diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..ac2113b
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+config.py
+**.swp
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..f70d689
--- /dev/null
+++ b/README.md
@@ -0,0 +1,55 @@
+# 把 GA 的 Log Raw Data 抓出來再上傳到 BigQuery
+
+## 運行方式
+1. Copy sample_config.py to config.py
+2. 修改 config.py 中的 PROJECT_ID, DATA_SET, VIEW_ID, DATE_INIT
+3. 執行 DauGA.py (python2), 須提供credential的path, 此credential需要有google analytics跟google big query的權限
+
+
+## 重要資源
+Google提供的[Demo網站](https://ga-dev-tools.appspot.com/dimensions-metrics-explorer/)，這個真的很重要，但是有很難找，應該會有機會用到。
+
+
+## 功能簡介
+這個 Code 是根據 Blog 上面一篇[文章](http://daynebatten.com/2015/07/raw-data-google-analytics/)寫成的。 
+文章的簡述了如何把 GA 裡面的 Raw Data Dump 出來，然後存進 BigQuery 裡面(這個功能如果由Google官方的服務來進行，一個月至少要付500美金，所以讓我們懷著感恩的心地使用這項功能吧。)
+
+能夠做到這個功能的關鍵是，你跟 GA 藉由 [Report API](https://developers.google.com/admin-sdk/reports/v1/reference/) 所撈取出來的資料，每筆資料欄位都只對應到<b>一筆資料</b> -- 理論上，Report API希望你撈取的資料是經過統計計算的結果，舉例：每個縣市的不同類型使用者(新使用者、重複使用者)，在某方面(頁面停留時間or點擊次數or...)的統計結果，前者叫做 Dimension，後者叫做 Metrics，意思就是說，Report API report 給你不同 Dimension 下的 Metrics 統計結果。有關於不同的 Dimension 和 Metrics 介紹請看[這裡](https://developers.google.com/analytics/devguides/reporting/core/dimsmets)。
+
+但是 Blog 裡面提供了一個厲害而簡單作法，GA 本身就有提供把自訂的 Customized Dimension 傳給 GA。如果我們傳給 GA 的 Customized Dimension 是完全不會重複的(ex. unique_user__id and timestamp)，我們跟 Report API request 資料的時候，它就會都把每一筆分開來，然後我們就可以拿到 GA log 的 Raw Data 了！！！
+
+目前 Report API 提供 50000 次 request，每次上線 10000 筆，所以除非網站已經大到一天要 Log 5億筆資料，不然這個功能可以完全滿足我們的需求 --> 順利把 GA log 的 raw data request 出來然後上傳到 BigQuery 裡面，方便我們做各樣的分析。更棒的是，你可以順便把一些 GA 提供的很棒的使用者資料 (ex. 地區、使用系統、機型、瀏覽器)一併帶出來，供後續使用。
+
+
+## 事前預備和程式執行方式
+我們的目的是儘量自動化把資料重 GA dump 出來，然後自動化上傳到 BigQuery 形成我們需要的 Table，按時更新資料(把新的資料 append 到 BigQuery 上面)，讓我們以後只要負責開心在 BigQuery 裡面做資料分析就好了。
+
+
+- 在執行 dauGA.py 之前需要準備好的注意事項...
