How We Migrated Snowflake Data Pipelines to a Databricks Lakehouse on AWS for a Data Services Organization

Distributed data pipelines and legacy infrastructure complexity

The client's data environment relied on Snowflake for data warehousing, while several data pipelines operated through Matillion and EC2-based scripts. As data volumes and analytics requirements increased, managing pipelines across multiple platforms introduced operational complexity and inefficiencies. The environment lacked standardized processes for data ingestion and transformation, making it more difficult to manage both historical and incremental data loads.

In addition, governance and access control policies were distributed across different systems, limiting visibility and consistency in data management. These challenges made it difficult to maintain a unified and scalable data platform capable of supporting the organization's growing analytics and data processing needs.

Databricks-powered data lakehouse modernization on AWS

zeb designed and implemented a modern data platform using Databricks on AWS, transforming the client's distributed Snowflake, Matillion, and EC2-based environment into a unified and scalable lakehouse architecture.

• Architecture Assessment and Design: Analyzed the existing Snowflake data warehouse, Matillion pipelines, and EC2 scripts to understand data flows, transformation dependencies, and processing requirements.
• Pipeline Migration and Refactoring:Migrated and converted legacy pipelines into automated Databricks workflows to enable centralized orchestration and streamlined data processing. The engagement included migrating 10 tables, converting 4 Matillion pipelines, and refactoring 6 EC2 pipelines into Databricks-based processing jobs.
• Lakehouse Architecture Implementation: Implemented the Databricks Medallion architecture to organize data processing across structured layers. Raw data was ingested into the Bronze layer, cleansed and standardized in the Silver layer, and curated datasets were prepared in the Gold layer to support analytics and downstream data consumption.
• Governance and Data Quality Framework: Implemented Unity Catalog to establish centralized governance across the data platform, including role-based and attribute-based access controls, metadata management, and data lineage visibility. A data quality framework was integrated into the pipelines to validate datasets and maintain consistency throughout the data lifecycle.

Streamlining data operations and enabling scalable analytics

The Databricks Lakehouse implementation created a unified data environment capable of supporting growing analytics needs.

• Unified Data Infrastructure: Migrating to Databricks unified distributed pipelines into a single platform
• Improved Pipeline Efficiency:Automated workflows simplified pipeline management and reduced dependency on multiple legacy scripts.
• Centralized Governance: Unity Catalog provided consistent access control policies and improved visibility across datasets.
• Foundation for Advanced Analytics:The Medallion architecture established a structured environment ready for future analytics initiatives.

Modern data environments require scalable architectures, reliable pipelines, and strong governance frameworks. zeb, a trusted Databricks partner, helps organizations migrate legacy data systems, design modern lakehouse architectures, and establish scalable data platforms using Databricks and AWS.

Connect with our experts to discuss how your organization can modernize its data infrastructure and build a reliable foundation for analytics and growth.