load-sample-data

Load Sample Data

Copy sample tables from bigquery-public-data into the user's dataset using bq cp. All copied tables are prefixed with sample_ so they can be easily identified and cleaned up later.

Environment

Verify env vars before running any command. If either is empty, ask the user to set it.

echo "Project: $GOOGLE_CLOUD_PROJECT"
echo "Dataset: $REVOS_BQ_DATASET"

• $GOOGLE_CLOUD_PROJECT — BQ project ID
• $REVOS_BQ_DATASET — target dataset

Step 1: Check Existing Tables

bq ls $REVOS_BQ_DATASET

If tables exist, list them. They will be relevant in Step 3 for collision handling.

Step 2: Present Sample Dataset Catalog

Available sample datasets:

1. thelook_ecommerce (default) — B2C e-commerce data
   Tables: sample_users, sample_orders, sample_order_items, sample_products, sample_events, sample_inventory_items, sample_distribution_centers
   Rows: ~100K users, ~300K orders
   Good for: customer analytics, purchase funnels, product performance

2. google_analytics_sample — Web analytics session data
   Tables: sample_ga_sessions (single table, one day snapshot)
   Rows: ~900K sessions
   Good for: web traffic analysis, user behavior, channel attribution

3. austin_bikeshare — Bikeshare trip and station data
   Tables: sample_bikeshare_trips, sample_bikeshare_stations
   Rows: ~1.3M trips
   Good for: geospatial analysis, demand forecasting, utilization metrics

Step 3: Copy Tables

If any table names from the chosen sample dataset collide with existing tables (from Step 1), ask the user whether to overwrite or skip each collision before copying. Skip collisions the user chose not to overwrite.

thelook_ecommerce

for table in users orders order_items products events inventory_items distribution_centers; do
  echo "Copying sample_$table..."
  bq cp -f bigquery-public-data:thelook_ecommerce.$table \
    $GOOGLE_CLOUD_PROJECT:$REVOS_BQ_DATASET.sample_$table
done

google_analytics_sample

The ga_sessions table is date-sharded. Copy a representative day:

echo "Copying sample_ga_sessions..."
bq cp -f bigquery-public-data:google_analytics_sample.ga_sessions_20170801 \
  $GOOGLE_CLOUD_PROJECT:$REVOS_BQ_DATASET.sample_ga_sessions

austin_bikeshare

for table in bikeshare_trips bikeshare_stations; do
  echo "Copying sample_$table..."
  bq cp -f bigquery-public-data:austin_bikeshare.$table \
    $GOOGLE_CLOUD_PROJECT:$REVOS_BQ_DATASET.sample_$table
done

Step 4: Verify

for table in <copied_tables>; do
  echo -n "$table: "
  bq query --nouse_legacy_sql --format=csv \
    "SELECT COUNT(*) FROM \`$GOOGLE_CLOUD_PROJECT.$REVOS_BQ_DATASET.$table\`" 2>/dev/null | tail -1
done

Final Response

Sample data loaded into $REVOS_BQ_DATASET.

Source: bigquery-public-data:<dataset_name>
Tables copied:
- <table_1>: <row_count> rows
- <table_2>: <row_count> rows

Next steps:
- Run "explore lakehouse" to inspect the data
- Run "create dbt transformations" to build bronze/silver/gold models
- Run "create cube" to generate Cube.dev semantic models

Rules

• Use bq cp -f (not CREATE TABLE AS SELECT) — faster, no query costs, preserves schema. The -f flag is required to avoid cross-region confirmation prompts that block non-interactive shells.
• Show progress for each table being copied.
• Report any failures clearly with the bq error message.
• Always prefix destination tables with sample_ — this allows easy identification and cleanup of sample data.