+- 先請上 [Google Cloud Platform](https://console.cloud.google.com/iam-admin/serviceaccounts/project) 申請你專屬的 Service Account，然後把檔案路徑 update 到 Code 的 CREDENTIAL_PATH 裡面。
+- 安裝特殊的 pandas，因為目前的 pandas 不支援把 table append 不是它自己創造的 BigQuery table ([相關issue](https://github.com/pydata/pandas/issues/13086))，但是我們需要這項功能，因此先 Fork 一個 Pandas 出來，目前 issue 上表示會在下一版 (0.18.2) 修正這個問題。
+
+```
+   pip install git+https://github.com/junyiacademy/pandas.git@tmp_solution_to_append_existed_bq_table
+```
+
+- 視需要修改config.py的參數
+- 裡面最重要的是 DATA_SET ，如果你是在 local 端執行，請務必改掉 DATA_SET 的參數，避免污染或破壞 BigQeury 上面我們真正在使用的 Table
+- 如果你要新增屬於你自己的 table，請把你要新增的 table 加在 ga\_bq\_config 這個變數裡面，它決定了你要從 GA report api dump 哪些東西下來，最終上傳哪些資料。關於 report\_request 的寫法，可以參考[這裡](https://developers.google.com/analytics/devguides/reporting/core/v4/rest/v4/reports/batchGet)。另外還要指定 "destination\_table" (你想把檔案存取到 BigQuery 的哪個 Table)
+- 機制概述：程式每次跑的時候，都會去到 {{DATA\_SET}}.upload\_progress\_log 的 Table 裡面，逐一去 check 從 2016-6-4(DATE_INIT) 到跑程式的前一天，每天每個要 update 的表格，是否有 "success" 的記錄，如果沒有，則把對應 table 裡面這一天的資料刪除(確定之後再次 load 資料的時候不會發生重複)，然後在把那一天的資料重新 dump 一次。以此來確定系統和資料的穩定性。
+- 不論每次程式 Dump 資料的成功與否，都會再 Update 到 {{DATA\_SET}}.upload\_progress\_log，供之後程式 Dump 資料時做參考，或供工程師 Review。
+
+## 測試方式
+#### 整個功能最主要做的事情有 1. 把資料從 GA 抓出來 2. 把資料做一些調整 3. 把資料上傳到 BigQuery 4. 確保 Robustness
+
+1. 可以在 function request\_df\_from\_ga 裡面去 check 你從 GA 抓下來的資料是否符合你在 ga\_bq\_config 裡面的設定
+2. 把資料做調整的過程寫在 function columns\_conversion 裡面，你可以讀取 function 回傳的 dataframe 確保要準備上傳的 table 符合你的需要。
+3. 最後在 table 成功上傳後，check BigQuery 上面相對應的 table 和你 local 端的 dataframe 是一致的。
+4. Robustness 是最需要小心測試的部份，因為 Code 是預期每天自動跑的，又牽扯到很多 外部的API，發生錯誤是預期當中的事情，我們不會希望每次出問題所有的資料都毀了需要重新來過，最理想的狀況是，哪一次程式 fail 了，之後重跑的時候可以直接修正掉之前的錯誤。程式實作的方法寫在前面的<b>機制概述</b>裡面。
+5. Robustness 測試的方法，是最重要的，目前的作法是在程式跑到不同階段 Raise Error，然後看 a. 會不會有殘缺的資料 upload 到 BigQuery?如果沒有，下次跑程式的時候這一天這個 table 的資料是不是還是會補上;如果有殘缺的資料上到 BigQuery，下次跑程式的時候是不是會先把殘缺的資料刪除掉。
+6. Check {{DATA\_SET}}.upload\_progress\_log 裡面每個 table 每天成功資料所記錄的 uploaded_data_size 和相對應的 table ga_session_date 的 count 是否一樣，來確認有沒有重複或缺少 load 資料的情況。 
+
+## Future Work
+
+1. 目前上傳的 BrowserUTC time 本身是含有 milli-second 的資料，但是因為 pd.to_gbq function 似乎只會上傳到 second 而已，如果真的需要更高的 resolution，之後可以有兩個作法 a. 修改 Pandas, b. 改成上傳 string，之後再用 query 的方式處理。
+2. ga_session_time & backup_date 其實都不是 UTC time，但是資料欄位裡面還是顯示是 utc ，但是目前看起來也不好調，很可能要動到 pandas，之後可以找機會來處理。 
diff --git a/dauGA.py b/dauGA.py
new file mode 100644
index 0000000..ce2613a
--- /dev/null
+++ b/dauGA.py
@@ -0,0 +1,231 @@
+import argparse
+import datetime
+'''
+pandas package now is installed from git+https://github.com/junyiacademy/pandas.git@tmp_solution_to_append_existed_bq_table
+It is a fork from pandas for us to be able to append to existed bq table.
