Merge pull request #631 from plotly/add-synapse-demo

Add Synapse Analytics Integration Demo (#minor)

Former-commit-id: b1670a5

apps/dash-synapse-demo/.gitignore

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+*__pycache__*
+*.vscode*

apps/dash-synapse-demo/Procfile

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+web: gunicorn app:server --workers 4 --preload

apps/dash-synapse-demo/README.md

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+# Dash Interest Rate Modeling (with Snowflake)
+
+This app shows how to query loan data from a Snowflake Data Warehouse, and train and analyze a Ridge regression model using Dash.
+
+![demo](assets/demo.gif)
+
+## Setting up Synapse
+
+This app uses an Azure Synapse Analytics database for querying the data used to train the model.
+
+### Datasets
+
+The data used was retrieved and modified from the [Lending Club Loan Data](https://www.kaggle.com/wendykan/lending-club-loan-data) from Kaggle. By using this dataset, you are agreeing to Kaggle's terms of use, as well as the original data license. You can download the modified files here:
+* `clean_loan.csv`: [Link](https://plotly-tutorials.s3-us-west-1.amazonaws.com/dash-sample-apps/snowflake-demos/clean_loan.csv)
+* `loan_desc.csv`: [Link](https://plotly-tutorials.s3-us-west-1.amazonaws.com/dash-sample-apps/snowflake-demos/loan_desc.csv)
+
+### Installion ODBC driver
+
+[This tutorial](https://docs.microsoft.com/en-us/sql/connect/odbc/linux-mac/installing-the-microsoft-odbc-driver-for-sql-server?view=sql-server-ver15) shows how to install ODBC driver for SQL Server on Linux. You will need to install ODBC 17 to use this app - if you need another version, you will have to update the selected drivers by modifying the source code.
+
+### SQL Setup from CSV file
+
+Use the Azure Storage Explorer application to uploading `clean_loan.csv` to Azure Blob Storage. See [tutorial](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-storage-explorer).
+
+
+The following steps require Synapse Studio.
+
+<!-- Connect to your Azure Blob Storage by selecting Data > "+" > Connect to external data > "Azure Blog Storage".
+
+![](images/external_data_1.jpg)
+![](images/external_data_2.jpg) -->
+
+
+Create a database called loan:
+
+![](images/create_sql_db.jpg)
+
+Inside the SQL Editor, select "loan" for "Use database".
+
+Create a new data source linking to the Azure Blob Storage containing the CSV file you previously uploaded. Execute the following (change the location if needed):
+```sql
+USE [loan];
+GO
+
+CREATE EXTERNAL DATA SOURCE sampleapps
+WITH ( LOCATION = 'https://sampleappsdata.blob.core.windows.net/');
+```
+
+Create a new format called "QuotedCsvWithHeaderFormat". Execute the following:
+
+```sql
+USE [loan];
+GO
+
+CREATE EXTERNAL FILE FORMAT CsvWithHeaderFormat
+WITH (  
+    FORMAT_TYPE = DELIMITEDTEXT,
+    FORMAT_OPTIONS ( FIELD_TERMINATOR = ',', FIRST_ROW = 2   )
+);
+GO
+```
+
+Finally, you can create your table:
+```sql
+USE [loan];
+GO
+
+CREATE EXTERNAL TABLE cleanLoan
+(
+    loan_amnt INT,
+    funded_amnt INT,
+    revol_bal INT,
+    policy_code INT,
+    funded_amnt_inv FLOAT,
+    int_rate FLOAT,
+    installment FLOAT,
+    annual_inc FLOAT,
+    dti FLOAT,
+    delinq_2yrs FLOAT,
+    inq_last_6mths FLOAT,
+    open_acc FLOAT,
+    pub_rec FLOAT,
+    revol_util FLOAT,
+    total_acc FLOAT,
+    out_prncp FLOAT,
+    out_prncp_inv FLOAT,
+    total_pymnt FLOAT,
+    total_pymnt_inv FLOAT,
+    term VARCHAR (100) COLLATE Latin1_General_BIN2,
+    grade VARCHAR (100) COLLATE Latin1_General_BIN2,
+    emp_length VARCHAR (100) COLLATE Latin1_General_BIN2,
+    home_ownership VARCHAR (100) COLLATE Latin1_General_BIN2,
+    verification_status VARCHAR (100) COLLATE Latin1_General_BIN2,
+    loan_status VARCHAR (100) COLLATE Latin1_General_BIN2,
+    pymnt_plan VARCHAR (100) COLLATE Latin1_General_BIN2,
+    purpose VARCHAR (100) COLLATE Latin1_General_BIN2,
+    initial_list_status VARCHAR (100) COLLATE Latin1_General_BIN2,
+    application_type VARCHAR (100) COLLATE Latin1_General_BIN2
+)
+WITH (
+    LOCATION = 'dash-sample-apps-data/clean_loan.csv',
+    DATA_SOURCE = sampleapps,
+    FILE_FORMAT = CsvWithHeaderFormat
+);
+```
+
+The columns can be generated with the following code (run after you downloaded `clean_loan.csv`):
+```python
+import pandas as pd
+import utils
+
+df = pd.read_csv("clean_loan.csv", nrows=5)
+print(utils.get_column_strings(df))
+```
+
+
+### Using a different user
+
+If you do not want to use your admin account, you can create one for the `loan` database limited to `SELECT`ing:
+```sql
+CREATE LOGIN developers WITH PASSWORD = '<your-password>';
+
+USE [loan]
+GO;
+
+CREATE USER developers FROM LOGIN developers;
+GRANT SELECT TO developers;
+```
+
+Set the username to `developers` and the password to your new password.
+
+Read more about [GRANT](https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/sql-authentication?tabs=serverless) and [SQL authentication](https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/sql-authentication?tabs=serverless).
+
+
+
+## Setting up the app
+
+First clone this repo:
+```
+git clone https://github.com/plotly/dash-sample-apps.git
+cd dash-sample-apps/apps/dash-interest-rate
+```
+
+Create a conda env (or venv) and install the requirements:
+```
+conda create -n dash-interest-rate python=3.6.7
+conda activate dash-interest-rate
+pip install -r requirements.txt
+```
+
+
+## Start the app
+
+Start a redis server:
+```
+redis-server --port 7777
+```
+
+In a separate terminal window, you can now run the app:
+```
+python app.py
+```
+
+and visit http://127.0.0.1:8050/.

apps/dash-synapse-demo/app.json

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+{
+    "scripts": {
+        "dokku": {
+            "predeploy": "bash predeploy.sh"
+        }
+    }
+}