Are traditional data warehouses holding your business back? In 2025, more companies are shifting to flexible data lake platforms to meet the growing need for real-time analytics, AI integration, and scalable storage. According to Datafold , cloud-to-cloud migrations are on the rise, with many businesses moving from rigid warehouse systems to open lakehouse formats that support diverse data types and enable faster access.
According to IDC, over 70% of enterprises have already begun migrating workloads from legacy warehouses to data lakes or lakehouse platforms for improved performance and cost efficiency. Moreover, as organizations create more unstructured and semi-structured data from IoT devices, social media, and cloud apps, traditional warehouses are struggling to keep up. That’s why companies like Netflix, Airbnb, and Uber have adopted modern data lake architectures, offering scalability, flexibility, and real-time analytics across diverse data sources.
Continue reading this blog to discover why businesses are making the switch, the challenges they face, and how to plan a smooth migration.
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Key Takeaways Businesses are shifting from traditional data warehouses to flexible, scalable data lakes for real-time analytics and AI integration. Data lakes offer cost efficiency, scalability, and support for structured and unstructured data , unlike rigid warehouse systems. Migration enables faster insights, machine learning adoption, and better handling of large, diverse datasets. Challenges include data format mismatches, governance issues, legacy integration, and skill gaps. A well-planned migration involves assessing architecture, defining goals, selecting platforms, and enforcing governance. Tools like Databricks, AWS Glue, Azure Synapse, and Google Dataflow simplify data movement and management . Kanerika helps organizations modernize data systems with secure, compliant, and AI-ready data lake and fabric solutions.
Why Migrate From a Data Warehouse to a Data Lake? Organizations are increasingly adopting data warehouse-to-data lake migration strategies to handle modern data demands. Traditional data warehouses, although efficient for structured data and analytics , often struggle with scalability, cost, and flexibility when handling diverse data formats, including semi-structured and unstructured data. In contrast, data lakes provide a scalable, cost-effective, and flexible environment that supports advanced analytics and machine learning workloads.
Key reasons for data warehouse to data lake migration include: Cost Efficiency: Data lakes store raw data at a much lower cost compared to structured data warehouses.Scalability: They easily scale to handle petabytes of data without performance loss.Data Variety: Capable of storing structured, semi-structured, and unstructured data from multiple sources.Support for AI and ML: Data lakes connect smoothly with analytics and machine learning tools.Real-Time Insights: Enable faster data ingestion and real-time analytics for better decision-making.Future-Readiness: Offer flexibility to adapt to evolving data setups, such as lakehouse platforms.
Migrating from a data warehouse to a data lake allows organizations to modernize their data infrastructure, improve agility, and unlock deeper insights from all types of data.
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Key Differences Between Data Warehouse and Data Lake Feature Data Warehouse Data Lake Data Type Stores structured data only Handles structured, semi-structured, and unstructured data Storage Cost High due to schema enforcement and computation Low, as it uses object storage systems Schema Approach Schema-on-write (defined before data loading) Schema-on-read (defined during data access) Scalability Limited scalability, costly to expand Highly scalable, ideal for large datasets Processing Framework Optimized for SQL queries and BI reporting Supports big data processing , ML, and AI frameworks Performance High performance for structured analytical queries Variable performance depending on data structure and processing Data Freshness Usually batch-processed and updated periodically Enables near real-time data ingestion and processing Use Cases Business reporting, dashboards, compliance analytics Data science, predictive analytics, IoT, and AI-driven insights Cost Model Expensive for large data volumes More cost-effective for massive and diverse datasets Integration Works best with BI tools Integrates easily with analytics, ML, and data visualization tools
Challenges in Data Warehouse to Data Lake Migration Migrating from a data warehouse to a data lake can unlock flexibility and scalability, but it also brings several technical and operational challenges. Additionally, the transition involves managing diverse data formats, new setups, and upskilling the team.
Data warehouses employ a rigid schema-on-write approach, whereas data lakes follow a schema-on-read approach. Furthermore, converting structured tables into formats such as Parquet or ORC often leads to compatibility issues, especially when incorporating streaming or semi-structured data.
2. Data Quality and Governance Without proper validation and lineage tracking, the ingestion of raw data can lead to inconsistencies. Therefore, establishing governance, metadata catalogs, and quality rules is crucial to maintain trust in migrated datasets.
3. Integration With Legacy Systems Legacy pipelines and BI tools built for relational databases may not work smoothly with data lakes. As a result, redesigning ETL workflows and updating dependent systems can add significant complexity.
4. Migration Complexity and Downtime Large-scale migrations can cause delays or downtime. Consequently, maintaining dual systems temporarily and planning rollback options are key to minimizing disruption.
Data lake environments demand skills in Spark, distributed computing, and cloud storage . Moreover, teams may need training and new tools for monitoring, cataloging, and security.
Key Steps in Data Warehouse to Data Lake Migration A structured plan ensures that migration happens smoothly and aligns with business goals.
1. Assess Current Architecture Begin by auditing data sources, ETL processes, and dependencies. Additionally, identify critical datasets and define the scope for migration.
2. Define Business Objectives Clarify why migration is needed — cost reduction, advanced analytics, or real-time data availability. Furthermore, align technical plans with measurable outcomes.
