Type-safe, code-first tooling for building real-time analytical backends--OLAP Databases, Data Streaming, ETL Workflows, Query APIs, and more.
bash -i <(curl -fsSL https://fiveonefour.com/install.sh) mooseDeclare all infrastructure (e.g. ClickHouse tables, Redpanda streams, APIs, etc.) and pipelines in pure TypeScript or Python. Your code auto-wires everything together, so no integration boilerplate needed.
from moose_lib import Key, OlapTable, Stream, StreamConfig, IngestApi, IngestApiConfig, Apifrom pydantic import BaseModel class DataModel(BaseModel): primary_key: Key[str] name: str # Create a ClickHouse tableclickhouse_table = OlapTable[DataModel]("TableName") # Create a Redpanda streaming topicredpanda_topic = Stream[DataModel]("TopicName", StreamConfig( destination=clickhouse_table,)) # Create an ingest API endpointingest_api = IngestApi[DataModel]("post-api-route", IngestConfig( destination=redpanda_topic,)) # Create a analytics API endpointclass QueryParams(BaseModel): limit: int = 10 def handler(client, params: QueryParams): return client.query.execute("SELECT * FROM {table: Identifier} LIMIT {limit: Int32}", { "table": clickhouse_table.name, "limit": params.limit, }) analytics_api = Api[RequestParams, DataModel]("get-api-route", query_function=handler)Each module is independent and can be used on its own. You can start with one capability and incrementally adopt more over time.