laaddata

laaddata is an open-source data loading framework designed to simplify the ingestion of large and heterogeneous datasets into data processing and analytics pipelines. It provides modular connectors, a schema inference engine, and support for both streaming and batch loading, enabling consistent validation and error handling across sources.

The project uses a pluggable architecture that separates sources, transformations, and destinations. The core library emphasizes performance and reliability, with a Rust-based core and bindings for Python and Java to fit common data ecosystems. It supports backpressure-aware streaming, checkpointing, and configurable retry policies to handle transient failures.

Supported data formats and connections include CSV, JSON, Parquet, and relational databases via standard connectors, as well as cloud storage objects. laaddata includes a schema inference system that can infer types from sample data and a validator to enforce constraints such as required fields, ranges, and uniqueness. Optional lightweight transformations and deduplication steps are also available.

Usage typically involves defining a pipeline with a source, optional transformations, and a destination. Data can be loaded in streaming mode for ongoing ingestion or batch mode for bulk loads, with telemetry and error reporting provided for monitoring and debugging.

Status and licensing: laaddata is an active open-source project with contributions from individual developers and organizations. It is released under an open-source license and is designed for use in prototyping and production workloads in moderate-scale environments. The project maintains documentation and example configurations to assist new users.