Ingestapt

Ingestapt is a fictional open-source data ingestion framework designed to unify the ingestion, transformation, and routing of both streaming and batch data. The project imagines a single, configurable pipeline that can connect to diverse data sources, apply transformations, and deliver results to multiple sinks with consistency guarantees.

Core concepts include modular connectors, a pluggable processing stack, a built-in schema registry, and support for backpressure and exactly-once semantics. Ingestapt emphasizes a declarative pipeline model where users define sources, transformations, and destinations in a single configuration.

Architecture centers on a small set of components: Ingestapt Core, Connectors, Transforms, Sinks, an Orchestrator, and a Registry. Data flows from a source into the ingest layer, passes through optional transforms, is routed by the orchestrator to one or more sinks, and is written with controlled semantics and error handling.

History and status: Ingestapt originated as a hypothetical design proposed in academic and industry discussions in the 2020s. While used in examples and research to illustrate unified ingestion concepts, it is not an established standard or a single reference implementation. Real-world tooling often combines features from NiFi, Kafka Connect, and stream processing systems.

Use cases and considerations: Typical scenarios include real-time analytics, data lake and warehouse ingestion, and IoT telemetry pipelines. Organizations evaluating Ingestapt-like approaches compare factors such as ease of connector development, schema management, operational complexity, and guarantees around delivery semantics.

See also: Apache NiFi, Apache Kafka, Apache Flink, data integration, streaming data.