Product Engineering

by Team Azilen

January 29, 2024

Elevate, Migrate, Innovate: A Well-Aligned Plan for Data Warehouse Migration + Challenges + How Do We De-Risk It

When we talk about moving data from one place to another, it can sound like a big, complicated task.

But think of it like moving valuable stuff from an old storage room to a new and improved one.

The question is, does it really make a difference where you put it?

Well, it turns out, it does. But not for the reasons you might expect.

So, to make it simple, we have listed down the considerations for when to choose data warehouse migration services.

However, it’s crucial to weigh the reasons for making such a significant move.

To assist in this evaluation process, we’ve assigned a challenge level from 1 to 10 for each factor.

The higher the level, the more influential the factor is in determining the need for migration.

After assessing all the factors, you can calculate a final score by summing up the weighted values corresponding to your specific needs.

Key Consideration Scoring Table

Key Consideration	Add Challenge Level Between 1 - 10
Key Consideration	Migrate or Not
Performance issues
Scalability requirements
Outdated technology
Cost optimization
Vendor lock-in
Integration needs
Security & Compliance
Data governance improvements
Advanced analytics & machine learning
Cloud adoption
Geographical considerations
User experience and accessibility
Your Final Score

If your final score is below 60, it may indicate that the current data warehouse setup is still adequately meeting your organization’s needs.

In such cases, opting for a migration might not be the most pressing priority.

However, if your final score surpasses 60, this suggests a strong case for pursuing data warehouse migration services.

So, if your score is above 60, you should continue reading this blog.

Here, we’ve provided valuable insights into the planning, execution, and best practices for a successful data warehouse migration.

(Please note the above framework is based on the best case scenario. For an exclusive assessment of your existing data infrastructure, please feel free to connect with our experts.)

Importance of Data Warehouse Migration to Cloud or to New Vendor

Imagine your data warehouse is like a favorite old car.

It’s been reliable for years, but lately, it’s struggling to keep up with the new speed limits.

That’s where data warehouse migration comes in, it’s like giving your car a turbo boost!

Here are some key reasons why data warehouse modernization is important.

Helps in making queries and data processing tasks faster, allowing for quicker and more efficient data analysis
It allows for handling larger data volumes and increased processing demands as organizations grow
Reduces operational expenses
Offers updated features, better integration capabilities, and support for advanced analytics techniques
It streamlines data integration processes and ETL operations for more efficient workflows
Enables better handling of diverse data types and formats
Provides enhanced security features and compliance capabilities
Data warehouse migration reduces dependency on a specific vendor
Offers seamless and efficient integration capabilities with the overall tech stack
It keeps your organization adaptable to emerging technologies
Provides a competitive edge by enabling faster, more informed decision-making based on data

A Well-Aligned Plan for Data Warehouse Migration

Data warehouse migration is a challenge for any size of company.

To execute it well and avoid unwanted surprises and higher costs, you need to thoroughly understand the challenges, mitigate risks, and plan your migration strategy.

At a high level, your plan should include the core data warehouse migration process steps and any tasks within them.

The main process steps are:

1. Pre-Migration Preparation

Several steps should be considered before you even begin a data warehouse migration.

Key Roles in Data Warehouse Migration Team

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)

Skills and Training Required for Data Warehouse Modernization

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

Full Assessment of Your Existing Data Warehouse

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.

On-Premises Preparation for Data Migration

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.

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

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

2. Data Warehouse Migration Strategy and Execution

Migration Goals

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

Migration Approach

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

Migration Scope

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.

Defining What Has to be Migrated

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.

Migration Control

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.

Migration Testing

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

3. Post-migration

After migrating the data warehouse to the cloud or a different provider/vendor, it’s crucial to manage and optimize the environment for continued success.

This stage ensures that it continues to meet the specific needs of your organization and remains optimal for data-driven decision-making.

The post-migration process mainly includes,

Data Validation and Quality Assurance

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

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.

Backup and Disaster Recovery

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.

Monitoring and Alerting

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.

👉 Explore our data engineering expertise and offerings

Challenges to Address During Data Warehouse Migration

Migrating a data warehouse is a crucial process that comes with its own set of challenges.

To ensure a seamless and efficient transition, it’s important to identify and address these hurdles.

1. Data Quality Assurance

Without adequate data quality assurance, migrated data may suffer from errors, security issues, or even loss.

This can lead to inaccurate reporting and poor decision-making, damaging the organization’s performance.

Solution:

Implement data validation scripts
Conduct thorough testing
Maintain data backups for rollback

2. Scaling for Large Datasets

Handling large-scale datasets demands careful planning and execution.

Because neglecting to address data volume and scale may lead to slow query processing and overall performance bottlenecks.

Solution:

Conduct thorough performance testing
Consider partitioning strategies to distribute data effectively
Implement data compression techniques to optimize storage

3. Navigating Data Transformation and ETL Process

Failing to replicate complex ETL processes accurately may lead to inconsistency in the transformed data, which can affect the downstream analytics.

This inaccurate data transformation can result in flawed insights and reports.

