Netflix just achieved a remarkable 75% performance boost and 28% cost reduction through its latest data warehouse migration to Amazon Aurora in December 2025. This demonstrates the transformative power of strategic data warehouse migration in 2025. The global data migration services market size was USD 10.55 billion in 2025 and is projected to touch USD 27.24 billion by 2033 , exhibiting a CAGR of 12.59%.
Enterprises are flipping to modern, cloud-native data warehouses that offer better scalability & flexibility. They are discovering that legacy systems simply cannot support the velocity and volume of AI-driven analytics demands,aking migration not just beneficial but essential for survival in today’s data-centric landscape.
This blog explores the entire process of data warehouse migration, including why data warehouse migration is important, the problems it addresses. You’ll also learn about important migration phases, and proven best practices to ensure a smooth, successful transition to the modern data era .
Key Takeaways Data warehouse migration is an important step in the modernization of enterprise data infrastructure and real-time analytics. A successful migration requires careful assessment, planning and governance – and not just technology replacement. Migration strategies such as lift-and-shift, replatforming, and refactoring should be chosen based on data maturity and business goals. Tools such as Databricks, Snowflake, BigQuery and Azure Synapse have a high role to play in cloud modernization. Following some best practices including pilot testing, automation, governance and team training result in more seamless transitions. Simplify Your Decision Between Informatica and Databricks Work with Kanerika to Build Scalable AI Solutions
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Understanding Data Warehouse Migration Data warehouse migration is the process of transferring data, workloads and analytical pipelines from legacy or on-premises data warehouse systems to modern data warehouse architectures and systems in the cloud, hybrid cloud or lakehouse architectures. It’s a critical step in modernizing an organization’s data ecosystem, ensuring scalability, flexibility and long-term performance.
The main objective of migration is to increase scalability, performance, and cost of infrastructure, while adding advanced analytics , AI, and reporting in real-time. Modern cloud data warehouses separate storage and compute so that enterprises only scale resources on demand and pay for what they use.
There are several common migration paths:
On-premises systems like Teradata or Netezza & cloud-native platforms such as Snowflake or Azure Synapse. Between cloud providers, for instance, from Amazon Redshift to Google BigQuery. From traditional ETL-based architectures to ELT-based lakehouse models that unify structured and unstructured data .
Why Organizations Migrate Their Data Warehouse 1. Cost Efficiency To start with, one of the biggest driving forces for data warehouse migration is cost efficiency. By transitioning from costly on-premises infrastructure to cloud based or modern infrastructure, organizations save a lot of money in hardware, licensing, and infrastructure maintenance costs. As a result they benefit from predictable pricing models and reduced total cost of ownership.
2. Scalability In addition, cloud data warehouses have the feature of elastic scalability. Enterprises can easily deal with massive amounts of data and high user concurrency — with separate storage and compute scaling independently as workloads fluctuate. This flexibility guarantees consistent performance, even under heavy analytical loads.
3. Agility Furthermore, migration enables business agility. Modern architectures enables users faster analytics, & support for self-service BI. IT also supports advanced AI and ML workloads, allowing teams to make data-driven decisions in real-time.
Legacy systems often rely on slow, batch-based data processing . By contrast, modern data warehouses support real-time or near-real-time ingestion, delivering quicker insights and enabling instant response to changing business needs .
5. Integration Modern data platforms integrate seamlessly with APIs, SaaS applications, and cloud services, improving interoperability across the enterprise ecosystem.
For example, Netflix transitioned away from Oracle towards cloud-based systems to accommodate its exponential streaming data growth and scale globally. Ultimately, organizations experience faster insights, reduced latency, and stronger, data-driven decision-making after a successful migration.
Data Warehouse Migration Assessment and Planning 1. Assess the Current Environment Understanding your existing data landscape is the key to any successful migration. This is a whole system assessment that uncovers system complexity and potential challenges before they become costly problems.
Create complete inventory – Document all databases, warehouses, file servers and applications Map data sources – Identify systems feeding reports and analytics platforms Document ETL processes – Document how data flows from one system to another and who created them Identify dependencies – Identify the systems that depend on other systems for data or services Track user access patterns – Analyze who accesses what data, when, and through which reports or direct queries
2. Define Business Goals There should be clear and measurable business objectives to drive every migration decision. Without clearly defined goals, organizations have little way of determining whether their migration investment brought an expected return.
Cost reduction targets – Make quantifiable savings from infrastructure and maintenance elimination Performance benchmarks – Establish benchmarks for quicker queries and report generation Governance requirements – Define standards for data quality, security and compliance AI enablement capabilities – Make sure that platform has machine learning and advanced analytics capabilities
3. Assess the Quality and Readiness of Data Migrating poor-quality data simply transfers existing problems to expensive new platforms. A thorough data quality assessment prevents these issues from undermining modern infrastructure benefits.
