bauplan-explore-data

Exploring Data in Bauplan

Explore and understand data stored in a Bauplan lakehouse using the Python SDK. This skill is read-only. It must not create tables, import data, run pipelines, or merge branches.

If the user asks for any write operation, stop and suggest switching to a write-capable skill (bauplan-data-pipeline or bauplan-safe-ingestion).

Before You Start

Ask the user which branch or ref to explore. All reads must be scoped to an explicit ref. Never rely on implicit defaults.

Environment Setup

Before writing any Python, check whether the project uses uv (look for pyproject.toml or uv.lock). If so, use uv run python to execute scripts and uv add to install packages. Otherwise, use the system python and pip install.

Ensure the required packages are installed:

• bauplan (the Bauplan Python SDK — required)
• polars (preferred for DataFrame operations — zero-copy Arrow interop)

Do not use pandas. Bauplan's client.query() returns a PyArrow table directly. Polars reads Arrow natively with zero-copy (pl.from_arrow(table)). Pandas requires a full data copy and is slower and more memory-intensive.

Required Deliverables

Every exploration MUST produce a summary.md file in the project root. This file is written in Phase 4. If you reach the end of Phase 3, you MUST proceed to Phase 4 and write summary.md. Do not end the conversation after Phase 3. The exploration is incomplete without summary.md.

Phased Execution (Critical)

This skill runs in four phases. Each phase is a separate bash execution of data_explorer.py. After each phase, report findings in the chat before proceeding to the next phase. The user must see incremental progress throughout the exploration.

The phases are:

Phase	Name	Typical duration	Gate
1	Discovery	<30s	Report table list. Ask which tables to explore.
2	Schema + Semantics	1-2 min	Report schemas and table descriptions. Proceed automatically.
3	Profiling + Anomalies	1-2 min per table	Announce each table before profiling, report findings after.
4	Joins + Summary	<1 min	Write summary.md. Present to user.

Between every phase, post a chat message summarizing what was found. Do not chain all phases into a single bash call.

Responsiveness rule (hard constraint): Always post a chat message between consecutive bash calls. Never execute two bash calls back-to-back without a message to the user in between.

Single Script, Iterative Execution

All exploration code lives in one file: data_explorer.py in the project root. Overwrite this file at the start of each phase with the code for that phase. Each phase prints its findings to stdout. Do not create additional Python files or subdirectories.

Setup Block

Write the following setup once at the top of data_explorer.py. All subsequent examples assume this block exists.

import bauplan
import polars as pl
from datetime import datetime, timezone

client = bauplan.Client()
ref = "<ref_to_explore>"  # branch name or commit hash — always explicit

---

Phase 1 — Discovery

Goal: List all namespaces and tables available on the ref. Report them. Ask the user which tables to explore in depth.

namespaces = list(client.get_namespaces(ref=ref))
print("=== Namespaces ===")
for ns in namespaces:
    print(f"  {ns.namespace}")

tables = list(client.get_tables(ref=ref))
print("\n=== Tables ===")
for t in tables:
    print(f"  {t.namespace}.{t.name}")

After execution: Post the table list in the chat. Ask: "Which tables should I explore in depth? I can inspect all of them, or you can pick a subset." If the user selects a subset, only those tables proceed to Phase 2. If the user says "all," proceed with all tables.

---

Phase 2 — Schema + Semantics

Goal: For each selected table, inspect its schema, sample 20 rows, and produce a one-sentence description of its purpose and grain.

2A. Schema and metadata

table = client.get_table(table="my_table", namespace="bauplan", ref=ref)
print(f"\n=== {table.namespace}.{table.name} ===")
print(f"Records: {table.records}")
for c in table.fields:
    print(f"  {c.name}: {c.type}")

2B. Sample and semantics

res = client.query("""
    SELECT *
    FROM bauplan.my_table
    LIMIT 20
""", ref=ref, max_rows=20)
# client.query() returns a PyArrow table directly — no .to_arrow() needed
df = pl.from_arrow(res)
print(df)

From the schema and sample, produce a one-sentence description: what entity each row represents, what the grain is, and what the table likely feeds into. Print this description as part of the output.

Example: "bauplan.raw_ecommerce_events: one row per user interaction with a product, timestamped, grouped by session."

After execution: Post schemas and descriptions in the chat. Proceed to Phase 3 automatically.

If more than 5 tables are selected, split Phase 2 into batches of 5 tables per bash call. Post findings after each batch.

---

Phase 3 — Profiling + Anomalies

Goal: Profile each selected table and detect anomalies. Process one table at a time. Announce each table before running queries, then report findings immediately after.

Execution rule (hard constraint)

Profile ONE table per bash execution. Do not loop over multiple tables in a single script run.

The sequence for each table is:

1. Chat message. Tell the user which table you are about to profile and where you are in the list: "Profiling bauplan.orders (3 of 7)..."
2. Rewrite data_explorer.py with queries for that single table only.
3. Run it. One bash call, one table.
4. Chat message. Report findings using the compact format below.
5. Move to the next table. Go back to step 1.

Do not combine multiple tables into one script. Do not use a for-loop over tables. Each bash call must target exactly one table. This ensures the user sees progress after every table.

