How to Build an ETL App for Mocean Data in Python with CData
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python and the petl framework, you can build Mocean-connected applications and pipelines for extracting, transforming, and loading Mocean data. This article shows how to connect to Mocean with the CData Python Connector and use petl and pandas to extract, transform, and load Mocean data.
With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Mocean data in Python. When you issue complex SQL queries from Mocean, the driver pushes supported SQL operations, like filters and aggregations, directly to Mocean and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Mocean Data
Connecting to Mocean data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.
Using API Key Authentication
Mocean uses API key authentication to control access to the API. To obtain an API Key:
- Log in to your Mocean account at https://dashboard.moceanapi.com
- Navigate to your account settings or API credentials section
- Copy your API Key
After obtaining your API Key, set the following connection properties:
- AuthScheme: Set this to APIKey.
- APIKey: Set this to your Mocean API Key. This is transmitted as a Bearer token in the Authorization header.
Example Connection String
Profile=C:\profiles\Mocean.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';
Connecting to Mocean
Once the authentication is configured, you can connect to Mocean and query data from any of the available tables such as AccountBalance, AccountPricing, MessageStatus, and NumberLookup.
After installing the CData Mocean Connector, follow the procedure below to install the other required modules and start accessing Mocean through Python objects.
Install Required Modules
Use the pip utility to install the required modules and frameworks:
pip install petl pip install pandas
Build an ETL App for Mocean Data in Python
Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.
First, be sure to import the modules (including the CData Connector) with the following:
import petl as etl import pandas as pd import cdata.api as mod
You can now connect with a connection string. Use the connect function for the CData Mocean Connector to create a connection for working with Mocean data.
cnxn = mod.connect("Profile=C:\profiles\Mocean.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';")
Create a SQL Statement to Query Mocean
Use SQL to create a statement for querying Mocean. In this article, we read data from the AccountBalance entity.
sql = "SELECT , FROM AccountBalance WHERE = ''"
Extract, Transform, and Load the Mocean Data
With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Mocean data. In this example, we extract Mocean data, sort the data by the column, and load the data into a CSV file.
Loading Mocean Data into a CSV File
table1 = etl.fromdb(cnxn,sql) table2 = etl.sort(table1,'') etl.tocsv(table2,'accountbalance_data.csv')
With the CData API Driver for Python, you can work with Mocean data just like you would with any database, including direct access to data in ETL packages like petl.
Free Trial & More Information
Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Mocean data. Reach out to our Support Team if you have any questions.
Full Source Code
import petl as etl
import pandas as pd
import cdata.api as mod
cnxn = mod.connect("Profile=C:\profiles\Mocean.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';")
sql = "SELECT , FROM AccountBalance WHERE = ''"
table1 = etl.fromdb(cnxn,sql)
table2 = etl.sort(table1,'')
etl.tocsv(table2,'accountbalance_data.csv')