diff --git a/prototypes/02-2_aggregates.py b/prototypes/02-2_aggregates.py
index d419ef5..dccc4d3 100644
--- a/prototypes/02-2_aggregates.py
+++ b/prototypes/02-2_aggregates.py
@@ -9,6 +9,7 @@ import sqlalchemy as sql
 
 from wattanalyse import db
 
+# %%
 importlib.reload(db)
 importlib.reload(external_code)
 # %%
@@ -58,6 +59,7 @@ external_code.dump_order_level_to_internal_database_wipe(df)
 df = external_code.load_order_level_from_internal_database()
 df
 # %%
+# ** aggregate production orders
 tmp = df.clone()
 
 # two ways to define the aggregate for date deviations: just use < 0 or use Boolean flag
@@ -73,42 +75,203 @@ else:
     filter_date_deviation_late = pl.col("Terminabweichung_Anzahl_Tage") > 0
 
 
-tmp.select(
+tmp = tmp.select(
     pl.col("Terminabweichung_Anzahl_Tage")
     .filter(filter_date_deviation_early)
     .mean()
     .abs()
     .round(mode="half_away_from_zero")
     .cast(pl.Int64)
-    .alias("Mittlere_Tage_Unterschreitung"),
+    .alias("MITTLERE_ANZAHL_TAGE_LIEFERTERMINUNTERSCHREITUNG"),
     pl.col("Terminabweichung_Anzahl_Tage")
     .filter(filter_date_deviation_late)
     .mean()
     .abs()
     .round(mode="half_away_from_zero")
     .cast(pl.Int64)
-    .alias("Mittlere_Tage_Ueberschreitung"),
+    .alias("MITTLERE_ANZAHL_TAGE_LIEFERTERMINUEBERSCHREITUNG"),
     pl.col("Terminabweichung_Anzahl_Tage")
     .std(ddof=1)
-    .alias("Standardabweichung_Lieferterminabweichung"),
+    .alias("STANDARDABWEICHUNG_TAGE_LIEFERTERMINABWEICHUNG"),
     pl.col("Import-Ist_Anzahl_Aenderungen")
     .mean()
     .abs()
     .round(mode="half_away_from_zero")
     .cast(pl.Int64)
-    .alias("Mittlere_Anzahl_Anpassungen_Liefertermin"),
+    .alias("MITTLERE_ANZAHL_ANPASSUNGEN_LIEFERTERMIN"),
     pl.col("Tage_zu_letzter_PSM_Historie")
     .list.explode()
     .mean()
     .abs()
     .round(mode="half_away_from_zero")
     .cast(pl.Int64)
-    .alias("Mittlere_Abstaende_PSM"),
+    .alias("MITTLERE_ABSTAENDE_ZWISCHEN_MELDUNGEN"),
     pl.col("Durchlaufzeit_Anzahl_Tage")
     .mean()
     .round(mode="half_away_from_zero")
     .cast(pl.Int64)
-    .alias("Mittlere_Durchlaufzeit"),
+    .alias("MITTLERE_DURCHLAUFZEIT_ANZAHL_TAGE"),
+)
+tmp
+# %%
+# to DB transform (mock Oracle database)
+cols_sorted = ["ID", "AKTUALISIERT_AM"] + [c for c in tmp.columns]
+tmp = (
+    tmp.with_columns(
+        pl.lit(1).alias("ID"),
+        pl.lit(datetime.datetime.now()).alias("AKTUALISIERT_AM"),
+    )
+    .select(
+        pl.col(pl.Boolean).cast(pl.Int8),
+        pl.all().exclude(pl.Boolean),
+    )
+    .select(cols_sorted)
+)
+tmp
+
+# %%
+
+# return sql_delete, sql_insert
+
+print(f"SQL DELETE: {sql_delete}\nSQL Insert: {sql_insert}")
+# %%
+prepared_oracle_pth = DATA_PTH / "db/oracle_prepare_KPI_PRODUKTIONSAUFTRAEGE.arrow"
+tmp.write_ipc(prepared_oracle_pth)
+# %%
+# ** aggregate supplier
+tmp = df.clone()
+
+USE_BOUNDARIES = False
+filter_date_deviation_early: pl.Expr
+filter_date_deviation_late: pl.Expr
+if USE_BOUNDARIES:
+    filter_date_deviation_early = pl.col("Terminunterschreitung")
+    filter_date_deviation_late = pl.col("Terminüberschreitung")
+else:
+    filter_date_deviation_early = pl.col("Terminabweichung_Anzahl_Tage") < 0
+    filter_date_deviation_late = pl.col("Terminabweichung_Anzahl_Tage") > 0
+
+tmp = (
+    tmp.group_by("Konfektionär")
+    .agg(
+        (
+            (
+                ~(filter_date_deviation_early | filter_date_deviation_late)
+                & (pl.col("Import-Ist_Anzahl_Aenderungen") == 0)
+            ).mean()
+            * 100
+        )
+        .round(4, mode="half_away_from_zero")
+        .alias("QUOTE_ERSTBESTAETIGUNG"),
+        ((~(filter_date_deviation_early | filter_date_deviation_late)).mean() * 100)
+        .round(4, mode="half_away_from_zero")
+        .alias("PROZENT_LIEFERTREUE"),
+        (filter_date_deviation_early.mean() * 100)
+        .round(4, mode="half_away_from_zero")
+        .alias("ANTEIL_PROZENT_LIEFERTERMINUNTERSCHREITUNG"),
+        (filter_date_deviation_late.mean() * 100)
+        .round(4, mode="half_away_from_zero")
+        .alias("ANTEIL_PROZENT_LIEFERTERMINUEBERSCHREITUNG"),
+        pl.col("Terminabweichung_Anzahl_Tage")
+        .filter(filter_date_deviation_early)
+        .mean()
+        .abs()
+        .round(mode="half_away_from_zero")
+        .cast(pl.Int64)
+        .alias("MITTLERE_ANZAHL_TAGE_LIEFERTERMINUNTERSCHREITUNG"),
+        pl.col("Terminabweichung_Anzahl_Tage")
+        .filter(filter_date_deviation_late)
+        .mean()
+        .abs()
+        .round(mode="half_away_from_zero")
+        .cast(pl.Int64)
+        .alias("MITTLERE_ANZAHL_TAGE_LIEFERTERMINUEBERSCHREITUNG"),
+        pl.col("Terminabweichung_Anzahl_Tage")
+        .std(ddof=1)
+        .alias("STANDARDABWEICHUNG_TAGE_LIEFERTERMINABWEICHUNG"),
+        pl.col("Import-Ist_Anzahl_Aenderungen")
+        .mean()
+        .abs()
+        .round(mode="half_away_from_zero")
+        .cast(pl.Int64)
+        .alias("MITTLERE_ANZAHL_ANPASSUNGEN_LIEFERTERMIN"),
+        pl.col("Tage_zu_letzter_PSM_Historie")
+        .list.explode()
+        .mean()
+        .abs()
+        .round(mode="half_away_from_zero")
+        .cast(pl.Int64)
+        .alias("MITTLERE_ABSTAENDE_ZWISCHEN_MELDUNGEN"),
+        pl.col("Durchlaufzeit_Anzahl_Tage")
+        .mean()
+        .round(mode="half_away_from_zero")
+        .cast(pl.Int64)
+        .alias("MITTLERE_DURCHLAUFZEIT_ANZAHL_TAGE"),
+        pl.col("Prod-Qualitaet_Historie")
+        .list.explode()
+        .mean()
+        .round(4, mode="half_away_from_zero")
+        .alias("MITTLERER_QUALITAETSSCORE_PSM"),
+    )
+    .sort("Konfektionär")
+)
+tmp
+
+# %%
+tmp = df.clone()
+tmp.filter(pl.col.Konfektionär == "BS Make Ltd").filter(
+    ~(filter_date_deviation_early | filter_date_deviation_late)
+).filter(pl.col("Import-Ist_Anzahl_Aenderungen") == 0)
+# %%
+tmp.filter(pl.col.Konfektionär == "BS Make Ltd")
+# %%
+tmp.head()
+# %%
+tmp.filter(pl.col.Konfektionär == "Siluet")
+
+# %%
+tmp.select(pl.col.Konfektionär.str.len_chars().alias("len_char")).sort(
+    "len_char", descending=True
 )
 
