Migrating from Traditional Databases to Databricks: A Strategic Path to Data Modernization

Birlasoft Databricks

Background:

As organizations increasingly rely on data for competitive advantage, the limitations of

traditional databases become more apparent. These legacy systems, designed for structured

data and on-premises hardware, struggle to keep up with today’s demands for scalability, real-

time analytics, and integration of diverse data sources.

Enter Databricks: a cloud-based platform that integrates data engineering, machine learning,

and analytics into a single ecosystem. This blog outlines how migrating from traditional

databases to Databricks can accelerate data modernization and provides a detailed migration

plan to guide organizations through the process.

Why Migrate to Databricks?

Before diving into the migration process, it’s important to understand the key benefits of

Databricks for data modernization:

1. Scalability and Elasticity: Traditional databases often struggle to scale as data

volumes increase. Databricks, built on the cloud, offers horizontal scalability, allowing

businesses to handle large datasets and scale resources up or down as needed.

2. Unified Analytics Platform: Databricks provides a unified platform that integrates data

engineering, data science, and business analytics. This reduces the need for multiple

tools and simplifies collaboration across teams.

3. Real-Time Data Processing: With Databricks, real-time data pipelines can be built

using Apache Spark, ensuring that insights are delivered as data is ingested, rather than

relying on batch processing.

4. Support for Structured and Unstructured Data: Databricks is not limited to relational,

structured data. It also supports unstructured, semi-structured (e.g., JSON, Parquet),

and streaming data, making it ideal for modern data architectures.

5. Machine Learning Integration: By combining data and ML in one platform, Databricks

enables quicker experimentation, model training, and deployment, all within a shared

environment.

6. Cost Efficiency: Databricks offers the flexibility to pay for what you use, eliminating the

overhead costs associated with managing on-premise infrastructure and under-utilized

hardware.

Migration Plan: From Traditional Databases to Databricks

Migrating to Databricks involves careful planning and execution to avoid disruption and ensure

that business objectives are met. Below is a detailed migration plan, divided into five key stages:

1. Assessment and Strategy Planning

As-IS State Analysis: Begin by assessing your existing database infrastructure,

including the type of databases in use (e.g., SQL Server, Oracle, PostgreSQL), data

size, performance bottlenecks, and security protocols.

Data Audit: Identify and categorize the types of data—structured, semi-structured, and

unstructured—that are managed by traditional databases. Evaluate the data’s criticality,

access patterns, and regulatory requirements.

Use Case Identification: Determine the specific use cases that will benefit from

Databricks, such as advanced analytics, real-time processing, or machine learning. Align

the migration objectives with these use cases.

Skill Assessment: Evaluate your team’s familiarity with Databricks, Apache Spark, and

cloud technologies. If necessary, invest in upskilling to ensure a smooth transition.

2. Migration Architecture Design

Hybrid or Full Cloud: Decide if you’ll be operating in a hybrid environment (where some

data remains on-premise) or fully in the cloud. For hybrid setups, consider using

Databricks Connect to seamlessly integrate existing data sources.

Data Lake Strategy: Implement a data lake architecture on cloud storage like Azure

Data Lake or AWS S3. Databricks thrives with data lakes, enabling scalable and flexible

storage of diverse data types.

Security and Governance: Ensure that your data migration adheres to regulatory

requirements. Databricks offers robust security features such as role-based access

control (RBAC), encryption, and GDPR compliance tools.

ETL Pipelines: Design Extract, Transform, Load (ETL) pipelines that will move data

from the traditional database to Databricks. Tools like Azure Data Factory or AWS Glue

can be used for efficient migration.

3. Data Migration and Validation

Schema Mapping: Convert the schemas of your traditional databases to fit the

Databricks ecosystem. Databricks supports Delta Lake, which is ideal for handling slowly

changing dimensions and schema evolution.

Incremental Migration: Rather than moving all data at once, opt for an incremental

approach. Migrate data in small, manageable chunks to minimize risk and avoid

downtime.

Quality Assurance: Validate the integrity and accuracy of the migrated data

through comprehensive testing. Use tools like Great Expectations to automate data

quality checks in Databricks.

Delta Lake Implementation: Leverage Delta Lake for better data management. It

allows you to version data, ensures ACID compliance, and offers better performance

over raw data lakes.

4. Operationalization and Integration

Integration with Legacy Systems: Set up necessary connectors to allow Databricks to

interact with legacy systems during the transition. This helps maintain business

continuity as legacy and cloud systems co-exist.

Data Access Layers: Implement data access layers using Databricks SQL for users

who need SQL-based access to the data. Use the Databricks Lakehouse architecture to

serve both analytics and operational queries from the same data source.

Automation and Monitoring: Set up automated workflows to manage data pipelines,

using tools such as Databricks Workflows or cloud-native services. Implement

monitoring tools to keep track of job performance and system health.

Training and Adoption: Foster a data-driven culture by providing training on Databricks

to all stakeholders, including data engineers, data scientists, and business analysts.

Promote collaboration using Databricks’ collaborative notebooks.

5. Optimization and Scaling

Performance Tuning: After migration, continuously optimize query performance by

adjusting partitioning, caching, and optimizing joins. Monitor resource usage to ensure

cost-effectiveness.

Advanced Analytics and AI: Enable advanced analytics use cases, such as predictive

modeling and real-time data streaming. Leverage Databricks’ MLflow for managing the

full machine learning lifecycle.

Scalability Planning: As data volumes grow, Databricks can scale elastically.

Implement proactive scaling policies to handle peak loads and prevent bottlenecks.

Cost Management: Use Databricks’ cost management features to track resource

utilization and optimize spending. Employ best practices such as shutting down unused

clusters and using spot instances to reduce cloud costs.

Summary

Migrating from traditional databases to Databricks is a key step towards achieving data

modernization. By moving to a cloud-native, scalable, and integrated platform, organizations

can unlock the full potential of their data, enhance analytics, and drive innovation. However, a

successful migration requires careful planning and execution, following a structured approach

that addresses data architecture, security, and operationalization. With the right strategy in

place, organizations can future-proof their data infrastructure and gain a competitive edge in

today’s data-driven world.

By following the migration plan outlined in this blog, businesses can transition seamlessly to

Databricks and accelerate their journey toward data modernization.

Key Takeaways:

• Databricks enables scalable, real-time data processing that traditional databases cannot match.
• A well-planned migration strategy is essential for minimizing risk and disruption.
• Integration, validation, and optimization are critical to ensuring a smooth transition to

Databricks.