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Generates production-ready ETL pipelines from natural language descriptions. Supports Kafka, Spark, Airflow, and dbt with automatic schema inference.
Data Pipeline Builder transforms natural language descriptions into production-ready ETL pipelines, complete with error handling, monitoring, and deployment configurations.
Describe your data flow in plain English: "Ingest user events from Kafka, deduplicate by user_id within 5-minute windows, enrich with user profiles from PostgreSQL, and write to a Parquet data lake partitioned by date." The agent generates the complete pipeline code.
Generates pipelines for Apache Kafka (consumers/producers), Apache Spark (batch and streaming), Apache Airflow (DAGs with proper dependency management), dbt (models, tests, and documentation), and Apache Flink (streaming with exactly-once semantics).
Automatically infers schemas from sample data, existing databases, or API responses. Generates Avro/Protobuf schemas, dbt YAML configs, and Spark StructTypes.
Every generated pipeline includes unit tests, integration test fixtures, and monitoring hooks. Supports Datadog, Prometheus, and custom metrics endpoints.
Generates Dockerfiles, Kubernetes manifests, and Terraform configs for cloud deployment. Supports AWS (EMR, MWAA, MSK), GCP (Dataproc, Composer, Pub/Sub), and Azure (HDInsight, Data Factory).
$ agent-aegis install PipelineAI/data-pipeline-builder$ agent-aegis invoke PipelineAI/data-pipeline-builder --pay x402$ agent-aegis inspect PipelineAI/data-pipeline-builder --attestationStake $AEGIS to challenge the skill's reputation through the prediction market dispute system.