Amazon Redshift Python Connector
Read, write, and update Redshift with Python
Easily connect Python-based data access, visualization, ORM, ETL, AI/ML, and custom apps with Amazon Redshift!
Python Connector Libraries for Redshift Data Connectivity. Integrate Redshift with popular Python tools like Pandas, SQLAlchemy, Dash & petl. Easy-to-use Python Database API (DB-API) Modules connect Redshift data with Python and any Python-based applications.
Features
Specifications
CData Python connectors in action!
Watch the video overview for a first hand-look at the powerful data integration capabilities included in the CData Python Connectors.
Watch the Python Connector Video OverviewPython connectivity with Amazon Redshift
Full-featured and consistent SQL access to any supported data source through Python
Connecting to Redshift with Python
CData Python Connectors leverage the Database API (DB-API) interface to make it easy to work with Redshift from a wide range of standard Python data tools. Connecting to and working with your data in Python follows a basic pattern, regardless of data source:
- Configure the connection properties to Redshift
- Query Redshift to retrieve or update data
- Connect your Redshift data with Python data tools.
Connecting to Redshift in Python
To connect to your data from Python, import the extension and create a connection:
Once you import the extension, you can work with all of your enterprise data using the python modules and toolkits that you already know and love, quickly building apps that help you drive business.
import cdata.redshift as mod
conn = mod.connect("[email protected]; Password=password;")
#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM RedshiftTable")
rs = cur.fetchall()
for row in rs:
print(row)
engine = create_engine("redshift///Password=password&User=user")
df = pandas.read_sql("SELECT * FROM RedshiftTable", engine)
df.plot()
plt.show()
Visualize Redshift Data with pandas
The data-centric interfaces of the Redshift Python Connector make it easy to integrate with popular tools like pandas and SQLAlchemy to visualize data in real-time.
More than read-only: full update/CRUD support
Redshift Connector goes beyond read-only functionality to deliver full support for Create, Read Update, and Delete operations (CRUD). Your end-users can interact with the data presented by the Redshift Connector as easily as interacting with a database table.
AI-assisted development with CData CLI
Build Redshift integrations faster with AI that understands your schema
Schema-aware AI
CData CLI gives AI coding tools access to your Redshift schema. No more guessing table names or column types—AI sees the same metadata in your Python Connectors.
Your AI Knows SQL
How to find table names, column names, and how to generate SQL syntax are things that AI knows well from millions of training data. No need for customization, no hallucinations. Your AI acts like a domain specialist to Redshift.
More Accurate, More Token-Efficient
With CData CLI's queryable schema detection and highly efficient queries with filters, aggregation, joins with correct pushdown, your AI will achieve more accuracy with less token usage.
Popular Python Videos:
