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diff --git a/src/dataframes.py b/src/dataframes.py
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+import pandas as pd
+from bs4 import BeautifulSoup
+from src.transform_lambda import read_from_s3_subfolder_to_df, tables
+from src.extract_lambda import extract_bucket
+import json
+import boto3
+import re
+from datetime import datetime as dt
+import requests
+
+# Table names:
+# fact_sales_order
+# fact_purchase_orders
+# fact_payment
+# dim_transaction
+# dim_staff
+# dim_payment_type
+# dim_location
+# dim_design
+# dim_date
+# dim_currency
+# dim_counterparty
+
+
+def create_dim_transaction(dict_of_df):
+    pass
+
+
+def create_fact_sales_order(dict_of_df):
+    df_sales = dict_of_df["sales_order"]
+    df_sales.index.name = "sales_record_id"
+    df_sales["created_date"] = pd.to_datetime(df_sales["created_at"]).dt.date
+    df_sales["created_time"] = pd.to_datetime(df_sales["created_at"]).dt.time
+    df_sales["last_updated_date"] = pd.to_datetime(df_sales["last_updated"]).dt.date
+    df_sales["last_updated_time"] = pd.to_datetime(df_sales["last_updated"]).dt.time
+    pd.merge(dict_of_df["staff"], df_sales["sales_staff_id"], on="staff_id", how="left")
+    # df_sales.rename(columns={"staff_id": "sales_staff_id"})
+    fact_sales_order = df_sales.loc[
+        :,
+        [
+            "sales_record_id",
+            "sales_order_id",
+            "created_date",
+            "created_time",
+            "last_updated_date",
+            "last_updated_time",
+            "sales_staff_id",
+            "counterparty_id",
+            "units_sold",
+            "unit_price",
+            "currency_id",
+            "design_id",
+            "agreed_payment_date",
+            "agreed_delivery_date",
+            "agreed_delivery_location_id",
+        ],
+    ]
+    return fact_sales_order
+
+
+# fact_purchase_order from purchase_order
+
+
+def create_fact_purchase_orders(dict_of_df):
+    df_po = dict_of_df["purchase_order"]
+    df_po.index.name = "purchase_record_id"
+    df_po["created_date"] = df_po["created_at"].date()
+    df_po["created_time"] = df_po["created_at"].dt.time
+    df_po["last_updated_date"] = df_po["last_updated_at"].date()
+    df_po["last_updated_time"] = df_po["last_updated_at"].dt.time
+    df_po["agreed_delivery_date"] = pd.to_datetime(
+        df_po["agreed_delivery_date"], format="%Y-%m-%d"
+    )
+    df_po["agreed_payment_date"] = pd.to_datetime(
+        df_po["agreed_payment_date"], format="%Y-%m-%d"
+    )
+    df_po.drop(labels=["created_at", "last_updated_at"], axis=1, inplace=True)
+    return df_po
+
+
+def create_fact_payment(dict_of_df):
+    df_payment = dict_of_df["payment"]
+    df_payment.index.name = "payment_record_id"
+    df_payment["created_date"] = pd.to_datetime(df_payment["created_at"]).dt.date
+    df_payment["created_time"] = pd.to_datetime(df_payment["created_at"]).dt.time
+    df_payment["last_updated_date"] = pd.to_datetime(df_payment["last_updated"]).dt.date
+    df_payment["last_updated_time"] = pd.to_datetime(df_payment["last_updated"]).dt.time
+    fact_payment = df_payment.loc[
+        :,
+        [
+            "payment_record_id",
+            "payment_id",
+            "created_date",
+            "created_time",
+            "last_updated_date",
+            "last_updated_time",
+            "transaction_id",
+            "counterparty_id",
+            "payment_amount",
+            "currency_id",
+            "payment_type_id",
+            "paid",
+            "payment_date",
+        ],
+    ]
+    return fact_payment
+
+
+# dim_location from address --> drops 2 columns
+
+
+def create_dim_location(dict_of_df):
+    df_loc = (
+        dict_of_df["address"]
+        .drop(labels=["created_at", "last_updated"], axis=1)
+        .rename(columns={"address_id": "location_id"})
+        .set_index("location_id")
+    )
+    return df_loc
+
+
+# dim_counterparty from address and counterparty
+
+
+def create_dim_counterparty(dict_of_df):
+    df_prefixed_address = dict_of_df["address"].add_prefix(
+        "counterparty_legal_", axis=1
+    )
+    df_cp = pd.merge(
+        dict_of_df["counterparty"],
+        df_prefixed_address,
+        left_on="legal_address_id",
+        right_on="address_id",
+        how="outer",
+    ).set_index("counterparty_id")
+    return df_cp
+
+
+# dim_date from purchase_order
+def create_dim_date(dict_of_df):
+    sr_date = pd.concat(
+        [
+            dict_of_df["created_date"],
+            dict_of_df["last_updated_date"],
+            dict_of_df["agreed_delivery_date"],
+            dict_of_df["agreed_payment_date"],
+        ]
+    ).sort()
+    df_date = pd.DataFrame(sr_date, columns="date_id")
+    df_date["year"] = df_date["date_id"].dt.year
+    df_date["month"] = df_date["date_id"].dt.month
+    df_date["day"] = df_date["date_id"].dt.day
+    df_date["day_of_week"] = df_date["date_id"].dt.dayofweek
+    df_date["day_name"] = df_date["date_id"].dt.day_name
+    df_date["month_name"] = df_date["date_id"].dt.month_name
