feat: add code samples for dbt bigframes integration

dbt_bigframes_integration/.dbt.yml

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+dbt_sample_project:
+  outputs:
+    dev:        # The target environment name (e.g., dev, prod)
+      compute_region: us-central1         # Region used for compute operations
+      dataset: dbt_sample_dateset         # BigQuery dataset where dbt will create models
+      gcs_bucket: dbt_sample_bucket       # GCS bucket to store output files
+      location: US                        # BigQuery dataset location
+      method: oauth                       # Authentication method
+      priority: interactive               # Job priority: "interactive" or "batch"
+      project: bigframes-dev              # GCP project ID
+      threads: 1                          # Number of threads dbt can use for running models in parallel
+      type: bigquery                      # Specifies the dbt adapter
+  target: dev   # The default target environment

dbt_bigframes_integration/dbt_sample_project/dbt_project.yml

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+
+# Name your project! Project names should contain only lowercase characters
+# and underscores. A good package name should reflect your organization's
+# name or the intended use of these models
+name: 'dbt_sample_project'
+version: '1.0.0'
+
+# This setting configures which "profile" dbt uses for this project.
+profile: 'dbt_sample_project'
+
+# These configurations specify where dbt should look for different types of files.
+# The `model-paths` config, for example, states that models in this project can be
+# found in the "models/" directory. You probably won't need to change these!
+model-paths: ["models"]
+analysis-paths: ["analyses"]
+test-paths: ["tests"]
+seed-paths: ["seeds"]
+macro-paths: ["macros"]
+snapshot-paths: ["snapshots"]
+
+clean-targets:         # directories to be removed by `dbt clean`
+  - "target"
+  - "dbt_packages"
+
+
+# Configuring models
+# Full documentation: https://docs.getdbt.com/docs/configuring-models
+
+# In this example config, we tell dbt to build all models in the example/
+# directory as views. These settings can be overridden in the individual model
+# files using the `{{ config(...) }}` macro.
+models:
+  dbt_sample_project:
+    # Optional: These settings (e.g., submission_method, notebook_template_id,
+    # etc.) can also be defined directly in the Python model using dbt.config.
+    submission_method: bigframes
+    # Config indicated by + and applies to all files under models/example/
+    example:
+      +materialized: view

dbt_bigframes_integration/dbt_sample_project/models/example/dbt_bigframes_code_sample_1.py

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+# This example demonstrates one of the most general usages of transforming raw
+# BigQuery data into a processed table using a dbt Python model with BigFrames.
+# See more from: https://cloud.google.com/bigquery/docs/dataframes-dbt.
+#
+# Key defaults when using BigFrames in a dbt Python model for BigQuery:
+# - The default materialization is 'table' unless specified otherwise. This
+#   means dbt will create a new BigQuery table from the result of this model.
+# - The default timeout for the job is 3600 seconds (60 minutes). This can be
+#   adjusted if your processing requires more time.
+# - If no runtime template is provided, dbt will automatically create and reuse
+#   a default one for executing the Python code in BigQuery.
+#
+# BigFrames provides a pandas-like API for BigQuery data, enabling familiar
+# data manipulation directly within your dbt project. This code sample
+# illustrates a basic pattern for:
+# 1. Reading data from an existing BigQuery dataset.
+# 2. Processing it using pandas-like DataFrame operations powered by BigFrames.
+# 3. Outputting a cleaned and transformed table, managed by dbt.
+
+
+def model(dbt, session):
+    # Optional: Override settings from your dbt_project.yml file.
+    # When both are set, dbt.config takes precedence over dbt_project.yml.
+    #
+    # Use `dbt.config(submission_method="bigframes")` to tell dbt to execute
+    # this Python model using BigQuery DataFrames (BigFrames). This allows you
+    # to write pandas-like code that operates directly on BigQuery data
+    # without needing to pull all data into memory.
+    dbt.config(submission_method="bigframes")
+
+    # Define the BigQuery table path from which to read data.
+    table = "bigquery-public-data.epa_historical_air_quality.temperature_hourly_summary"
+
+    # Define the specific columns to select from the BigQuery table.
+    columns = ["state_name", "county_name", "date_local", "time_local", "sample_measurement"]
+
+    # Read data from the specified BigQuery table into a BigFrames DataFrame.
+    df = session.read_gbq(table, columns=columns)
+
+    # Sort the DataFrame by the specified columns. This prepares the data for
+    # `drop_duplicates` to ensure consistent duplicate removal.
+    df = df.sort_values(columns).drop_duplicates(columns)
+
+    # Group the DataFrame by 'state_name', 'county_name', and 'date_local'. For
+    # each group, calculate the minimum and maximum of the 'sample_measurement'
+    # column. The result will be a BigFrames DataFrame with a MultiIndex.
+    result = df.groupby(["state_name", "county_name", "date_local"])["sample_measurement"]\
+        .agg(["min", "max"])
+
+    # Rename some columns and convert the MultiIndex of the 'result' DataFrame
+    # into regular columns. This flattens the DataFrame so 'state_name',
+    # 'county_name', and 'date_local' become regular columns again.
+    result = result.rename(columns={'min': 'min_temperature', 'max': 'max_temperature'})\
+        .reset_index()
+
+    # Return the processed BigFrames DataFrame.
+    # In a dbt Python model, this DataFrame will be materialized as a table
+    return result

