How to Build an ETL App for Neo4J Data in Python with CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK and the petl framework, you can build Neo4J-connected applications and pipelines for extracting, transforming, and loading Neo4J data. This article shows how to connect to Connect AI and use petl to extract, transform, and load Neo4J data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to Neo4J in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Neo4J" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Neo4J.

   To connect to Neo4j, set the following connection properties:

   • Server: The server hosting the Neo4j instance.
   • Port: The port on which the Neo4j service is running. The provider connects to port 7474 by default.
   • User: The username of the user using the Neo4j instance.
   • Password: The password of the user using the Neo4j instance.
   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for Neo4J Data in Python

Once the required modules are installed, you are ready to build the ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Create a SQL Statement to Query Neo4J

Use SQL to create a statement for querying Neo4J. In this article, we read data from the ProductCategory entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Neo4j1).

sql = (
    "SELECT CategoryId, CategoryName "
    "FROM [Neo4j1].[Neo4j].[ProductCategory] "
    "WHERE CategoryOwner = 'CData Software'"
)

Extract, Transform, and Load the Neo4J Data

With a connection and query in hand, use petl to extract, transform, and load the Neo4J data. In this example, we extract Neo4J data, sort the data by the CategoryName column, and load the data into a CSV file.

table1 = etl.fromdb(conn, sql)

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

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

Neo4J is a read-only source in Connect AI, so this pipeline can extract and transform Neo4J data but not load rows back. Close the connection when the extract is complete:

conn.close()

With the CData Connect AI Python SDK, you can work with Neo4J data just like you would with any database, including direct access to data in ETL packages like petl.

More Information and Free Trial

Now you can pipe live Neo4J data through petl using the CData Connect AI Python SDK. For more information on connecting to Neo4J (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Neo4J data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT CategoryId, CategoryName "
    "FROM [Neo4j1].[Neo4j].[ProductCategory] "
    "WHERE CategoryOwner = 'CData Software'"
)

table1 = etl.fromdb(conn, sql)

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

etl.tocsv(table2, 'productcategory_data.csv')
conn.close()