+please use the following command to install pandas:
+   pip install git+https://github.com/junyiacademy/pandas.git@tmp_solution_to_append_existed_bq_table
+'''
+import pandas as pd
+# A self written library to handle google authorization
+from google_auth import google_auth
+# Import our config file
+import config
+
+def bq_query_to_table(query, table):  # query from bq then save into a bq table
+    dataset = table.split('.')[0]
+    table = table.split('.')[1]
+    job = bigquery.jobs().insert(projectId=config.PROJECT_ID,
+        body={"projectId": config.PROJECT_ID,
+              "configuration":{
+                "query": {
+                    "query": query,
+                        "destinationTable": {
+                            "projectId": config.PROJECT_ID,
+                            "datasetId": dataset,
+                            "tableId": table
+                        },
+                    "writeDisposition":"WRITE_TRUNCATE",
+                    "createDisposition":"CREATE_IF_NEEDED"
+                }
+              }
+        }).execute()
+    return job['id']
+
+
+def check_table_exist(table):
+    dataset = table.split('.')[0]
+    table = table.split('.')[1]
+    result = bigquery.tables().list(projectId=config.PROJECT_ID, datasetId=dataset).execute()
+    if not 'tables' in result:
+        return False
+    table_list = [i['tableReference']['tableId'] for i in result['tables']]
+    return table in table_list
+
+
+def check_ga_session_date_exist(destination_table, date, credential_path):  # check if destination table has data of certain date
+    if not check_table_exist(destination_table):  # has no certain date if the table not exist
+        return False
+    query = 'SELECT count(*) FROM [%s] WHERE DATE(ga_session_date) == "%s"' % (destination_table, date.strftime("%Y-%m-%d"))
+    return (pd.read_gbq(query, project_id=config.PROJECT_ID, verbose=False, private_key=credential_path).iloc[0, 0] > 0)
+
+
+def remove_certain_ga_session_date_data(destination_table, date):  # remove data of certain date
+    query = 'SELECT * FROM [%s] WHERE DATE(ga_session_date) != "%s"' % (destination_table, date.strftime("%Y-%m-%d"))
+    return bq_query_to_table(query, destination_table)
+
+
+def parse_result_to_df(result):  # convert ga request response to df
+    columns_list = []
+    columns_list.extend(result['reports'][0]['columnHeader']['dimensions'])
+    columns_list.extend([i['name'] for i in result['reports'][0]['columnHeader']['metricHeader']['metricHeaderEntries']])
+
+    row_num = len(result['reports'][0]['data']['rows'])
+    df = pd.DataFrame(columns = columns_list, index=range(row_num))
+    for i, row in enumerate(result['reports'][0]['data']['rows']):
+        list_to_append = []
+        list_to_append.extend(row['dimensions'])
+        list_to_append.extend(row['metrics'][0]['values'])
+        for j in range(len(list_to_append)):
+            df.iat[i, j] = list_to_append[j]  # df.append(my_dict, ignore_index=True)
+    return df
+
+
+def unix_time_millis(dt):
+    epoch = datetime.datetime.utcfromtimestamp(0)
+    return (dt - epoch).total_seconds() * 1000.0
+
+
+def convert_js_date_format(date_str):  # change the js datetime format to python datetime format
+    if date_str.isdigit():
+        return date_str
+    date_str = date_str.replace(' GMT', '').replace(' UTC', '')
+    if date_str.count(":") == 3:
+        try:
+            return unix_time_millis(datetime.datetime.strptime(date_str, "%a, %d %b %Y %H:%M:%S:%f"))
+        except:
+            # print "date_str: %s cannot be converted" % date_str
+            return date_str
+    elif date_str.count(":") == 2:
+        try:
+            return unix_time_millis(datetime.datetime.strptime(date_str, "%a, %d %b %Y %H:%M:%S"))