Profile existing data – Analyze completeness, accuracy, and consistency across all sources Identify duplicate records – Find repeated customers, products, or transactions with variations Address incomplete records – Locate gaps in essential data fields Standardize formats – Ensure consistent patterns for dates, phone numbers, and addresses
4. Choose the Right Time and Resources Strategic timing and appropriate resource allocation have a major influence on success of migration. Poor timing messes up operations while inadequate resources cause rushed implementations.
Plan around Business Cycles – Do not plan for peak periods such as holidays or quarter-end closing Determine downtime windows – Select phases where unavailability has minor effects Assemble cross-functional teams – Involve engineers, architects, analysts, business owners Secure adequate resources – Make sure adequate budget and achievable timelines are in place
5. Risk and Compliance Checks Regulatory requirements imply mandatory constraints on migration approaches. Understanding these requirements early prevents costly violations and ensures platform compliance.
Evaluate regulatory requirements – Understand GDPR, HIPAA, and industry-specific requirements Evaluate data residency restrictions – Determine laws that put restrictions on where data can be kept Develop rollback plans – Develop procedures for rolling back in case migration runs into problems Establish monitoring capabilities – Implement integrity verification and audit reports systems
Data Warehouse Migration Strategies 1. Lift and Shift (Rehosting) This approach takes your existing data warehouse “as is” and transfers it to the cloud infrastructure without needing to redesign the underlying architecture or business logic.
Pros:
Fastest migration approach – completed in weeks rather than months Minimal code change and technical modifications needed Reduced upfront implementation costs and reduced project complexity Cons :
Carries forward legacy system inefficiencies and performance bottlenecks Misses opportunities for cloud native optimizations and cost savings Limited Long Term performance enhancements and Scalability benefits 2. Replatforming (Modernization) This approach entails migrating to modern cloud data warehouse platforms such as Snowflake, Azure Synapse or Google BigQuery while redesigning data pipelines to take advantage of new data warehouse capabilities. It makes a compromise between speed and optimization.
Key Activities:
Rebuild ETL/ELT pipelines that are designed for cloud scalability and performance Implement workflow automation and orchestration tools Optimize data models to take advantage of new features of the platform Enable elastic compute and storage separation to be cost efficient
This approach provides better performance, lower costs, and improved scalability than lift-and-shift without the complexity and timeline of complete redesign.
3. Refactoring (Rearchitecting) Complete redesign of your data architecture based on modern architectures such as data lakehouse or data mesh . This is a holistic approach to system rebuilds from the ground up to support advanced analytics and emerging technologies.
Ideal For :
Organizations adopting the AI and machine learning workloads scale Companies that need the ability to perform real-time streaming analytics Businesses putting enterprise-wide self-service analytics into practice
This strategy has the greatest up-front cost and provides the greatest long-term value in terms of modern architecture.
4. Hybrid Migration A step-by-step method which increments migration of workloads while mission-critical systems remain on-premises during the transition phase. This strategy means the least risk and provides organizations with the option to develop expertise on the cloud over time.
Strategy :
Move less critical workloads and development environments first Conduct parallel systems through large validation and testing phases Synchronize data in the on-premises and cloud environments Migrating high risk, mission-critical applications last with proven processes
This means that the approach is paving the way for a smooth transition with comprehensive testing, reduced business disruption.
5. Real-Time Migration Uses Change Data Capture (CDC) technology for continuous synchronization for near zero downtime migrations. This approach is essential for systems that can’t have long outages or service interruptions.
Technical Approach :
Do initial full data load to target cloud platform Implement CDC to capture ongoing changes in source systems Maintain the real-time replication and sync of data Execute final cut over with minimum minutes downtime.
Best For: Always-on systems with the need for 24/7 availability, financial services with 24/7 trading requirements, e-commerce platforms and global operations with different time zones
Tools and Platforms for Data Warehouse Migration Successful data warehouse migration requires both powerful data warehouse migration tools and modern data warehouses. Here’s a breakdown of top solutions in both categories..
1. Leading Cloud Data Warehouses Organizations today have a number of good choices when it comes to a target data warehouse platform .
Snowflake Multi-cloud architecture is deployed on AWS, Azure, and Google clouds , which provides flexibility and portability. It features elastic scaling which separates compute and storage with the ability to scale them independently.
Google BigQuery Serverless platform removes the infrastructure management completely. Pay-per-query pricing model only charges based on the data scanned and hence cost-effective for variable workloads. Designed for massive analytics of petabytes and big data processing. Best suited to those organizations where ease of use and analytics performance are a key organizational priority.
