How to Build an ETL App for Redshift 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 Redshift-connected applications and pipelines for extracting, transforming, and loading Redshift data. This article shows how to connect to Connect AI and use petl to extract, transform, and load Redshift 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 Redshift 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 "Redshift" from the Add Connection panel

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

   To connect to Redshift, set the following:

   • Server: Set this to the host name or IP address of the cluster hosting the Database you want to connect to.
   • Port: Set this to the port of the cluster.
   • Database: Set this to the name of the database. Or, leave this blank to use the default database of the authenticated user.
   • User: Set this to the username you want to use to authenticate to the Server.
   • Password: Set this to the password you want to use to authenticate to the Server.

   You can obtain the Server and Port values in the AWS Management Console:

   1. Open the Amazon Redshift console (http://console.aws.amazon.com/redshift).
   2. On the Clusters page, click the name of the cluster.
   3. On the Configuration tab for the cluster, copy the cluster URL from the connection strings displayed.
   
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 Redshift 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 Redshift

Use SQL to create a statement for querying Redshift. In this article, we read data from the Orders entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Redshift1).

sql = (
    "SELECT ShipName, ShipCity "
    "FROM [Redshift1].[Redshift].[Orders] "
    "WHERE ShipCountry = 'USA'"
)

Extract, Transform, and Load the Redshift Data

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

table1 = etl.fromdb(conn, sql)

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

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

Load New Rows Back into Redshift

When Redshift supports writes, load rows back with a batch INSERT. The SDK's executemany takes @name placeholders and a list of parameter dictionaries, one per row.

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [Redshift1].[Redshift].[Orders] (ShipName, ShipCity) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")

conn.close()

Note: Even for writable sources, a read-only PAT or connection permission will reject write operations.

With the CData Connect AI Python SDK, you can work with Redshift 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 Redshift data through petl using the CData Connect AI Python SDK. For more information on connecting to Redshift (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Redshift 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 ShipName, ShipCity "
    "FROM [Redshift1].[Redshift].[Orders] "
    "WHERE ShipCountry = 'USA'"
)

table1 = etl.fromdb(conn, sql)

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

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

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [Redshift1].[Redshift].[Orders] (ShipName, ShipCity) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")
conn.close()