Chapter 2
Pipeline Modeling and Workflow Orchestration
Step behind the scenes of sophisticated data engineering with this chapter's deep dive into the theory and practice of pipeline modeling and orchestration. Here, you'll learn to wield Conduit.io's declarative power for crafting dynamic, resilient data workflows that can evolve as quickly as your organization's needs. Experience firsthand how advanced orchestration techniques transform static configurations into living, responsive data systems.
2.1 Declarative Pipeline Specification
Conduit.io employs a declarative approach to defining data pipelines, allowing the specification of complex topologies through configuration files written in YAML, JSON, or HashiCorp Configuration Language (HCL). This model emphasizes the what of the pipeline structure and behavior, rather than the how, granting engineers clear, modular, and concise control over data flow architectures. Declarative specification abstracts away low-level procedural details that traditionally complicate pipeline management, directly contributing to enhanced robustness and maintainability across large-scale deployments.
At its core, a Conduit.io pipeline consists of three fundamental elements: sources, stages, and destinations. Each element is defined as an independent configuration block within a structured document. Sources represent the ingress points for data streams ranging from message queues, log files, or IoT feeds, while stages embody processing units responsible for transformation, enrichment, filtering, or routing. Destinations supply the egress endpoints, which may include databases, analytic platforms, or real-time dashboards. This modular partitioning promotes reusability and compositionality, where stages can be chained or combined into topologies that reflect complex processing pipelines.
The declarative syntax in YAML, JSON, and HCL exhibits a consistent logical schema. For example, a minimal pipeline may be expressed as follows in YAML:
sources: - name: kafka_source type: kafka config: brokers: ["kafka1:9092", "kafka2:9092"] topic: sensor_data stages: - name: json_parser type: parse_json - name: filter_temp type: filter config: condition: "temp > 25" destinations: - name: timeseries_db type: influxdb config: url: "http://influxdb.local:8086" database: sensor_metrics Here, the sources block assigns the source named kafka_source to ingest from a Kafka topic. The stages array declares two sequential processing steps: parsing input as JSON and filtering on a temperature condition. Finally, the destinations block routes resulting data to an InfluxDB instance. Notice the clarity with which one describes the data flow, independent of implementation details such as threading, error handling, or deployment mechanics; these are automatically managed by the Conduit runtime.
Validation is an essential aspect of declarative pipeline configuration. Conduit.io performs schema validation against a predefined JSON Schema or HCL specification to catch misconfigurations or incompatible component settings early. For instance, supplying an invalid broker address or missing a mandatory stage type triggers immediate validation errors, preventing the pipeline from deploying. Such static checks reduce runtime failures and improve operational stability, particularly critical when scaling pipelines to hundreds of components or nodes.
Composition of pipeline components through configuration fosters flexible topology design. Complex directed acyclic graphs (DAGs) emerge naturally from referencing named sources and destinations within stages, or by attaching multiple outputs to one stage. For example, branching and merging data streams can be encoded without verbose scripting, illustrating an advantage of declarative specification that simplifies thought and documentation of intricate flows. Consider this JSON snippet illustrating parallel processing branches:
{ "sources": [ ...