Amazon Redshift AWS native solution integrates well with s3 data lakes and AWS Glue for ETL. Cost-effective pricing options with reserved instances save a lot of money. High performance for structured data warehouse workloads. Perfect for the organizations that are already invested into the AWS ecosystem.
Azure Synapse Analytics Tight integration with Microsoft ecosystem including Power BI, Azure Data Factory and Microsoft 365. Excellent for hybrid environments using on-premise and cloud data. Unified analytics workspace is a combination of data warehousing, big data , and data integration. Best choice for Microsoft-centric Organizations
2. Migration & ETL Tools Different tools serve different migration needs and technical requirements.
Informatica and Talend Both provide enterprise-grade ETL automation with comprehensive data quality features. Strong governance capabilities ensure data accuracy throughout migration. Support complex transformations and validation rules.
Matillion and Fivetran Cloud-native tools designed specifically for ELT patterns. Matillion excels at in-warehouse transformations leveraging cloud compute power. Fivetran offers pre-built connectors for fast source integration. Ideal for analytics teams wanting rapid deployment.
Selection Factors Compatibility – Ensure tools support both source systems and target platforms. Check for pre-built connectors reducing custom development needs. Ease of Use – Evaluate learning curve for your team’s skill level. Low-code/no-code options accelerate implementation for less technical users. Monitoring – Look for built-in dashboards tracking migration progress and data quality. Real-time alerts identify issues before they impact production systems. Licensing Model – Compare subscription vs. consumption-based pricing. Consider total cost of ownership including infrastructure, training, and support.
The right combination depends on existing infrastructure, team expertise, migration complexity, and budget constraints. Many organizations use multiple tools—for example, Fivetran for initial data loading combined with Databricks for transformation and analytics .
Best Practices for Successful Data Warehouse Migration 1. Assessment & Planning Take inventory of your existing data sources, schemas, and how they work together. Write down data volumes, transformation rules, what reports you need. Have specific goals, deadlines, and keep everyone in the loop from the beginning.
2. Migration Strategy Selection Select between big bang, phased rollouts, or hybrid approaches based on business requirements and risk tolerance. Consider Zero downtime migration tools for mission critical systems. Evaluate the decision of whether to rebuild systems cloud-native or do lift-and-shift base upon long-term goals.
3. Data Validation & Testing Set up automatic checks to ensure that the quality of data remains good throughout the move. Compare results of old and new systems so that problems can be caught early. As a rule, run all kinds of tests before going live.
4. Data Validation & Testing Implement end to end encryption for data in-transit and at rest when migrating data. Adhere to rules, such as GDPR, HIPAA, that apply to your industry. Control access to what and who can input what and keep a record of all the changes.
Utilize AI tools that can convert schemas and map data automatically to save time and minimize mistakes. Implement automated ETL/ELT pipelines for data transformation and loading . Monitoring tools can be used to track progress and identify issues early.
Design your new system for scalability and future growth requirements. Set up indexes and organize data in ways that make queries run faster. Consider modern setups that will be great for both storage and analysis.
What are Common Pitfalls to Avoid During Data Warehouse Migration ? 1. Skipping Data Quality Checks Migrating poor-quality data leads to inaccurate reports and unreliable insights. Always validate data before moving it. Set up automatic quality rules which check for business logic failures and flag down anomalies during migration. Write comparison reports between source and target systems to make sure row counts, checksums and key metrics are a 100%.
2. Underestimating Complexity Legacy schemas, ETL logic, and dependencies often need redesign. Ignoring this increases migration time and risk. Map out all data flows, stored procedures and custom functions that are to be converted to cloud native alternatives. Moreover, Plan additional testing time for testing complicated transformations and edge cases
3. Ignoring Governance The uncontrolled movement of data can lead to compliance and security problems. Also, one should have clear policies for access, lineage and auditing. Establish standards for classifying data; and encrypt sensitive data when transferring and storing it. Set up proper user access controls and monitor those who can see what data in the new system.
4. Poor Cost Estimation Cloud resources scale fast, so without monitoring and cost limits, expenses can rise quickly. Therefore, set up billing alerts and spending caps before migration begins so that there are no surprises in the bill. Moreover, make storage tier choices and compute sizes according to actual usage patterns not peak capacity.
5. Rushing Cut-Over Avoid the habit of migrating everything at the same time. If it is done in phases, it provides for testing and validation and minimum downtime. Therefore, start with non-critical systems in order to figure out problems before you move important business data. Additionally, keep old and new systems running together for a while so that you can switch back in case something goes wrong.
