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Modernizing a Global E-Commerce Data Pipeline with Agentic AI
De-Risk and Accelerate from Legacy to Databricks-ready Workflows
Modernizing a Global E-Commerce Data Pipeline with Agentic AI
The Challenge: Fragmented Legacy Data Pipelines
Imagine a global e-commerce enterprise running on a patchwork of legacy data platforms. Customer analytics live in Amazon Redshift, inventory data sits in on-premises SQL Server, and newer insights might even tap into Snowflake or BigQuery. This fragmented architecture makes it difficult to get a unified view of data and costly to maintain multiple pipelines. Migrating such ETL pipelines to a modern platform is daunting – manual rewrites are slow, error-prone, and costly, often risking broken data lineage or misinterpreted business logic. It’s no surprise that a full migration is typically projected to take well over a year (14–18 months in many cases) with significant uncertainty and risk.
Accelerating Migration with AI-Powered Automation
Xebia’s Agentic Data Pipeline Migrator offers a solution to dramatically speed up and de-risk this modernization journey. Powered by large language model (LLM) intelligence and a multi-agent architecture, this AI-driven tool can automatically convert entire SQL and ETL codebases from legacy systems into Databricks-ready workflows. In our hypothetical e-commerce company’s case, the Migrator would scan all pipeline code across Redshift and SQL Server, then translate each SQL dialect and script into Databricks-compatible code while preserving business logic, data lineage, and performance optimizations. The platform’s heterogeneous mix of Redshift SQL and T-SQL is no longer a barrier – the AI system adapts queries to the target Databricks syntax seamlessly, resolving proprietary functions or macros along the way. All of this happens with minimal human intervention, turning a tedious rewrite project into an automated process.
How the Migration Works
Using the Agentic Data Pipeline Migrator, our e-commerce enterprise could execute a migration to Databricks in a few key steps:
- Comprehensive Codebase Scan: The tool’s analysis agents crawl through existing SQL scripts, stored procedures, and ETL logic across all sources (Redshift, SQL Server, etc.), mapping out dependencies and data lineage. This establishes a clear picture of the legacy pipelines and their interconnections.
- Intelligent Cross-Dialect Translation: Next, translation agents employ LLM-driven intelligence to convert each piece of code into Databricks-native equivalents. Redshift’s SQL and SQL Server’s T-SQL are automatically rewritten into Spark SQL or PySpark, adapting to Databricks’ syntax and performance best practices. Platform-specific elements (like proprietary functions or macros) are resolved or replaced, ensuring the logic remains consistent in the new environment.
- Schema Alignment and Validation: The migrator validates the translated code against target data schemas and business rules. Using built-in SQL linting and schema checks, it ensures that the new queries will run correctly on Databricks and that no business logic was lost in translation. Any discrepancies in data types or logic are flagged early.
- Databricks Workflow Generation: Once translation and validation are complete, generation agents assemble the output into production-ready Databricks workflows. This could mean generating notebooks, Delta Live Tables pipelines, or job scripts that mirror the original scheduling and dependencies. The result is a set of native Databricks pipeline workflows ready to be executed on the Lakehouse platform, without having to start from scratch.
- Lineage Tracking and Optimization: Throughout the process, the system retains full data lineage information, so the enterprise can visualize end-to-end data flows even after migration. It also applies optimizations specific to Databricks (such as leveraging Delta Lake for performance) wherever possible. Importantly, the tool features graceful error handling – if it encounters any non-translatable legacy code, it will skip or mark those elements with comments and continue the migration rather than halting the entire process. This ensures momentum isn’t lost, and any necessary manual fixes are clearly highlighted for developers.
Xebia’s Agentic Data Pipeline Migrator offers a solution to dramatically speed up and de-risk this modernization journey. Powered by large language model (LLM) intelligence and a multi-agent architecture, this AI-driven tool can automatically convert entire SQL and ETL codebases from legacy systems into Databricks-ready workflows. In our hypothetical e-commerce company’s case, the Migrator would scan all pipeline code across Redshift and SQL Server, then translate each SQL dialect and script into Databricks-compatible code while preserving business logic, data lineage, and performance optimizations. The platform’s heterogeneous mix of Redshift SQL and T-SQL is no longer a barrier – the AI system adapts queries to the target Databricks syntax seamlessly, resolving proprietary functions or macros along the way. All of this happens with minimal human intervention, turning a tedious rewrite project into an automated process.
From 18 Months to Weeks – Minimizing Risk and Delay
One of the biggest benefits of an AI-powered migration is the dramatic reduction in project timeline. Traditionally, a team rewriting thousands of SQL queries and ETL jobs by hand might need 14–18 months before the new Databricks platform is fully operational. In contrast, our hypothetical e-commerce company could see this timeline shrink to a matter of weeks with the Agentic Migrator. By automating what would have been weeks of manual effort into mere hours of machine-driven processing, the tool accelerates the move to Databricks without burning months of engineering cycles. This speed does not come at the expense of quality or trust. Every automatically converted script is accompanied by detailed reporting – the Migrator produces high-level migration summaries and code-level analyses that allow the IT team to audit and understand every change. Stakeholders get full transparency into what was changed, what was validated, and if any elements need follow-up. The preserved lineage and side-by-side logic comparisons mean that data engineers and business analysts can verify that critical business logic and performance tuning have carried over intact. Moreover, error-tolerant processing ensures that the overall migration doesn’t stall due to a few edge cases. The end result is a migration that is faster, lower-risk, and thoroughly documented.
Future-Ready Data Modernization with Intelligence
In the era of AI-driven solutions, enterprise data platform modernization no longer needs to be a painful, drawn-out ordeal. Our scenario illustrates how a global e-commerce company could leverage intelligent automation to modernize to the Databricks Data Intelligence Platform quickly and confidently.
By using Xebia’s Agentic Data Pipeline Migrator as an AI-powered accelerator, organizations achieve a fast, trustworthy, and future-ready path to Databricks. The entire process is transparent and auditable, and it transforms migrations from a bottleneck into a catalyst for innovation. For IT leaders and data platform decision-makers, the message is clear: embracing automation and multi-agent AI intelligence in your migration strategy means you can unlock the benefits of a unified Databricks lakehouse in weeks instead of years, with minimal risk and maximum confidence. In the age of AI, such an approach isn’t just advantageous; it’s quickly becoming the new standard for enterprise data modernization.
Do you want to see the Agentic Data Pipeline Migrator in action? Book a demo from this page.
Written by
Matt Gosselin
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