MLflow 3.13 (tracking + AI Gateway) on this stack. Load when logging experiments from Spark/Python jobs, registering models, or wiring AI Gateway routes to Anthropic / Ollama for LLM-in-pipeline patterns.
Spark Declarative Pipelines (SDP) — the declarative pipeline framework in Spark 4.x. Load when designing, running, or debugging an SDP pipeline. Covers the OSS `pyspark.pipelines` API, dataset primitives, the `spark-pipelines` CLI, and the medallion pattern on this stack.
Delta Lake 4.2 operations on Spark 4.1 in this stack. Load when working with Delta tables specifically — covers OPTIMIZE, VACUUM, time travel, UniForm interop with Iceberg, and when to choose Delta over Iceberg here.
Kafka 3.6 producer/consumer patterns for this stack. Load when wiring Kafka into a Spark job, debugging stream lag, or writing demo producers. Covers topic creation, Structured Streaming integration, schema serialization, and the local test harness.
Apache Spark 4.1 reference. Load when writing PySpark/Spark SQL against this stack — covers DataFrame API conventions, Spark 4.1-specific features (Connect, ANSI mode, streaming UDTFs), and gotchas vs older Spark.
Unity Catalog OSS 0.4.x — the only catalog in this stack. Load when configuring the UC server, creating catalogs/schemas/tables via REST, or wiring a non-Spark engine (DuckDB, Trino) against UC. Covers the REST API surface, credential vending, and the no-JDBC-catalog rule.
Use when starting, stopping, demoing, or troubleshooting the open-lakehouse stack (Spark 4.1, Kafka, Airflow, Unity Catalog, MLflow). Provides deterministic start/stop/demo runbooks an AI agent can execute top-to-bottom.
Apache Airflow 3.1 orchestration for this stack. Load when writing DAGs, dealing with the 2→3 migration gotchas, configuring Spark/Kafka connections, or debugging Airflow inside the docker-compose-airflow.yml setup.