Assess the pilot for data accuracy, query performance, and compatibility with existing workflows and then make any necessary adjustments.
9. Execute the migration
This step is the culmination of all your preparation work. Begin migrating your data warehouse and stick to the plan you set up in step six and tested in step eight.
Monitor the migration closely to catch potential problems early so you can make any necessary on-the-fly adjustments to keep the migration on schedule and within scope.
Post-migration testing, training, optimization, and monitoring
10. Test and validate
Testing and validation are how you ensure data accuracy and performance in your new environment. This should include:
- Data integrity checks to confirm no records are missing, duplicated, or corrupted
- Performance tests to verify performance meets expected levels
- Compatibility validations to ensure all dependent applications are functioning correctly
11. Optimize and finalize
Adjust indexing, partitioning, and other settings based on your pilot test, expected workloads, and simulated load testing on the new system to optimize performance.
Update your documentation to reflect the new environment’s architecture and processes. This final optimization phase ensures your new environment is resilient and operates efficiently.
12. Conduct end-user training
Once the migration is complete, it’s time to start training users on any new processes, tools, or reporting interfaces. As you go, solicit feedback from users to identify and deal with any issues they’re facing.
This step is as critical as any technical implementation because it will encourage user adoption.
13. Monitor and continuously optimize
The end of the migration phase is the beginning of the maintenance phase.
Monitoring tools are key to maintaining data quality, performance, and user satisfaction. You can use these tools to track system performance, usage patterns, and data integrity, and address any issues when they occur.
This also gives you insight you can use to plan for iterative improvements by refining queries, adjusting configurations, and adding or updating data pipelines as needed.
5 best practices for data warehouse migration
1. Be clear on your needs and wants
Nothing is more important to the success of your data warehouse migration than clear goals and system requirements. But, defining your goals and clarifying your requirements is not straightforward.
It requires deep, careful analysis of your existing workloads, resources, workflows, budget, continuity requirements, compliance and legal regulations, and potential future needs.
The labor in planning is worthwhile, though. Clear goals and requirements will help you eliminate the long list of alternative tools, configurations, and strategies you could potentially pursue.
2. Know your users and your dependencies
Understanding 1) who will use the new data warehouse and 2) how it interacts with other systems gives you the necessary perspective to minimize disruption and optimize the system.
Identify and meet with key user groups—such as business analysts, data scientists, and executive teams—and work to understand their unique requirements.
The insights you glean from these meetings will help you make better configuration choices, ensure compatibility, and optimize workflows. Equally important is documenting dependencies like integrations, data flows, and data ownership so nothing is overlooked in the transition and all connections remain intact.
3. Clean as you go
Migrating data is an excellent opportunity to address inconsistent, inaccurate, or duplicate data. Rather than transferring flawed data, clean it before and during migration. This could include standardizing formats, validating records, and removing redundant information.
Cleaning data as you go minimizes the quantity of low-quality, unreliable data in the new warehouse. This enhances performance and establishes trust in the system.
4. Double-check security protocols
A breach carries significant downside risk to an organization. And a data warehouse migration project is an ideal opportunity for hackers.
Security should be a top priority throughout the project. Protocols should be reviewed and updated to align with the new cloud environment’s requirements and capabilities.
This includes configuring access controls, encryption settings, and monitoring tools to ensure your system meets compliance standards and protects sensitive data throughout the migration process.
5. Validate twice, migrate once
With so many moving pieces, validating your approach is key to a successful migration.
Before you move any data, test your plan with sample datasets, checking for accuracy, performance, and compatibility. Before and after migrating, review data integrity to verify that no records are missing, duplicated, or corrupted.
Validating multiple times helps reduce the risk of errors, streamline troubleshooting, and increase confidence in your migration.
8 challenges of data warehouse migration
1. Data compatibility and schema conversion
Each data warehouse handles data formats, schemas, and structures differently. These differences are especially stark in an on-premise data warehouse vs. a cloud data warehouse.
This is why carefully mapping schema from source to target system, using schema conversion tools, and conducting comprehensive testing are vital to preventing issues.
2. Data quality and integrity
As the scale of your data warehouse migration increases, the likelihood of corrupting, duplicating, or losing data increases. Issues with data integrity can cause reporting errors, less trustworthy data, and broken processes.
Again, testing and validation are crucial to maintaining data integrity. Breaking your migration into phases can also help mitigate the risk of large-scale data integrity issues.
3. Downtime and business continuity
Depending on your organization’s reliance on continuous data access, minimizing disruption to daily operations can be tricky. Since downtime can lead to productivity loss and lost revenue, maintaining business continuity is critical.
