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-# ETL-TPCH
+Example Production ETL System - TPC-H
+=====================================
+
+This is an example system that runs regular jobs on a schedule, at scale, on
+the cloud using a variety of technologies:
+
+-  **Prefect:** for job scheduling and workflow management
+-  **Coiled::** for cloud hardware provisioning
+-  **Dask:** for parallel processing
+-  **Parquet** and **Deltalake:** for data storage
+-  **XGBoost:** for model training
+-  **Streamlit** and **Fast API:** for dashboarding and model hosting
+
+Raw data flows in every minute and is transformed through several stages at
+different scales and cadences.
+
+-  **Data Generation:** (*every 30 seconds*) new JSON files appear
+-  **Data Preprocessing:** (every minute) JSON gets transformed into Parquet / Delta
+-  **Data Compaction:** (every 30 minutes) small Parquet files get repartitioned into larger ones
+-  **Business Queries:** (every hour) large scale multi-table analysisqueries run that feed dashboards
+-  **Model Training:** (every six hours) with XGBoost
+
+Additionally we keep Streamlit and FastAPI servers up and running.
+
+It looks kinda like this:
+
+![ETL Pipeline](images/excalidraw.png)
+
+How this works
+--------------
+
+### Concurrent Flows
+
+The file [workflow.py](workflow.py) defines several Prefect flows operating at
+different cadences:
+
+```python
+# workflow.py
+...
+
+generate = generate_json.to_deployment(
+    name="generate_data",
+    interval=timedelta(seconds=30),
+)
+preprocess = json_to_parquet.to_deployment(
+    name="preprocess",
+    interval=timedelta(minutes=1),
+)
+train = update_model.to_deployment(
+    name="train",
+    interval=timedelta(hours=6),
+)
+
+prefect.serve(
+    generate,
+    preprocess,
+    train,
+    ...
+)
+```
+
+### Coiled Functions + Prefect Tasks
+
+These flows are defined in files like [pipeline/preprocess.py](pipeline/preprocess.py), which combine prefect tasks either with Coiled functions for remote execution like the following:
+
+```python
+# pipeline/preprocess.py
+
+import coiled
+from prefect import task, flow
+import pandas as pd
+
+@task
+@coiled.function(region="us-east-2", memory="64 GiB")
+def json_to_parquet(filename):
+    df = pd.read_json(filename)
+    df.to_parquet(OUTFILE / filename.split(".")[-2] + ".parquet")
+
+@flow
+def convert_all_files():
+    files = list_files()
+    json_to_parquet.map(files)
+```
+
+### Dask Clusters for larger jobs
+
+Or in files like [pipeline/reduce.py](pipeline/reduce.py) which create larger
+clusters on-demand.
+
+```python
+# pipeline/reduce.py
+
+import coiled
+from prefect import task, flow
+import dask.dataframe as dd
+
+@task
+def query_for_bad_accounts(bucket):
+    with coiled.Cluster(n_workers=20, region="us-east-2") as cluster:
+        with cluster.get_client() as client:
+            df = dd.read_parquet(bucket)
+
+            ... # complex query
+
+            result.to_parquet(...)
+```
+
+Data Generation
+---------------
+
+In the background we're generating the data ourselves.  This data is taken from
+the TPC-H benchmark suite.  It's a bunch of tables that simulate orders /
+customers / suppliers, and has a schema that looks like this:
+
+![TPC-H Schema](https://docs.snowflake.com/en/_images/sample-data-tpch-schema.png)
+
+This gives the system a sense of realism, while still being able to tune up or
+down in scale and run easily as an example.
+
+How to Run Locally
+------------------
 
 Make sure you have a [Prefect cloud](https://www.prefect.io/cloud) account and have authenticated your local machine.
 
@@ -21,3 +141,8 @@ streamlit run dashboard.py  # Serve dashboard
 ```
 
 Watch things run, both in your terminals and on Prefect cloud, and then ctrl-C to shut everything down.
+
+How to Run in the Cloud
+-----------------------
+
+This all works, we just haven't documented it well yet.
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