Accelerating the data modernization journey from Teradata to Azure Synapse analytics

The architecture of analytic data platforms has evolved over the years, progressing from a simple data repository to a data warehouse, then to a data lake, and further evolving into lakehouse and data mesh architectures. Data management has expanded to include the handling of unstructured data, such as documents, media files and streaming data. Modern data architecture offers varied computer capabilities—batch processing, streaming, and in-memory computing—at lesser cost, allowing for consumption based on demand.

Organizations adhering to legacy data platforms forego the advantages provided by newer capabilities offered by modern data architecture on cloud. Legacy data platforms lead to frustration within the business as they are often costly to upgrade and incur increased costs in managing them.

Azure Synapse offers a unified analytics platform, streamlining data modernization efforts. Its integrated analytics services provide real-time insights and support both structured and unstructured data. The platform’s scalability allows organizations to handle growing data volumes efficiently. With advanced AI integration, security features, cloud-based architecture, it ensures a robust and future-ready analytics data platform.

Migrating from Teradata to Synapse – a look at what we delivered for a client

The existing Teradata environment of large retail organization faced challenges in delivering performance and the administration efforts had significantly increased over the last 2 years. The Teradata support contract is set to expire by the end of 2024. There was no interest in transitioning to Teradata cloud as the key requirement was to adopt a modern data platform capable of handling varied workloads with lower maintenance costs.

Modernization approach

1. Assessment/Code analysis
The objective of the code analysis/assessment phase is to comprehensively evaluate the existing Teradata SQL codebase, determining the SQL usage, identifying syntax differences, and uncovering potential challenges. We also determined any optimization opportunities for a seamless transition. During this phase, a detailed report was created outlining the scope of required code modifications, providing a roadmap for the conversion process, and offering insights into potential performance enhancements. This analysis laid the foundation for a successful migration, ensuring that the converted code adhered to Azure Synapse SQL syntax, maintained functionality, and achieved optimal performance in the new environment.

2. MVP: Code conversion and validation
The objective of the MVP phase was to transform the chosen set of Teradata SQL code into Azure Synapse SQL syntax and demonstrate the value. Special attention was given to address syntax differences for a smooth transition. We meticulously validated the converted code to ensure functionality and adherence to Azure Synapse best practices. Additionally, our focus on performance optimization aimed to guarantee that the converted code not only matched but surpassed the efficiency of the existing Teradata code, ensuring optimal performance in the new environment.

3. Factory model migration
Following the successful completion of the MVP, we initiated a Factory Model Migration to deliver the new system in iterations, adhering to a standardized setup of processes and toolsets. This migration process included the following components:

• Modernization POD team: POD team was assembled comprising of a team of Synapse, Teradata experts, developers, and quality assurance professionals specializing in Data reconciliation process.
• Automation tools and framework: We deployed and configured the Ascendion AVA platform, a Low Code Data Modernization platform designed for SQL dialect conversion and Data Migration. The POD team was onboarded to the Ascendion AVA platform to streamline the migration process, ensuring consistency, accuracy, and efficiency in transforming code from Teradata SQL to Azure Synapse SQL.
• Iterative development and testing: An iterative approach was implemented, converting and testing code in manageable batches. Thorough testing was conducted at each iteration, including functional testing to ensure that the translated code maintained its intended behavior and performance testing to validate optimal query execution in the Azure Synapse environment.
• Documentation and knowledge transfer: Throughout the migration process, comprehensive documentation was maintained, capturing insights, challenges, and solutions. Knowledge sharing was facilitated across POD teams and with stakeholders to ensure ongoing acceleration and optimization in the modernization process.

 
Ascendion AVA Low Code Modernization Platform
Ascendion Low Code Data Modernization platform specializes in automating migration from legacy platforms such as Oracle, Informatica, DB2, Teradata, Hadoop. Through the incorporation of advanced automation techniques, our solution excels at expertly handling the migration of schema, data, and processes from various databases and ETL tools to Azure services. This results in a streamlined and efficient transition to modern platforms within the Azure environment.

Ascendion AVA Low Code Modernization Platform

Some of the key benefits delivered includes:

• Conversion was 80% accurate, saving 900+ man hours
• Automatically applied SQL standards and best practices, ensuring code quality and compliance
• Eliminated risk of bugs and performance issues, leading to more reliable and secure system

Explore the 2 weeks free assessment offer at the Azure Marketplace
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