Smarter ETL Starts Here: How to Modernize Data Pipelines for Scale, Speed, and AI Readiness.
Legacy ETL is breaking under the weight of today’s data demands. Petabyte-scale analytics, distributed architectures, and AI initiatives require a smarter, more adaptable approach to data transformation.
In this white paper, “A Guide to Smarter ETL and Data Pipelines with Starburst”, you’ll discover how SQL-based ETL, open table formats like Apache Iceberg, and centralized governance enable faster, more cost-effective, and compliant data operations.
Why Read This Guide:
Traditional ETL pipelines can’t keep up with AI-driven business needs. This guide provides a step-by-step roadmap to modernize ETL, from auditing legacy workloads to adopting open table formats and extending into streaming and ML.
You’ll learn how to modernize incrementally, measure results, and build momentum without disrupting existing operations.
This guide is built for data engineers, architects, and analytics leaders who want to:
- Replace complex Spark jobs with SQL
- Reduce ETL costs and latency
- Govern data consistently across platforms
- Fuel AI and machine-learning pipelines with high-quality, trusted data
Get the guide and discover how to simplify ETL, cut costs, and accelerate AI innovation.
Download now
Legacy ETL is breaking under the weight of today’s data demands. Petabyte-scale analytics, distributed architectures, and AI initiatives require a smarter, more adaptable approach to data transformation.
In this white paper, “A Guide to Smarter ETL and Data Pipelines with Starburst”, you’ll discover how SQL-based ETL, open table formats like Apache Iceberg, and centralized governance enable faster, more cost-effective, and compliant data operations.
Why Read This Guide:
Traditional ETL pipelines can’t keep up with AI-driven business needs. This guide provides a step-by-step roadmap to modernize ETL, from auditing legacy workloads to adopting open table formats and extending into streaming and ML.
You’ll learn how to modernize incrementally, measure results, and build momentum without disrupting existing operations.
This guide is built for data engineers, architects, and analytics leaders who want to:
- Replace complex Spark jobs with SQL
- Reduce ETL costs and latency
- Govern data consistently across platforms
- Fuel AI and machine-learning pipelines with high-quality, trusted data
Get the guide and discover how to simplify ETL, cut costs, and accelerate AI innovation.