A phased data migration can help you balance your organization’s needs while moving forward with the migration.
4. Query performance optimization
Optimizing performance is a project in itself when migrating from an on-premises data warehouse to a cloud-based warehouse.
This is because the way on-premises warehouses store and process data is much different from how cloud-based warehouses do. However, to maximize the benefits of the cloud environment, tailoring your system’s performance optimization strategies is critical.
5. Data security and compliance
Data is at its most vulnerable when it’s on the move. A breach or failure to maintain compliance with regulations can lead to fines, legal action, and damage to the company’s brand.
To keep data secure, plan to use encryption, secure transfer protocols, and carefully assign access controls. Work with your vendors to ensure they’re helping you stay compliant and secure.
6. Existing systems and applications integrations
By design, a wide variety of applications, databases, and third-party tools typically rely on a data warehouse. And disrupted integrations with these systems can impact business operations.
This makes it critical to document, test, and establish compatibility with all integrations, workflows, and systems.
7. Cost management
Cloud-based pricing is much different from traditional on-premises costs. And if you don’t plan for and monitor costs, you may incur unexpected expenses. Between data storage, processing, and additional services, these costs can escalate quickly.
To avoid this, use cost-monitoring tools, set budgets, and optimize resources by right-sizing storage and processing power based on actual needs.
8. User adoption and training
A cloud data warehouse migration isn’t complete when the last byte of data is transferred; it’s complete when the last byte is transferred, and the system’s users understand how to use the system.
Lack of user adoption kills the ROI of even the most technically sound data warehouse migrations. This is why providing thorough, ongoing training for end users is critical.
4 data warehouse migration tools
Tools are key to accelerating your migration by automating repetitive tasks and eliminating human error. Which ones are most useful to you depends on which vendor you work with.
The major cloud providers offer a variety of tools to make your data warehouse migration easier.
1. AWS Schema Conversion Tool (SCT)
AWS’s SCT allows you to automate much of the schema conversion when you’re moving data from a non-AWS database to an AWS data service. And for the schema, it can’t automatically convert, SCT will produce a report of where the schema still needs to be mapped.
2. BigQuery Migration Service
BigQuery Migration Service provides a set of features designed to facilitate data warehouse migrations to BigQuery. It is built for tasks like assessing and transferring data and SQL script conversion from on-premises systems to BigQuery.
3. Snowflake’s Native Bulk Data Loaders
Snowflake’s Bulk Data Loaders are built-in utilities and APIs that help you load large-scale data into Snowflake from external storage, primarily cloud storage like AWS S3, Google Cloud Storage, and Azure Blob Storage. With Snowflake’s loaders, you can move large datasets into Snowflake with minimal manual intervention.
4. Azure Data Box
The Azure Data Box is a physical device used to transfer large volumes of data to Azure. It is useful when data transfer via network is not feasible due to bandwidth constraints, time sensitivity, or security concerns.
How a private aviation company migrated to the cloud with Intellias
While all the tools, platforms, and providers discussed so far are effective in their own way, they can’t replace a trusted technology partner.
To see the power of a partner, here’s a short case study of how Intellias assisted one of our customers with a phased migration from Oracle to Snowflake.
Challenge:
A leading private aviation company with a fleet of over 350 aircraft faced high costs due to its technology choice (Oracle) and inefficient data management practices. This led to operational and budgetary challenges that limited their ability to effectively manage data across finances, marketing, customer relationships, and more.
Solution:
Intellias helped develop a Snowflake-based data warehouse and created a gradual migration plan. Key components of the new solution included:
- Centralized analytics for company-wide data, including finance, marketing, customer relations, and service management
- Visualized reports in Tableau for enhanced data interpretation
- Integrations with internal and third-party systems for billing, invoicing, operational management, business process management, customer relationship management, and cloud flight radars
- Implementation of ETL processes using Airflow and DBT for streamlined data processing and management
Business Impact:
- Improved operational efficiency and optimized resource allocation.
- Saved ~$24 million by avoiding excess commodity purchases.
- Enhanced business scalability.
- Improved financial planning and decision-making.
- Reduced data management costs.
Conclusion
Data warehouse migration is complex, but it’s a worthwhile process that can modernize your organization’s approach to data management. With a smart strategy, the right tools, and the right partners, you can complete a successful migration that meets your current needs and future goals.
If you’re ready to start your migration or have specific questions about your project, the Intellias team is here to provide in-depth consultation and support tailored to your objectives.
