Modernizing Your Data Lake with Azure Databricks: A Migration Playbook 

Modernizing your data lake isn’t just a tech trend—it’s a business imperative. With global data volumes expected to reach 175 zettabytes by 2025, are your legacy systems equipped to keep up with the scale and speed of modern analytics?  

If you’re dealing with growing data volumes, fragmented analytics workflows, and slower time-to-insight, you’re not alone. Many enterprises find themselves at a tipping point. Enter Azure Databricks—a cloud-native platform that blends the performance of Apache Spark with the scalability and security of Microsoft Azure. 

But let’s be honest: migrating to Azure Databricks without a clear strategy can be overwhelming. We’ve created this interactive migration playbook—to walk you through each step and set you up for a successful transition. 

Whether you’re a data architect planning the migration, a CDO overseeing digital transformation, or an engineering lead tasked with execution, this playbook is designed for you. It’s not just about technology—it’s about aligning people, processes, and tools to transform your data ecosystem.  

Why modernize your data lake? 

Let’s face it—traditional on-premises or first-gen cloud data lakes are slowing businesses down. Here’s why modernizing to Azure Databricks is a game-changer: 

• Elastic scalability: Scale compute and storage independently, optimizing for cost and performance. 
• Unified platform: Combines data engineering, machine learning, and analytics in one collaborative environment. 
• Security and compliance: Enterprise-grade security, native Azure integration, and governance capabilities. 
• Real-time analytics: Accelerate time-to-insight with high-performance Spark-based processing. 
• Collaborative innovation: Enable cross-functional teams to collaborate on data projects using notebooks and version-controlled workflows. 

Still not sure if Azure Databricks migration is right for you? Ask yourself these questions: 

• Are batch jobs taking too long? 
• Is data duplication a problem? 
• Do business users struggle to get timely insights? 
• Is your team spending more time maintaining pipelines than innovating? 
• Are you locked into a platform that’s expensive or hard to scale? 

If you said ‘yes’ to any of the above, it’s time to consider Azure Databricks. 

Your Azure Databricks migration playbook 

Here’s a structured approach to help you plan and execute a successful data lake migration: 

1. Assessment and planning 

Before making any moves, it’s crucial to assess your current environment: 

• Inventory your data sources, volume, and formats 
• Evaluate your existing ETL/ELT workloads 
• Identify performance bottlenecks and operational inefficiencies 
• Define business objectives and KPIs for the migration 

Pro tip: Engage key stakeholders early. Get buy-in from data consumers, IT, and business units to ensure alignment. 

This is also the stage where you set expectations and establish timelines. Consider building a project governance framework and forming a steering committee to review progress periodically. 

2. Define the target architecture 

Azure Databricks enables a modern lakehouse architecture, blending the best of data lakes and data warehouses. Your target stack might include: 

• Delta Lake for ACID-compliant, scalable storage 
• Azure Data Factory or Synapse for orchestration and integration 
• Azure Data Lake Storage Gen2 as your scalable, secure data layer 
• Unity Catalog for centralized governance, lineage, and access control 
• Azure Monitor or Log Analytics for operational insights 

Interactive check: Sketch your current vs. target architecture. Identify dependencies, compatibility issues, and improvement areas. This will guide your transformation blueprint. 

3. Data and workload profiling 

Not all workloads are created equal. Profiling helps determine what needs to be migrated, optimized, or retired. Categorize workloads into: 

• High-complexity jobs: Typically require redesign, refactoring, and deeper testing 
• Repeatable, low-complexity jobs: Perfect candidates for automation tools like LeapLogic 
• Obsolete or redundant jobs: Eliminate these to simplify your ecosystem 

This triage process ensures you focus on what truly delivers business value. Create a migration heatmap to visualize priorities based on complexity and impact. 

4. Migration strategy 

You’ve got options—select a migration strategy that aligns with your goals, risk appetite, and resource availability: 

• Lift and shift: Fastest route, minimal change, but may not leverage cloud-native advantages 
• Replatforming: Moderate effort, adapting workloads to new paradigms (e.g., Spark, Delta Lake) 
• Refactoring: Deep transformation that yields maximum optimization and long-term scalability 

You can also consider a phased approach—starting with pilot workloads and expanding incrementally. 

