How to Build an ETL App for Amazon DynamoDB 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 Python Connector for Amazon DynamoDB and the petl framework, you can build Amazon DynamoDB-connected applications and pipelines for extracting, transforming, and loading Amazon DynamoDB data. This article shows how to connect to Amazon DynamoDB with the CData Python Connector and use petl and pandas to extract, transform, and load Amazon DynamoDB data.

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

Connecting to Amazon DynamoDB Data

Connecting to Amazon DynamoDB 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.

The connection to Amazon DynamoDB is made using your AccessKey, SecretKey, and optionally your Domain and Region. Your AccessKey and SecretKey can be obtained on the security credentials page for your Amazon Web Services account. Your Region will be displayed in the upper left-hand corner when you are logged into DynamoDB.

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

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

cnxn = mod.connect("Access Key=xxx;Secret Key=xxx;Domain=amazonaws.com;Region=OREGON;")

Create a SQL Statement to Query Amazon DynamoDB

Use SQL to create a statement for querying Amazon DynamoDB. In this article, we read data from the Lead entity.

sql = "SELECT Industry, Revenue FROM Lead WHERE FirstName = 'Bob'"

Extract, Transform, and Load the Amazon DynamoDB Data

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

Loading Amazon DynamoDB Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Lead table.

Adding New Rows to Amazon DynamoDB

table1 = [ ['Industry','Revenue'], ['NewIndustry1','NewRevenue1'], ['NewIndustry2','NewRevenue2'], ['NewIndustry3','NewRevenue3'] ]

etl.appenddb(table1, cnxn, 'Lead')

With the CData Python Connector for Amazon DynamoDB, you can work with Amazon DynamoDB 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 Python Connector for Amazon DynamoDB to start building Python apps and scripts with connectivity to Amazon DynamoDB data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.amazondynamodb as mod

cnxn = mod.connect("Access Key=xxx;Secret Key=xxx;Domain=amazonaws.com;Region=OREGON;")

sql = "SELECT Industry, Revenue FROM Lead WHERE FirstName = 'Bob'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Industry','Revenue'], ['NewIndustry1','NewRevenue1'], ['NewIndustry2','NewRevenue2'], ['NewIndustry3','NewRevenue3'] ]

etl.appenddb(table3, cnxn, 'Lead')