+    df_date["quarter"] = df_date["date_id"].dt.quarter
+    df_date.set_index("date_id")
+
+
+def scrape_currency_names():
+    response = requests.get("https://www.xe.com/currency/").content
+    soup = BeautifulSoup(response, "html.parser")
+    currency = [
+        item.text for item in soup.findAll("a", attrs={"class": "sc-299dec64-6 fZPTSw"})
+    ]
+    sr = pd.Series(currency)
+    df_cur = sr.str.split(pat=" - ", expand=True).rename(
+        {0: "currency_code", 1: "currency_name"}, axis=1
+    )
+    return df_cur
+
+
+def create_dim_currency(dict_of_df, names=scrape_currency_names()):
+    df_cur = dict_of_df["currency"].drop(labels=["created_at", "last_updated"], axis=1)
+    dim_cur = pd.merge(
+        df_cur, names, left_on="currency_code", right_on="currency_code", how="inner"
+    ).set_index("currency_id")
+    return dim_cur
+
+
+def create_dim_payment_type(dict_of_df):
+    df_payment_type = dict_of_df["payment_type"]
+    dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]]
+    return dim_payment_type
+
+
+def create_fact_payment(dict_of_df):
+    df_payment = dict_of_df["payment"]
+    df_payment.index.name = "payment_record_id"
+    df_payment["created_date"] = pd.to_datetime(df_payment["created_at"]).dt.date
+    df_payment["created_time"] = pd.to_datetime(df_payment["created_at"]).dt.time
+    df_payment["last_updated_date"] = pd.to_datetime(df_payment["last_updated"]).dt.date
+    df_payment["last_updated_time"] = pd.to_datetime(df_payment["last_updated"]).dt.time
+    fact_payment = df_payment.loc[
+        :,
+        [
+            "payment_record_id",
+            "payment_id",
+            "created_date",
+            "created_time",
+            "last_updated_date",
+            "last_updated_time",
+            "transaction_id",
+            "counterparty_id",
+            "payment_amount",
+            "currency_id",
+            "payment_type_id",
+            "paid",
+            "payment_date",
+        ],
+    ]
+    return fact_payment
+
+
+def create_dim_design(dict_of_df):
+    df_design = dict_of_df["design"]
+    dim_design = df_design.loc[
+        :, ["design_id", "design_name", "file_name", "file_location"]
+    ]
+    return dim_design
+
+
+def create_dim_staff(dict_of_df):
+    staff_department = pd.merge(
+        dict_of_df["staff"], dict_of_df["department"], on="department_id", how="left"
+    )
+    dim_staff = staff_department.loc[
+        :,
+        [
+            "staff_id",
+            "first_name",
+            "last_name",
+            "department_name",
+            "location",
+            "email_address",
+        ],
+    ]
+    return dim_staff
+
+
+def create_dim_currency(dict_of_df):
+    df_currency = dict_of_df["currency"]
+    dim_currency = df_currency.loc[:, ["currency_id", "currency_code"]]
+    mappings = {"GBP": "Pound", "USD": "US Dollar", "EUR": "Euro"}
+    dim_currency["currency_name"] = dim_currency["currency_code"].map(mappings)
+    return dim_currency
+
+
+def create_dim_date(dict_of_df):
+    df_sales = dict_of_df["sales"]
+    df_sales = df_sales.loc[:, ["agreed_delivery_date"]]
+    df_sales["agreed_delivery_date"] = pd.to_datetime["agreed_delivery_date"]
+    df_sales["year"] = df_sales["agreed_delivery_date"].dt.year
+    df_sales["month"] = df_sales["agreed_delivery_date"].dt.month
+    df_sales["day"] = df_sales["agreed_delivery_date"].dt.day
+    df_sales["day_of_week"] = df_sales["agreed_delivery_date"].dt.dayofweek
+    df_sales["day_name"] = df_sales["agreed_delivery_date"].dt.day_name()
+    df_sales["month_name"] = df_sales["agreed_delivery_date"].dt.month_name()
+    df_sales["quarter"] = df_sales["agreed_delivery_date"].dt.quarter()
+    dim_date = [
+        "date_id",
+        "year",
+        "month",
+        "day",
+        "day_of_week",
+        "day_name",
+        "month_name",
+        "quarter",
+    ]  # series.dt.quarter()
+    return dim_date
+
+
+# TO DO:
+# complete dim_date from merged fact table
+# merge dataframes into one dataframe
+# remove duplicates
+# test dim_date and fact_sales_order
+
+
+def create_sales_star_schema(dict_of_df):
+    dim_design = create_dim_design(dict_of_df)
+    dim_staff = create_dim_staff(dict_of_df)
+    dim_currency = create_dim_currency(dict_of_df)
+    dim_date = create_dim_date(dict_of_df)
+
+    fact_sales_order = create_fact_sales_order(dict_of_df)
+
+    fact_sales_order = fact_sales_order.merge(dim_design, on="design_id", how="left")
+    fact_sales_order = fact_sales_order.merge(
+        dim_staff, left_on="sales_staff_id", right_on="staff_id", how="left"
+    )
+    fact_sales_order = fact_sales_order.merge(
+        dim_currency, on="currency_id", how="left"
+    )
+    fact_sales_order = fact_sales_order.merge(
+        dim_date, left_on="agreed_delivery_date", right_on="date_id", how="left"
+    )
+
+    return fact_sales_order
+
+
+def create_dim_payment_type(dict_of_df):
+    df_payment_type = dict_of_df["payment_type"]
+    dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]]
+    return dim_payment_type