adding iceberg notebook
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Writing to Apache Iceberg\n",
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"\n",
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"1. Insure you have a Google Bigquery service account key on disk\n",
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"2. The service key location is set as an environment variable **BQ_KEY**\n",
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"3. The dataset will be automatically created within the project associated with the service key\n",
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"\n",
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"The cell below creates a dataframe that will be stored within Google Bigquery"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['data transport version ', '2.4.0']\n"
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]
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}
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],
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"source": [
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"#\n",
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"# Writing to Google Bigquery database\n",
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"#\n",
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"import transport\n",
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"from transport import providers\n",
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"import pandas as pd\n",
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"import os\n",
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"\n",
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"PRIVATE_KEY = os.environ['BQ_KEY'] #-- location of the service key\n",
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"DATASET = 'demo'\n",
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"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
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"# bqw = transport.get.writer(provider=providers.ICEBERG,catalog='mz',database='edw.mz',table='friends')\n",
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"bqw = transport.get.writer(provider=providers.ICEBERG,table='edw.mz.friends')\n",
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"bqw.write(_data,if_exists='replace') #-- default is append\n",
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"print (['data transport version ', transport.__version__])\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Reading from Google Bigquery\n",
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"\n",
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"The cell below reads the data that has been written by the cell above and computes the average age within a Google Bigquery (simple query). \n",
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"\n",
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"- Basic read of the designated table (friends) created above\n",
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"- Execute an aggregate SQL against the table\n",
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"\n",
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"**NOTE**\n",
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"\n",
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"By design **read** object are separated from **write** objects in order to avoid accidental writes to the database.\n",
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"Read objects are created with **transport.get.reader** whereas write objects are created with **transport.get.writer**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" name age\n",
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"0 James Bond 55\n",
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"1 Steve Rogers 150\n",
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"2 Steve Nyemba 44\n",
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"--------- STATISTICS ------------\n"
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]
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}
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],
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"source": [
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"\n",
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"import transport\n",
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"from transport import providers\n",
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"import os\n",
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"PRIVATE_KEY=os.environ['BQ_KEY']\n",
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"pgr = transport.get.reader(provider=providers.ICEBERG,database='edw.mz')\n",
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"_df = pgr.read(table='friends')\n",
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"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
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"_sdf = pgr.read(sql=_query)\n",
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"print (_df)\n",
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"print ('--------- STATISTICS ------------')\n",
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"# print (_sdf)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"An **auth-file** is a file that contains database parameters used to access the database. \n",
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"For code in shared environments, we recommend \n",
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"\n",
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"1. Having the **auth-file** stored on disk \n",
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"2. and the location of the file is set to an environment variable.\n",
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"\n",
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"To generate a template of the **auth-file** open the **file generator wizard** found at visit https://healthcareio.the-phi.com/data-transport"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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