 # %%
+# // whole pipeline
+# ** aggregate production orders
+tmp = df.clone()
+
+tmp = external_code.aggregate_production_orders(tmp.lazy()).collect()
+print(tmp)
+tmp = external_code.oracle_prepare_KPI_aggregate(tmp.lazy()).collect()
+print(tmp)
+prepared_oracle_pth = DATA_PTH / "db/oracle_prepare_KPI_PRODUKTIONSAUFTRAEGE.arrow"
+tmp.write_ipc(prepared_oracle_pth)
+
+# %%
+stmts = external_code.oracle_generate_sql_insert(
+    table_name="KPI_PRODUKTIONSAUFTRAEGE", columns=tmp.columns
+)
+print(f"SQL DELETE: {stmts.delete}\nSQL Insert: {stmts.insert}")
+
+# %%
+# ** aggregate supplier
+tmp = df.clone()
+RENAME_SCHEME = {"Konfektionär": "KONFEKTIONAER"}
+tmp = external_code.aggregate_suppliers(tmp.lazy()).collect()
+print(tmp.head())
+tmp = external_code.oracle_prepare_KPI_aggregate(
+    tmp.lazy(),
+    rename_schema=RENAME_SCHEME,
+    sort_by="KONFEKTIONAER",
+    sort_descending=False,
+).collect()
+print(tmp.head())
+prepared_oracle_pth = DATA_PTH / "db/oracle_prepare_KPI_KONFEKTIONAERE.arrow"
+tmp.write_ipc(prepared_oracle_pth)
+# %%
+stmts = external_code.oracle_generate_sql_insert(
+    table_name="KPI_KONFEKTIONAERE", columns=tmp.columns
+)
+print(f"SQL DELETE: {stmts.delete}\nSQL Insert: {stmts.insert}")
+# %%
+tmp
+# %%