On-Premise to Cloud Data Warehouse Migration

Overview

JashDS successfully migrated a data services company's on-premise Teradata database to Azure cloud data lake using advanced automation and big data technologies. The solution leveraged Azure Databricks, Spark SQL, and Azure Data Factory to create production-ready data transformations and automated orchestration processes that modernized the client's entire data infrastructure.

About the Client

One of the leading retailers of the USA requires modernization of its data infrastructure through cloud migration of its existing on-premise data warehouse systems.

The Challenge

The client operated with an existing on-premise Teradata database system that needed to be migrated to a modern cloud-based data lake architecture. The migration required automated schema extraction, seamless data ingestion processes, comprehensive data transformation capabilities, and ongoing synchronization between on-premise and cloud environments while maintaining data integrity and production readiness.

Key Results

Successfully migrated the entire on-premise Teradata database to Azure cloud data lake increasing data visibility and access by 60%.
Implemented automated data synchronization processes, improving data consistency.
Created production-ready aggregated views and transformations, accelerating analytics processing by 80%.

Our Solution

The migration was executed through a comprehensive multi-phase approach utilizing Azure cloud services and big data technologies. Automation scripts were developed using Apache Sqoop and Bash scripting to fetch table schemas from the Teradata database, ensuring accurate metadata transfer.

Shell scripts were created to facilitate data ingestion from the on-premise Teradata warehouse to a 16-node HDFS cluster, followed by transfer to the Azure data lake. This approach provided a robust staging environment for data validation and processing.

Azure Databricks notebooks were developed to run Spark SQL transformations, making the data production-ready through table joins, view creation, and Change Data Capture (CDC) queries. Aggregated views were created for optimized downstream processing and analytics.

Synchronization scripts were implemented on Azure Databricks notebooks to maintain data consistency between the on-premise Teradata warehouse and Azure data lake, ensuring real-time data availability across both environments.

The entire process was orchestrated using Azure Data Factory (ADF) pipelines, with automated email reporting capabilities for monitoring and alerting purposes.

Technologies Used

Apache SqoopTeradataAzure Data LakeAzure Data Factory (ADF)Azure DatabricksHDInsightApache SparkSpark SQLHDFSBash Scripting

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