dbt_bigframes_integration/dbt_sample_project/models/example/dbt_bigframes_code_sample_2.py

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+# This example demonstrates how to build an **incremental dbt Python model**
+# using BigFrames.
+# 
+# Incremental models are essential for efficiently processing large datasets by
+# only transforming new or changed data, rather than reprocessing the entire
+# dataset every time. If the target table already exists, dbt will perform a
+# merge based on the specified unique keys; otherwise, it will create a new
+# table automatically.
+#
+# This model also showcases the definition and application of a **BigFrames
+# User-Defined Function (UDF)** to add a descriptive summary column based on
+# temperature data. BigFrames UDFs allow you to execute custom Python logic
+# directly within BigQuery, leveraging BigQuery's scalability.
+
+
+import bigframes.pandas as bpd
+
+def model(dbt, session):
+    # Optional: override settings from dbt_project.yml.
+    # When both are set, dbt.config takes precedence over dbt_project.yml.
+    dbt.config(
+        # Use BigFrames mode to execute this Python model. This enables
+        # pandas-like operations directly on BigQuery data.
+        submission_method="bigframes",
+        # Materialize this model as an 'incremental' table. This tells dbt to
+        # only process new or updated data on subsequent runs.
+        materialized='incremental',
+        # Use MERGE strategy to update rows during incremental runs.
+        incremental_strategy='merge',
+        # Define the composite key that uniquely identifies a row in the
+        # target table. This key is used by the 'merge' strategy to match
+        # existing rows for updates during incremental runs.
+        unique_key=["state_name", "county_name", "date_local"],
+    )
+
+    # Reference an upstream dbt model or an existing BigQuery table as a
+    # BigFrames DataFrame. It allows you to seamlessly use the output of another
+    # dbt model as input to this one.
+    df = dbt.ref("dbt_bigframes_code_sample_1")
+
+    # Define a BigFrames UDF to generate a temperature description.
+    # BigFrames UDFs allow you to define custom Python logic that executes
+    # directly within BigQuery. This is powerful for complex transformations.
+    @bpd.udf(dataset='dbt_sample_dataset', name='describe_udf')
+    def describe(
+        max_temperature: float,
+        min_temperature: float,
+    ) -> str:
+        is_hot = max_temperature > 85.0
+        is_cold = min_temperature < 50.0
+
+        if is_hot and is_cold:
+            return "Expect both hot and cold conditions today."
+        if is_hot:
+            return "Overall, it's a hot day."
+        if is_cold:
+            return "Overall, it's a cold day."
+        return "Comfortable throughout the day."
+
+    # Apply the UDF using combine and store the result in a column "describe".
+    df["describe"] = df["max_temperature"].combine(df["min_temperature"], describe)
+
+    # Return the transformed BigFrames DataFrame.
+    # This DataFrame will be the final output of your incremental dbt model.
+    # On subsequent runs, only new or changed rows will be processed and merged
+    # into the target BigQuery table based on the `unique_key`.
+    return df