Phase 7: Connector SDK, tutorials, and CI workflow#9
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New source connectors: - Kafka (batch + streaming modes, configurable offsets) - MongoDB (collection reads, aggregation pipeline support) - REST API (generic, with offset/cursor pagination, bearer/api-key auth) - BigQuery source (table reads + SQL queries) New sink connectors: - Kafka (batch + streaming with checkpointing) - Elasticsearch (Spark ES connector with REST bulk API fallback) All connectors include healthCheck for weaver doctor. Dependencies: - kafka-clients 3.7.0 (provided, for health checks) Schema: - Updated pipeline.schema.json with new connector types Total connectors: 8 sources + 6 sinks = 14 connectors
LLM Transforms: - LLMTransformPlugin: generic LLM-as-transformation with prompt templating, structured JSON output parsing, batching, caching, retry with backoff - Supports: claude, openai, local (Ollama), any OpenAI-compatible API - Config: batchSize, maxConcurrent, retryOnError, cache, apiKey, baseUrl RAG Transforms: - ChunkingPlugin: fixed, sentence, recursive chunking strategies with configurable size and overlap - EmbeddingPlugin: vector embedding generation via OpenAI/Vertex AI/Cohere APIs, returns embedding column (array of doubles) - GraphExtractionPlugin: entity/relationship extraction using LLM, returns source_entity, source_type, relation, target_entity, target_type Local model support: - All LLM plugins support provider: "local" for Ollama (http://localhost:11434/v1/chat/completions) - No API key required for local models - Custom baseUrl for self-hosted endpoints Total transform types: 6 (SQL, DataQuality, LLMTransform, Chunking, Embedding, GraphExtraction)
Deployment targets: - SubmitCommand: spark-submit wrapper for standalone, k8s, EMR, Dataproc - Dockerfile: JDK 17 + Spark 4.0.2 + data-weaver.jar - docker-entrypoint.sh: container entry point - install.sh: one-line installer (curl | bash) Airflow integration: - DataWeaverOperator: Airflow operator for pipeline execution - Python package (data-weaver-airflow) with pyproject.toml - Template fields for pipeline, env, extra_args Usage: docker run data-weaver:latest apply pipeline.yaml --env prod weaver apply --submit k8s --master k8s://host:6443 --image img:tag weaver apply --submit emr --cluster-id j-XXXXX curl -fsSL .../install.sh | bash
Connector SDK: - CONNECTOR_SDK.md: complete guide for building custom connectors - Source and Sink trait documentation with examples - ServiceLoader registration instructions - Best practices for health checks and config handling Tutorials (3 real-world pipelines with sample data): - 01-csv-to-parquet: CSV read, SQL transform, quality checks, Parquet write - 02-multi-source-join: parallel transforms, multi-source join, JSON output - 03-rag-pipeline: document chunking for RAG preparation - Sample data: customers.csv, orders.csv, docs.json Reusable CI workflow: - weaver-pipeline-ci.yml: GitHub Actions workflow for pipeline validation - Auto-validates all YAML files in pipelines/ directory - Runs inline tests for each pipeline - Reusable via workflow_call for user projects
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Summary
Adds developer documentation, real-world tutorials, and reusable CI workflow.
Connector SDK
Tutorials (3 pipelines with sample data)
Reusable CI Workflow
weaver-pipeline-ci.yml: validates and tests all pipelines in a directoryworkflow_callfor user projectsTest plan
weaver validate