+        except:
+            # print "date_str: %s cannot be converted" % date_str
+            return date_str
+    else:
+        return date_str
+
+
+def columns_conversion(df, date):  # change the df we request from ga to the one we upload to bq
+    columns = [c.replace(':', '_') for c in df.columns]
+    for i, c in enumerate(columns):
+        if c == "ga_dimension8":
+            columns[i] = "user_key_name"
+        elif c == "ga_dimension9":
+            columns[i] = "browser_utc_time"
+            df.iloc[:, i] = df.iloc[:, i].apply(convert_js_date_format).astype(str)
+        elif c == "ga_dimension10":
+            columns[i] = "cookie_uuid"
+        elif c == "ga_timeOnPage" or c == "ga_pageviews" or c == "ga_hits":
+            df.iloc[:, i] = df.iloc[:, i].apply(lambda x: int(float(x)))
+        elif c == "ga_exits":
+            df.iloc[:, i] = df.iloc[:, i].astype(bool)
+    df.columns = columns
+    if 'ga_dateHour' in df.columns and 'ga_minute' in df.columns:
+        df.loc[:, 'ga_session_time'] = pd.to_datetime((df.loc[:, 'ga_dateHour'] + df.loc[:, 'ga_minute']), format="%Y%m%d%H%M")
+        df.drop(['ga_dateHour', 'ga_minute'], inplace=True, axis=1)
+    df['ga_session_date'] = pd.to_datetime(date)  # we always add ga session date to data
+    return df
+
+
+def request_df_from_ga(request, page_token=""):
+    request["reportRequests"][0]["pageToken"] = page_token
+    result = analytics.reports().batchGet(body=request).execute()
+    if 'rows' not in result['reports'][0]['data']:  # get no data from GA
+        print 'reqeust from Ga get no data. Row number is 0'
+        return (0, -1)
+    df = parse_result_to_df(result)
+    if 'nextPageToken' in result['reports'][0]:
+        return (df, result['reports'][0]['nextPageToken'])
+    else:
+        return (df, -1)
+
+
+def daterange(start_date, end_date):
+    for n in range(int((end_date - start_date).days)):
+        yield start_date + datetime.timedelta(n)
+
+
+def ga_upload_to_bq_by_day(ga_to_bg_config_name, date, credential_path):
+    if not isinstance(date, datetime.date):
+        print 'force the date parameter as datetiem.date format'
+        return None
+
+    request_body = config.ga_bq_config[ga_to_bg_config_name]["request_body"]
+    destination_table = config.ga_bq_config[ga_to_bg_config_name]["destination_table"]
+
+    if len(request_body["reportRequests"]) > 1:
+        print 'only allowed one reportRequests at this time'
+        return None
+
+    request_body["reportRequests"][0]['dateRanges'] = [{"startDate": date.strftime("%Y-%m-%d"), "endDate": date.strftime("%Y-%m-%d")}]
+
+    cont_page_token = ''
+    total_row = 0
+    finish_flag = False
+    retry_limit_flag = False
+    cont_page_token = ''
+    retry_count = 0
+    print 'Start loading data from GA and upload to %s, from %s' % (destination_table, date)
+    for i in range(1000):  # GA report API request limit: 1000 --> set limit to 50,000,000 row per day
+        try:
+            (df, cont_page_token) = request_df_from_ga(request_body, cont_page_token)
+            df = columns_conversion(df, date)
+            df.to_gbq(destination_table=destination_table, project_id=config.PROJECT_ID, if_exists='append', private_key=credential_path)
+            # df.to_csv("%s-%s-data" % (ga_to_bg_config_name, date))
+            row_num = len(df.index)
+            total_row = total_row + row_num
+            if cont_page_token == -1:
+                finish_flag = True
+
+        except Exception as e:
+            print "Failing download response from Ga or upload to %s" % destination_table
+            print str(e)
+            retry_count += 1
+            print "already tried %s times" % retry_count
+            if retry_count == 10:
