Extract, Transform, and Load Wasabi Data in Python

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Wasabi Python Connector

Python Connector Libraries for Wasabi Data Connectivity. Integrate Wasabi with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Wasabi enables you to create ETL applications and pipelines for Wasabi data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Wasabi and the petl framework, you can build Wasabi-connected applications and pipelines for extracting, transforming, and loading Wasabi data. This article shows how to connect to Wasabi with the CData Python Connector and use petl and pandas to extract, transform, and load Wasabi data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Wasabi data in Python. When you issue complex SQL queries from Wasabi, the driver pushes supported SQL operations, like filters and aggregations, directly to Wasabi and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Wasabi Data

Connecting to Wasabi 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.

To authorize Wasabi requests, provide the credentials for an administrator account or for an IAM user with custom permissions. Set AccessKey to the access key Id. Set SecretKey to the secret access key.

Note: You can connect as the AWS account administrator, but it is recommended to use IAM user credentials to access AWS services.

For information on obtaining the credentials and other authentication methods, refer to the Getting Started section of the Help documentation.

After installing the CData Wasabi Connector, follow the procedure below to install the other required modules and start accessing Wasabi 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 Wasabi 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.wasabi as mod

You can now connect with a connection string. Use the connect function for the CData Wasabi Connector to create a connection for working with Wasabi data.

cnxn = mod.connect("AccessKey=a123;SecretKey=s123;")

Create a SQL Statement to Query Wasabi

Use SQL to create a statement for querying Wasabi. In this article, we read data from the Buckets entity.

sql = "SELECT Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'"

Extract, Transform, and Load the Wasabi Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Wasabi data. In this example, we extract Wasabi data, sort the data by the OwnerId column, and load the data into a CSV file.

Loading Wasabi Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'OwnerId')

etl.tocsv(table2,'buckets_data.csv')

With the CData Python Connector for Wasabi, you can work with Wasabi 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 Wasabi Python Connector to start building Python apps and scripts with connectivity to Wasabi 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.wasabi as mod

cnxn = mod.connect("AccessKey=a123;SecretKey=s123;")

sql = "SELECT Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'OwnerId')

etl.tocsv(table2,'buckets_data.csv')