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# Add virtual environment support for running in Google Colab !apt-get install -qq python3.10-venv
# Install PyAirbyte %pip install --quiet airbyte
Locating your Data Source
To see what data sources are available, you can check our docs or run the following:
# Import PyAirbyte import airbyte as ab
# Show all available connectors ab.get_available_connectors()
Load the Source Data using PyAirbyte
Create and install a source connector:
import airbyte as ab
# Create and install the source: source: ab.Source = ab.get_source("source-faker")
Installing 'source-faker' into virtual environment '/content/.venv-source-faker'. Running 'pip install airbyte-source-faker'...
Connector 'source-faker' installed successfully! For more information, see the source-faker documentation: https://docs.airbyte.com/integrations/sources/faker#reference
# Configure the source source.set_config( config={ "count": 50_000, # Adjust this to get a larger or smaller dataset "seed": 123, }, ) # Verify the config and creds by running `check`: source.check()
Connection check succeeded for `source-faker`.
Read Data from the PyAirbyte Cache
Once data is read, we can do anything we want to with the resulting streams. This includes to_pandas() which registers a Pandas dataframe and to_sql_table() which gives us a SQLAlchemy Table boject, which we can use to run SQL queries.
# Select all of the source's streams and read data into the internal cache: source.select_all_streams() read_result: ab.ReadResult = source.read()
Read Progress
Started reading at 20:10:41.
Read 100,100 records over 1min 17s (1,300.0 records / second).
Wrote 100,100 records over 11 batches.
Finished reading at 20:11:59.
Started finalizing streams at 20:11:59.
Finalized 11 batches over 1 seconds.
Completed 3 out of 3 streams:
users
purchases
products
Completed writing at 20:12:01. Total time elapsed: 1min 19s
Completed `source-faker` read operation at 20:12:01.
# Display or transform the loaded data products_df = read_result["products"].to_pandas() display(products_df)
PyAirbyte integrates with Pandas, which integrates with matplotlib as well as many other popular libraries. We can use this as a means of quickly creating graphs.
%pip install matplotlib
import matplotlib.pyplot as plt
users_df = read_result["users"].to_pandas()
plt.hist(users_df["age"], bins=10, edgecolor="black") plt.title("Histogram of Ages") plt.xlabel("Ages") plt.ylabel("Frequency") plt.show()
Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1) Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.0) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1) Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.48.1) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5) Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.23.5) Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.2) Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0) Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1) Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0) /usr/local/lib/python3.10/dist-packages/duckdb_engine/__init__.py:178: DuckDBEngineWarning: duckdb-engine doesn't yet support reflection on indices warnings.warn(
Working in SQL
Since data is cached in a local DuckDB database, we can query the data with SQL.
We can do this in multiple ways. One way is to use the JupySQL Extension, which we'll use below.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/95.7 kB ? eta -:--:-- ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 92.2/95.7 kB 2.9 MB/s eta 0:00:01 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 95.7/95.7 kB 2.3 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 250.9/250.9 kB 13.9 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 193.0/193.0 kB 20.5 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 41.1/41.1 kB 5.2 MB/s eta 0:00:00 Deploy Panel apps for free on Ploomber Cloud! Learn more: https://ploomber.io/s/signup
# Get the SQLAlchemy 'engine' object for the cache engine = read_result.cache.get_sql_engine() # Pass the engine to JupySQL %sql engine
# Get table objects for the 'users' and 'purchases' streams users_table = read_result.cache["users"].to_sql_table() purchases_table = read_result.cache["purchases"].to_sql_table() display([users_table.fullname, purchases_table.fullname])
['main.users', 'main.purchases']
%%sql # Show most recent purchases by purchase date: SELECT users.id, users.name, purchases.product_id, purchases.purchased_at FROM {{ users_table.fullname }} AS users JOIN {{ purchases_table.fullname }} AS purchases ON users.id = purchases.user_id ORDER BY purchases.purchased_at DESC LIMIT 10
Running query in 'duckdb:///.cache/default_cache_db.duckdb'
# Show tables for the other streams %sqlcmd tables
Name products purchases users _PyAirbyte_state _PyAirbyte_streams
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About the Author
Should you build or buy your data pipelines?
Download our free guide and discover the best approach for your needs, whether it's building your ELT solution in-house or opting for Airbyte Open Source or Airbyte Cloud.