Real-World Success Stories: How Kanerika Migration Accelerators Simplify Complex Data Migrations
Case Study 1: Transforming Retail Reporting and Analytics with SQL to Microsoft Fabric Migration Client Challenge: A big retail company had messy reporting systems spread across different SQL databases. Getting sales reports took hours because data lived in separate places. Their IT team spent too much time fixing broken reports instead of helping the business grow. Monthly reports often had wrong numbers because data didn’t match up.
Kanerika’s Solution : We used Kanerika’s migration accelerator to move all their old SQL reports to Power BI on Microsoft Fabric, making everything run faster and more reliably. We automated all the manual data prep work by putting business rules into Fabric pipelines so teams didn’t have to do boring data checks anymore. We replaced their messy old reporting systems with one clean Microsoft Fabric setup that answers questions much quicker.
Impact Delivered:
Sales teams now see live inventory numbers instead of day-old data IT team freed up 20 hours per week to work on new business projects 74% Faster Reporting Cycles
Case Study 2: Moving from Informatica to Talend with Smart Automation Client Challenge : The company had a big Informatica system that cost too much and was hard to maintain. License costs kept going up every year. Their data workflows were complicated, and any changes required lots of manual work. They couldn’t modernize because migrations would take forever.
Kanerika’s Solution : We used FLIP technology to automatically convert Informatica mappings and business rules into Talend format. Our FIRE tool pulled out all the repository data so we could create new Talend jobs without rebuilding everything by hand. We tested all outputs carefully and set everything up to work in the cloud.
Impact Delivered :
70% reduction in manual migration effort 60% faster time to delivery Better stability through accurate logic preservation
Kanerika holds the Microsoft Data Warehouse Migration to Azure Specialization, making us a trusted partner for organizations looking to modernize their data platforms efficiently and securely. Modernizing legacy systems opens up better data access, live analytics, cloud solutions that grow with you, and smart AI-powered decisions.
Old migration methods can be messy, expensive, and full of mistakes. We fix these problems with our migration accelerators powered by FLIP that make transitions smooth, accurate, and reliable. Our accelerators handle many different migrations like Tableau to Power BI, Crystal Reports to Power BI, SSRS to Power BI, SSIS to Fabric, SSAS to Fabric, Cognos to Power BI, Informatica to Talend, and Azure to Fabric.
We use automation, ready-made templates, and deep knowledge to help companies cut downtime, keep data safe, and get teams using new analytics platforms faster. With Kanerika, businesses can confidently prepare their data systems for the future and get the most value from every migration project.
FAQs 1. What is Data Warehouse Migration? Data warehouse migration is the process of transferring data, workloads, and analytics pipelines from legacy or on-premises systems to modern platforms such as cloud or hybrid data warehouses. The goal is to improve scalability, performance, and accessibility while reducing maintenance costs.
2. Why do organizations migrate their data warehouse? Organizations migrate to modernize infrastructure, cut costs, handle growing data volumes, and enable real-time analytics and AI. Cloud platforms like Snowflake, Databricks, and BigQuery offer flexibility, faster query performance, and seamless integration across tools.
3. What are the main steps in a data warehouse migration? A data warehouse migration starts with assessing the current environment, including data sources, schemas, and reports. Next, the target platform and architecture are defined based on business and performance needs. The data is then cleaned, mapped, and migrated using a phased or full approach. After migration, data is validated and tested for accuracy and performance. Finally, users are switched to the new system and the old warehouse is retired.
4. How long does a typical data warehouse migration take? A typical data warehouse migration can take anywhere from a few weeks to several months. The timeline depends on data size, system complexity, number of sources, and level of transformation needed. Smaller migrations with limited data and reports move faster, while large, enterprise setups take longer. Proper planning and testing can significantly reduce delays.
5. What are the biggest challenges during migration? The biggest challenges during a data warehouse migration include poor data quality, unclear requirements, and complex legacy systems. Downtime risk and data loss are also common concerns during the move. Report and dashboard breakages can slow adoption if not tested early. Lack of user training often causes issues after go-live.
6. Which tools are used for data warehouse migration? Common tools used for data warehouse migration include ETL and data integration tools like Informatica, Talend, and Fivetran. Cloud-native services such as Azure Data Factory, AWS Glue, and Google Dataflow are also widely used. Database migration tools help move schemas and data efficiently. Data validation and testing tools are often used to ensure accuracy after migration.
7. How can businesses ensure a successful migration? Businesses can ensure a successful migration by starting with clear goals and a detailed migration plan. Cleaning and validating data before moving reduces errors later. Running phased migrations with proper testing helps avoid downtime. Training users early ensures faster adoption and smoother operations after go-live.
