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Tue, April 21DataTech BackEndOpsTech
Building reliable ETL pipelines for MongoDB requires balancing speed, complexity, and governance. As data volumes grow and use cases expand across analytics and compliance, traditional approaches can become brittle and time-consuming. This session explores how AI-assisted techniques are reshaping MongoDB ETL design, using real-world scenarios to demonstrate practical approaches.
The talk covers how natural-language-driven pipeline creation, automated transformations, and unified workflows can simplify common challenges such as data masking, aggregation for analytics, and event streaming with Kafka. It focuses on modern ETL patterns that reduce operational friction, shorten development cycles, and make MongoDB data pipelines easier to build, evolve, and govern.
What You Will Learn
How to build MongoDB ETL pipelines using natural language with AI-generated transformations
How to handle real-world use cases such as data masking, analytics aggregation, and Kafka-based event streaming
How AI-assisted workflows can reduce pipeline development time and operational complexity
Who Should Attend
Data engineers
Backend developers working with MongoDB
Platform and infrastructure engineers
Architects designing data pipelines
Teams working on analytics, compliance, or event-driven systems
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Siamion Makarski is a technology leader specialising in large-scale architecture and engineering strategy. He partners closely with engineering, Product, Sales, and Marketing teams to align technical direction with business goals. Siamion has led teams of all sizes and successfully delivered complex systems across multiple companies. Previously, he held leadership roles at HelloFresh, driving scalable, high-performance solutions in a fast-paced global environment.