+                retry_limit_flag = True
+
+        if finish_flag:
+            print 'Successfully download response from Ga and upload to %s' % destination_table
+            return {"status": "success", "data_size": total_row}
+        elif retry_limit_flag:
+            print "Reach retry limit, Script Closed"
+            return {"status": "failure", "data_size": total_row}
+    print "Download GA data exceed row limit!!! Need to increase the GA report API request limit"
+    return {"status": "failure", "data_size": total_row}
+
+
+if __name__ == "__main__":
+    # Parse the argument to get the credential_path
+    parser = argparse.ArgumentParser(description='Input secre_json_path and corresponding dataset')
+    parser.add_argument('--credential_path', type=str, dest='credential_path', required=True, help='input the path of service account credential from gcp, use $gcp_service_account in jenkings')
+    args = vars(parser.parse_args())
+    credential_path = args["credential_path"]
+    # Use google_auth library to get access to google
+    Auth = google_auth(credential_path)
+    bigquery = Auth.get_auth('bigquery_v2')
+    analytics = Auth.get_auth('analytics_v4')
+    # Check if the GA_BQ_UPLOAD_STATUS_LOG table exist in gbq
+    if check_table_exist(config.GA_BQ_UPLOAD_STATUS_LOG):
+        ga_bq_upload_status_log = pd.read_gbq(query="SELECT * FROM [%s]" % config.GA_BQ_UPLOAD_STATUS_LOG, project_id=config.PROJECT_ID, private_key=credential_path)
+    else:
+        ga_bq_upload_status_log = pd.DataFrame(columns=['config_name', 'ga_session_date', 'status', 'backup_date', "uploaded_data_size"])
+    # Set the time region
+    d = config.DATE_INIT.split("-")
+    date_init = datetime.date(int(d[0]),int(d[1]),int(d[2]))
+    date_now = datetime.datetime.now().date()
+
+    for config_name in config.ga_bq_config:
+        for date in daterange(date_init, date_now):
+            destination_table = config.ga_bq_config[config_name]["destination_table"]
+            print "start checking (%s, %s) pair for GA to BQ" % (config_name, date)
+            condition = (ga_bq_upload_status_log["config_name"]==config_name) & (ga_bq_upload_status_log["ga_session_date"]==date.strftime("%Y-%m-%d"))
+            if ga_bq_upload_status_log[condition].empty:  # no such condition, totally new table-date pair
+                print 'find no pair within the record, try to upload data with (%s, %s)' % (config_name, date)
+                if check_ga_session_date_exist(destination_table, date, credential_path):
+                    print 'find corresponding data in bq table, remove them.'
+                    remove_certain_ga_session_date_data(destination_table, date)
+                upload_result = ga_upload_to_bq_by_day(config_name, date, credential_path)
+                current_result = pd.DataFrame(data={"config_name": config_name, "ga_session_date": date.strftime("%Y-%m-%d"), "status": upload_result['status'], "backup_date": date_now.strftime("%Y-%m-%d"), "uploaded_data_size": upload_result['data_size']}, index=[0])
+                print "update corresponding result of (%s, %s) to %s" % (config_name, date, config.GA_BQ_UPLOAD_STATUS_LOG)
+                current_result.to_gbq(destination_table=config.GA_BQ_UPLOAD_STATUS_LOG, project_id=config.PROJECT_ID, if_exists='append', private_key=credential_path)
+            elif 'success' in ga_bq_upload_status_log[condition]['status'].values:
+                print "already success in such pair"
+            else:  # if failure, remove the data of that date/table and re-upload again
+                print 'find pair with failure status, remove existed data and re-uploard'
+                remove_certain_ga_session_date_data(destination_table, date)
