Discover the power of version control, reusability, and automated testing by treating your data pipelines as code. Learn how transform.do makes ETL as code a reality.
A practical, step-by-step tutorial on using the transform.do API to automate the conversion of complex JSON structures into clean, usable CSV files, and vice versa.
Explore the future of data processing. We delve into how agentic workflows on transform.do use AI to autonomously interpret and execute complex data mapping and cleansing tasks.
Reduce time-to-value for new clients by creating a fully automated data ingestion and transformation service with transform.do, freeing up valuable engineering resources.
Learn techniques for programmatically standardizing formats, validating data, and enriching records to turn inconsistent source data into a reliable, high-quality asset.
Follow our guide to connect transform.do with AWS S3 and Lambda to create a powerful, event-driven, and infinitely scalable serverless data processing pipeline.
Break free from monolithic ETL jobs. Learn how turning transformations into composable, API-first microservices enables agile, scalable, and maintainable data architecture.
A 5-step strategic plan for teams looking to move from legacy, UI-based ETL platforms to a more flexible, scalable, and developer-friendly code-based approach.
Tackle a common developer challenge by using transform.do's agentic rules to easily parse, flatten, and reshape deeply nested JSON objects for easier consumption.
A look at our cutting-edge research into using AI to analyze source and target data schemas to automatically suggest and generate the necessary transformation rules.