5. Execution 

Here’s where the rubber meets the road: 

• Set up your Azure Databricks workspace, clusters, and security settings 
• Incrementally migrate datasets, ensuring compatibility with Delta Lake 
• Rebuild or refactor pipelines using Spark, SQL, and ML capabilities within Databricks 
• Recreate data validation logic, alerts, and triggers 
• Update business applications and dashboards to reflect the new data sources 

Best practice: Use CI/CD pipelines with GitHub Actions, Azure DevOps, or Jenkins to streamline deployment and reduce manual errors. 

6. Testing and validation 

Migration success hinges on robust validation: 

• Functional validation: Are jobs producing accurate and consistent outputs? 
• Performance benchmarking: Are data processing tasks faster or more efficient? 
• Security checks: Are RBAC and data masking policies enforced? 
• End-user feedback: Are analytics teams and business users satisfied with the new environment? 

Don’t skip this phase—automated test frameworks and data quality tools can speed up this process significantly. 

7. Optimization and monitoring 

Once migrated, fine-tune your platform: 

• Monitor cluster usage, job runtimes, and resource costs 
• Tune Spark configurations, cache strategies, and shuffles 
• Implement alerts for job failures or data quality issues 
• Use dashboards to track data pipeline performance and reliability 

Tip: Establish a center of excellence (CoE) or tiger team to continuously identify optimization opportunities and promote best practices across departments. 

How Impetus’ LeapLogic automates migration to Azure Databricks 

Manual migration can be time-consuming, error-prone, and expensive. That’s where Impetus’ LeapLogic, an automated solution for data platform modernization and migration services, comes in. Impetus’ LeapLogic automates the end-to-end transformation of legacy workloads to Azure Databricks with minimal risk and maximum efficiency. Here’s how: 

Intelligent assessment 

Impetus’ LeapLogic performs an in-depth analysis of your existing data estate—be it Hadoop, Teradata, Netezza, or other platforms. It maps lineage, identifies code patterns, and highlights optimization opportunities. This upfront intelligence helps create a robust migration plan. 

Automated transformation 

Impetus’ LeapLogic fast-tracks the migration of legacy workloads (like SQL, HiveQL, ETL scripts, and orchestration logic) to Azure Databricks with up to 95% automation. This includes: 

• Syntax and semantic conversion 
• Business logic preservation 
• Dependency resolution and refactoring 
• Pipeline re-architecture using best-fit patterns for Databricks 

Testing and validation 

LeapLogic ensures every migrated workload is production-ready. It automatically generates parallel test cases and provides tools for: 

• Data reconciliation 
• Output comparison 
• Performance benchmarking 

These automated test suites reduce manual effort and build confidence in migration. 

Accelerated deployment 

With CI/CD integration, infrastructure-as-code templates, and reusable components, Impetus’ LeapLogic enables: 

• Rapid prototyping 
• Production-grade deployment 
• Faster onboarding of teams and workloads 

Enterprises can shrink migration timelines from months to weeks. 

Risk mitigation 

Impetus’ LeapLogic identifies potential migration risks upfront—like incompatible UDFs, deprecated APIs, or inefficient joins—and offers actionable remediation. This minimizes disruption and accelerates issue resolution. 

Bonus: LeapLogic also supports hybrid and multi-cloud strategies, making it easier to integrate Azure Databricks into broader modernization initiatives. Whether you’re using AWS, GCP, or other Azure services, LeapLogic ensures interoperability and governance. 

Conclusion 

Modernizing your data lake to Azure Databricks is more than a tech upgrade—it’s a strategic move to future-proof your business. It enables real-time insights, supports AI/ML innovation, and creates a scalable foundation for growth. But to unlock its full potential, migration must be done right. 

With this playbook, you’re equipped to navigate the complexities of migration. And with a partner like Impetus by your side, you can automate and accelerate your modernization journey with confidence—without compromising on performance, security, or business continuity. 

Want to see Impetus’ LeapLogic in action? 

Book a demo to experience automated migration to Azure Databricks—tailored to your data landscape and modernization goals.