+                upload_result = ga_upload_to_bq_by_day(config_name, date, credential_path)
+                current_result = pd.DataFrame(data={"config_name": config_name, "ga_session_date": date.strftime("%Y-%m-%d"), "status": upload_result['status'], "backup_date": date_now.strftime("%Y-%m-%d"), "uploaded_data_size": upload_result['data_size']}, index=[0])
+                print "update corresponding result of (%s, %s) to %s" % (config_name, date, config.GA_BQ_UPLOAD_STATUS_LOG)
+                current_result.to_gbq(destination_table=config.GA_BQ_UPLOAD_STATUS_LOG, project_id=config.PROJECT_ID, if_exists='append', private_key=credential_path)
diff --git a/google_auth.py b/google_auth.py
new file mode 100644
index 0000000..b77cb66
--- /dev/null
+++ b/google_auth.py
@@ -0,0 +1,36 @@
+from oauth2client.service_account import ServiceAccountCredentials
+from googleapiclient.discovery import build
+from httplib2 import Http
+
+
+class google_auth:
+    credential_path = "./credential.json"
+    types = {
+        'analytics_v4': {
+            'api_name': 'analyticsreporting',
+            'scope': 'https://www.googleapis.com/auth/analytics',
+            'version': 'v4'
+        },
+        'bigquery_v2': {
+            'api_name': 'bigquery',
+            'scope': 'https://www.googleapis.com/auth/bigquery',
+            'version': 'v2'
+        },
+        'storage_v1': {
+            'api_name': 'storage',
+            'scope': 'https://www.googleapis.com/auth/cloud-platform',
+            'version': 'v1'
+        }
+    }
+
+    def __init__(self, path):
+        self.credential_path = path
+
+    def get_auth(self, name):
+        if name in self.types:
+            credentials = ServiceAccountCredentials.from_json_keyfile_name(self.credential_path, self.types[name]['scope'])
+            http_auth = credentials.authorize(Http())
+            auth_object = build(self.types[name]['api_name'], self.types[name]['version'], http=http_auth)
+            return auth_object
+        else:
+            raise LookupError('There is no such type!')
diff --git a/sample_config.py b/sample_config.py
new file mode 100644
index 0000000..b42a6ff
--- /dev/null
+++ b/sample_config.py
@@ -0,0 +1,126 @@
+# Copy this file to config.py and change the settings
+
+# Project ID and dataset to use in Big query
+PROJECT_ID = "my_project"
+#DATA_SET = "GoogleAnalyticsData"
+DATA_SET = "my_ga_data"
+GA_BQ_UPLOAD_STATUS_LOG = (DATA_SET+".upload_progress_log")
+# View ID of GA. Check your GA view to find it.
+VIEW_ID = "12345678"
+# First day we upload user_key_email and time so we can request every single log from ga
+DATE_INIT = "2016-1-1"
+
+# Tables we want to store from GA
+ga_bq_config = {
+    "user_page_view": {
+        "request_body": {
+            "reportRequests":
+              [
+                {
+                  "viewId": VIEW_ID,
+                  "dateRanges": [{"startDate": "", "endDate": ""}],
+                  "dimensions": [{"name": "ga:dimension8"}, {"name": "ga:dimension9"}, {"name": "ga:pagePath"}, {"name": "ga:dateHour"}, {"name": "ga:minute"}],
+                  "dimensionFilterClauses": {},
+                  "metrics": [{"expression": "ga:timeOnPage"}, {"expression": "ga:exits"}],
+                  "orderBys": [{"fieldName": "ga:dateHour"}, {"fieldName": "ga:minute"}, {"fieldName": "ga:dimension8"}],
+                  "pageSize": "10000",
+                  "pageToken": ""
+                }
+              ]
+        },
+        "destination_table": DATA_SET+".ga_page_view"
+    },
+    "user_event":{
+        "request_body": {
+            "reportRequests":
+            [
+                {
+                  "viewId": VIEW_ID,
+                  "dateRanges": [{"startDate": "", "endDate": ""}],
