Parquet Python Connector
SQL-based access to Parquet from Python
Easily connect Python-based data access, visualization, ORM, ETL, AI/ML, and custom apps with Apache Parquet!
Python Connector Libraries for Parquet Data Connectivity. Integrate Parquet with popular Python tools like Pandas, SQLAlchemy, Dash & petl. Easy-to-use Python Database API (DB-API) Modules connect Parquet 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 Apache Parquet
Full-featured and consistent SQL access to any supported data source through Python
Connecting to Parquet with Python
CData Python Connectors leverage the Database API (DB-API) interface to make it easy to work with Parquet 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 Parquet
- Query Parquet to retrieve or update data
- Connect your Parquet data with Python data tools.
Connecting to Parquet 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.parquet as mod
conn = mod.connect("[email protected]; Password=password;")
#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM ParquetData")
rs = cur.fetchall()
for row in rs:
print(row)
engine = create_engine("parquet///Password=password&User=user")
df = pandas.read_sql("SELECT * FROM ParquetData", engine)
df.plot()
plt.show()
Visualize Parquet Data with pandas
The data-centric interfaces of the Parquet Python Connector make it easy to integrate with popular tools like pandas and SQLAlchemy to visualize data in real-time.
AI-assisted development with CData CLI
Build Parquet integrations faster with AI that understands your schema
Schema-aware AI
CData CLI gives AI coding tools access to your Parquet 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 Parquet.
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.
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