Select a data lake platform based on your cloud environment — AWS (Glue, Lake Formation), Azure (Data Factory, Synapse), GCP (Dataflow), or Databricks for unified analytics and AI.
Decide between ETL (transform before load) or ELT (transform after load). Moreover, establish pipelines for both batch and streaming data, ensuring schema consistency and optimizing performance.
5. Establish Governance and Security Implement a data catalog , enforce access policies, and maintain data lineage. Additionally, ensure compliance with privacy and security standards, such as GDPR or SOC 2.
Test data accuracy , query performance, and cost efficiency after migration. Furthermore, fine-tune partitioning, caching, and compute resources to maximize performance.
Choosing the right tools is crucial for a smooth and efficient migration from a data warehouse to a data lake. The best choice depends on your existing cloud system, data volume, and integration needs. Here’s an overview of the widely used platforms and technologies that simplify and accelerate migration.
1. AWS Ecosystem AWS Glue and AWS Lake Formation are core tools for building and managing data lakes on AWS.
AWS Glue automates ETL processes , handles schema discovery, and supports serverless data movement. Lake Formation simplifies setup, access control, and cataloging, ensuring secure and governed data storage.
2. Azure Ecosystem For Microsoft users, Azure Data Factory and Azure Synapse Analytics provide end-to-end data integration and analytics.
Data Factory enables scalable ETL/ELT workflows with drag-and-drop pipeline design. Synapse brings together big data and data warehouse capabilities, making it easier to query both historical and real-time data.
3. Google Cloud Ecosystem Google Cloud Dataflow and Dataproc are built for large-scale batch and stream processing.
Dataflow handles event-driven data pipelines using Apache Beam. Dataproc provides a managed Spark and Hadoop environment for flexible analytics.
Databricks is a leading choice for enterprises moving to a lakehouse setup. Furthermore, it combines scalable storage, Spark-based data processing , and built-in ML tools, enabling a single platform for ETL, analytics, and AI. Snowflake can also complement a data lake setup, providing a high-performance SQL-based engine for querying curated datasets.
FLIP by Kanerika further enhances this ecosystem by simplifying complex data migration and modernization projects. It offers automated data mapping, quality checks, and schema transformation to accelerate warehouse-to-lake migrations. Designed for flexibility, FLIP integrates seamlessly with cloud ecosystems like AWS, Azure, and GCP, enabling faster, more reliable data movement with minimal manual intervention.
Solutions like Informatica, Talend, and Matillion simplify complex data transformations and ensure consistency during migration. Additionally, they support automation, data quality checks, and integration across multiple systems.
6. Open Source and Storage Technologies Frameworks such as Apache Hudi, Delta Lake, and Apache Iceberg introduce ACID transactions, schema evolution, and time travel to data lakes, making them more reliable and production-ready. Moreover, for workflow orchestration, Apache Airflow and Prefect are popular choices for scheduling, monitoring, and efficiently managing pipelines.
Kanerika: Empowering Seamless Data Warehouse to Data Lake Migration At Kanerika, we help enterprises modernize their data landscape by choosing the correct setup that aligns with their operational needs, data complexity, and long-term analytics goals. Traditional data warehouses are effective for managing structured, historical data used in reporting and business intelligence, but they often fall short in today’s dynamic, real-time environments. Consequently, this is where data lakes and data fabric setups come into play, offering the flexibility to efficiently handle diverse, unstructured, and streaming data sources.
As a Microsoft Solutions Partner for Data & AI and an early user of Microsoft Fabric, Kanerika delivers unified, future-ready data platforms. Furthermore, we focus on designing intelligent setups that combine the strengths of data warehouses and data lakes. For clients focused on structured analytics and reporting, we establish robust warehouse models. For those managing distributed, real-time, or unstructured data, we create scalable data lake and fabric layers that ensure easy access, automated governance, and AI readiness.
All our implementations comply with global standards, including ISO 27001, ISO 27701, SOC 2, and GDPR, ensuring security and compliance throughout the migration process. Moreover, with our deep expertise in both traditional and modern systems, Kanerika helps organizations transition from fragmented data silos to unified, intelligent platforms, unlocking real-time insights and accelerating digital transformation—without compromise.
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FAQs What is the difference between a data warehouse and a data lake? A data warehouse stores structured, processed data for reporting and analytics, while a data lake can store raw, semi-structured, and unstructured data, enabling advanced analytics, AI, and real-time insights.
Why should organizations migrate from a data warehouse to a data lake? Migration helps reduce storage costs, handle diverse data types, improve scalability, and support advanced analytics and machine learning workloads that traditional warehouses cannot efficiently manage.
What are the key challenges in data warehouse to data lake migration? Common challenges include data quality issues, schema mismatches, security and governance setup, integration with existing tools, and ensuring minimal downtime during migration.
Which tools and platforms are best for data warehouse to data lake migration? Popular choices include AWS Glue, Azure Data Factory , Google Cloud Dataflow, Databricks, Snowflake, and migration accelerators like FLIP by Kanerika, which automate data mapping, validation, and transformation.
How long does a typical data warehouse to data lake migration take? The timeline depends on data volume, complexity, and automation tools used. For most enterprises, it can range from a few weeks (with automation tools) to several months for large-scale migrations.