+                  "dimensions": [{"name": "ga:dimension8"}, {"name": "ga:dimension9"}, {"name": "ga:dateHour"}, {"name": "ga:minute"}, {"name": "ga:eventCategory"}, {"name": "ga:eventAction"}, {"name": "ga:eventLabel"}],
+                  "dimensionFilterClauses": {},
+                  "metrics": [{"expression": "ga:hits"}],
+                  "orderBys": [{"fieldName": "ga:dateHour"}, {"fieldName": "ga:minute"}, {"fieldName": "ga:dimension8"}],
+                  "pageSize": "10000",
+                  "pageToken": ""
+                }
+            ]
+        },
+        "destination_table": DATA_SET+".ga_user_event"
+    },
+    "user_device": {
+        "request_body": {
+            "reportRequests":
+            [
+                {
+                  "viewId": VIEW_ID,
+                  "dateRanges": [{"startDate": "", "endDate": ""}],
+                  "dimensions": [{"name": "ga:dimension8"}, {"name": "ga:deviceCategory"}, {"name": "ga:operatingSystem"}, {"name": "ga:operatingSystemVersion"},
+                    {"name": "ga:browser"}, {"name": "ga:browserVersion"}],
+                  "dimensionFilterClauses": {},
+                  "metrics": [{"expression": "ga:timeOnPage"}, {"expression": "ga:pageviews"}],
+                  "orderBys": [{"fieldName": "ga:dimension8"}],
+                  "pageSize": "10000",
+                  "pageToken": ""
+                }
+            ]
+        },
+        "destination_table": DATA_SET+".ga_user_device"
+    },
+    "user_region": {
+        "request_body": {
+            "reportRequests":
+              [
+                {
+                  "viewId": VIEW_ID,
+                  "dateRanges": [{"startDate": "", "endDate": ""}],
+                  "dimensions": [{"name": "ga:dimension8"}, {"name": "ga:region"}, {"name": "ga:city"}],
+                  "dimensionFilterClauses": {},
+                  "metrics": [{"expression": "ga:timeOnPage"}, {"expression": "ga:pageviews"}],
+                  "orderBys": [{"fieldName": "ga:dimension8"}],
+                  "pageSize": "10000",
+                  "pageToken": ""
+                }
+              ]
+        },
+        "destination_table": DATA_SET+".ga_user_region"
+    },
+    "user_cookie_map": {
+        "request_body": {
+            "reportRequests":
+              [
+                {
+                  "viewId": VIEW_ID,
+                  "dateRanges": [{"startDate": "", "endDate": ""}],
+                  "dimensions": [{"name": "ga:dimension8"}, {"name": "ga:dimension10"}],
+                  "dimensionFilterClauses": {},
+                  "metrics": [{"expression": "ga:timeOnPage"}, {"expression": "ga:pageviews"}],
+                  "orderBys": [{"fieldName": "ga:dimension8"}],
+                  "pageSize": "10000",
+                  "pageToken": ""
+                }
+              ]
+        },
+        "destination_table": DATA_SET+".ga_user_cookie_map"
+    },
+    "user_mobile": {
+        "request_body": {
+            "reportRequests":
+            [
+                {
+                  "viewId": VIEW_ID,
+                  "dateRanges": [{"startDate": "", "endDate": ""}],
+                  "dimensions": [{"name": "ga:dimension8"}, {"name": "ga:mobileDeviceInfo"},
+                        {"name": "ga:operatingSystem"}, {"name": "ga:operatingSystemVersion"},
+                        {"name": "ga:browser"}, {"name": "ga:browserVersion"}],
+                  "dimensionFilterClauses": {},
+                  "metrics": [{"expression": "ga:timeOnPage"}, {"expression": "ga:pageviews"}],
+                  "orderBys": [{"fieldName": "ga:dimension8"}],
+                  "pageSize": "10000",
+                  "pageToken": ""
+                }
+            ]
+        },
+        "destination_table": DATA_SET+".ga_user_mobile"
+    },
+}