Solution:

Document existing ETP processes and recreate them in the new environment
Perform thorough ETL workflow testing to ensure accuracy
Leverage ETL automation tools for efficient and seamless migration

4. Schema Mapping and Compatibility

Overlooking this factor during data warehouse migration can lead to misalignment or data loss between source and target systems.

This can result in incorrect analyses and decision-making.

Solution:

Conduct detailed schema analysis to identify misalignments
Create a mapping plan and use transformation scripts to ensure compatibility
Validate data integrity after schema mapping

5. Rigorous Data Validation and Testing

The appropriate data validation and testing are paramount.

Because an inadequate approach may result in errors or inconsistencies, which can impact the reliability of the migrated data.

This can potentially lead to faulty reports and insights, affecting business decisions.

Solution:

Execute comprehensive testing scenarios
Implement automated testing scripts for repetitive validation
Involve key stakeholders in UAT for validation

6. Mitigating Downtime and Business Impact

Failing to plan for minimal downtime can disrupt your ongoing operations, which can cause valuable financial losses and customer dissatisfaction.

Solution:

Create a detailed migration plan that includes defined downtime windows and communication strategies
Consider a phased migration approach to minimize disruption
Implement rollback time to tackle unexpected issues

By proactively decoding these challenges and implementing appropriate strategies, you can navigate the complexities of data warehouse migration and ensure a smooth transition to the new environment.

How We De-Risk Data Warehouse Migration Project?

As a product engineering company, we comprehend the criticality of de-risking the data warehouse migration project in order to maximize the likelihood of success.

For that, there are several things that we execute promptly.

It includes,

Create a lab for testing, identifying issues, and optimizing migration approaches.
Automate tasks, use built-in tools and develop templates to capture best practices.
Assign accountable owners for tests as defined in your migration strategy.

Introduce data virtualization between BI tools and your data warehouse.
Use virtualization tools to hide migration complexities from users.
Maintain user transparency by accessing virtual tables during migration.

Archive unused tables to Data Lake Storage before migration.
Create external tables for archived data.

Set up a VM with a development version of the existing legacy data warehouse DBMS.
Move the data warehouse schema to the VM before migration.

Define migration order and dependencies.
Prepare infrastructure and operations teams early for data migration.

Identify differences in DBMS functionality and address proprietary business logic.
Recognize challenges in migrating stored procedures for ELT processing.

Consider migrating data marts first, followed by the data warehouse.
Prioritize migration based on business needs for a more manageable process.
Establish clear checkpoints and performance metrics at each stage

Evaluate the use of data virtualization to simplify the current data warehouse architecture.
Replace physical data marts with virtual ones to reduce data stores, copies, and costs.
Switch from physical to virtual data marts as a modernization step before migration.

Positioning $SNOW in the data ecosystem:

Snowflake’s core business is the data warehouse, but they’ve expanded aggressively over the last few years.

Frank Slootman mentioned this in last quarter’s earnings call:

We’ve been working very hard…in delivering just an absolute ton… pic.twitter.com/zD0JCHpUFt

— Eric (@EricFlaningam) January 10, 2024

👉 Check out our case study on leveraging Snowflake for a car sharing platform

Our Expert Data Warehouse Migration Services

At Azilen, we believe in the power of data to revolutionize businesses.

With our 14+ years of experience, we bring a wealth of expertise in orchestrating smooth transitions from legacy systems to robust data environments.

We know that your data is the heartbeat of your business, and we’re here to ensure that it thrives.

Hence, our data warehouse migration services are designed not just to move data, but to elevate your entire data ecosystem.

For that, we have PRO data engineers by your side, anticipating a smooth transition that sets the stage for unparalleled growth and innovation.

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Elevate, Migrate, Innovate: A Well-Aligned Plan for Data Warehouse Migration + Challenges + How Do We De-Risk It

Importance of Data Warehouse Migration to Cloud or to New Vendor

A Well-Aligned Plan for Data Warehouse Migration

1. Pre-Migration Preparation

Key Roles in Data Warehouse Migration Team

Skills and Training Required for Data Warehouse Modernization

Full Assessment of Your Existing Data Warehouse

On-Premises Preparation for Data Migration

2. Data Warehouse Migration Strategy and Execution

Migration Goals

Migration Approach

Migration Scope

Defining What Has to be Migrated

Migration Control

Migration Testing

3. Post-migration

Data Validation and Quality Assurance

Performance Tuning

Backup and Disaster Recovery

Monitoring and Alerting

👉 Explore our data engineering expertise and offerings

Challenges to Address During Data Warehouse Migration

1. Data Quality Assurance

2. Scaling for Large Datasets

3. Navigating Data Transformation and ETL Process

4. Schema Mapping and Compatibility

5. Rigorous Data Validation and Testing

6. Mitigating Downtime and Business Impact

How We De-Risk Data Warehouse Migration Project?

👉 Check out our case study on leveraging Snowflake for a car sharing platform

Our Expert Data Warehouse Migration Services

We're Here to Help You

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