Report format:

bauplan.orders (3/7)
  Rows: 1,234,567
  Time range: 2024-01-01 → 2024-12-31 (56 days stale)
  Null flags: shipping_address (72% null)
  Duplicate keys: none

If standard checks raise a flag, tell the user before running deep checks: "→ shipping_address is 72% null. Running deep checks..." Then report those results in a follow-up message.

Standard checks (always run)

Combine row count, time range, and null rates into a single query per table. Phase 2 already collected the schema, so you know every column name. Generate the query dynamically.

# One query covers row count, time range, and null rates for all columns.
# Adjust column names based on the schema collected in Phase 2.
client.query("""
    SELECT
        COUNT(*) AS row_count,
        MIN(event_time) AS min_t,
        MAX(event_time) AS max_t,
        1.0 - CAST(COUNT(order_id) AS DOUBLE) / COUNT(*) AS null_rate_order_id,
        1.0 - CAST(COUNT(customer_id) AS DOUBLE) / COUNT(*) AS null_rate_customer_id,
        1.0 - CAST(COUNT(shipping_address) AS DOUBLE) / COUNT(*) AS null_rate_shipping_address
    FROM bauplan.orders
""", ref=ref, max_rows=1)

For every timestamp column, compare MAX to today. If the gap exceeds what the table's grain implies (e.g., an hourly event table whose latest row is 30 days old), flag the table as potentially stale. Print the gap in days.

Flag any column where the null rate exceeds 50%.

Candidate key duplicates (second query). For columns whose names contain _id or that appear first in the schema, check for duplicates.

client.query("""
    SELECT order_id, COUNT(*) AS n
    FROM bauplan.my_table
    GROUP BY order_id
    HAVING COUNT(*) > 1
    LIMIT 10
""", ref=ref, max_rows=10)

Report the count of duplicated keys and a few examples if any exist.

That is two queries per table for standard checks.

Deep checks (opt-in)

Run these when the standard checks raise a flag, or when the user explicitly requests a thorough inspection. Announce what triggered the deep check and which column you are investigating.

Cardinality surprises. Compute distinct count for categorical columns (status, type, category) and identifiers (user_id, order_id). Flag if a categorical column has unexpectedly high cardinality or an identifier has unexpectedly low cardinality.

client.query("""
    SELECT
        COUNT(DISTINCT status) AS distinct_status,
        COUNT(DISTINCT user_id) AS distinct_user_id
    FROM bauplan.my_table
""", ref=ref, max_rows=1)

Value distribution for flagged columns. When a column has a high null rate or unexpected cardinality, sample its values.

client.query("""
    SELECT status, COUNT(*) AS n
    FROM bauplan.my_table
    GROUP BY status
    ORDER BY n DESC
    LIMIT 20
""", ref=ref, max_rows=20)

Type-value mismatches. For columns whose names imply a specific format (email, url, phone, ip_address, zip_code), sample values and verify they match the expected pattern.

client.query("""
    SELECT email
    FROM bauplan.my_table
    WHERE email IS NOT NULL
    LIMIT 20
""", ref=ref, max_rows=20)

Inspect the sample. If values clearly violate the expected format, flag the column.

---

Phase 4 — Joins + Summary

Goal: Identify join candidates across tables, then write summary.md.

4A. Join candidates

After inspecting multiple tables, look for columns that serve as join keys.

Name matching. Scan column names across all inspected tables. Columns with identical names or conventional foreign key patterns (e.g., PULocationID matching LocationID) are candidates.

Key overlap. For each candidate pair, verify overlap.

client.query("""
    SELECT COUNT(*) AS overlap
    FROM (
        SELECT DISTINCT user_id FROM bauplan.orders
        INTERSECT
        SELECT DISTINCT user_id FROM bauplan.users
    )
""", ref=ref, max_rows=1)

Join cardinality. Determine whether the relationship is one-to-one, one-to-many, or many-to-many.

client.query("""
    SELECT
        COUNT(*) AS total_rows,
        COUNT(DISTINCT user_id) AS distinct_keys
    FROM bauplan.orders
""", ref=ref, max_rows=1)

If total_rows == distinct_keys, the key is unique on that side.

For each viable join, print: the two tables, the join columns, the overlap count, and the cardinality (e.g., "orders.user_id → users.user_id: 98% overlap, many-to-one").

4B. Write summary.md

Write summary.md in the project root. This is a required deliverable. Use the template below.

# Data Exploration Summary

**Ref:** <ref explored>
**Date:** <timestamp>
**Tables inspected:** <count>

## Tables

### <namespace>.<table_name>

**Semantics:** <one-sentence description of purpose and grain>

**Stats:**
- Rows: <count>
- Time range: <min> → <max> (<N days stale> or "fresh")

**Schema (key columns):**
| Column | Type |
|--------|------|
| col1   | type |
| col2   | type |

**Anomalies:**
<List each flag from Phase 3. If none, state "No anomalies detected.">

**Join candidates:**
<List viable joins with overlap and cardinality. If none, state "No join candidates identified.">

---

(Repeat for each table.)

## Cross-Table Observations

<Any patterns that span multiple tables: shared keys, referential integrity gaps, schema inconsistencies, temporal misalignment between tables. If none, state "No cross-table observations.">

Separate facts (derived from queries) from inferences (suggested by patterns). Label inferences explicitly.

After execution: Present summary.md to the user. The exploration is complete only after this file is delivered.

---

Compare Across Refs

When the user needs to compare branches, run the same query with different ref= values.

q = "SELECT COUNT(*) AS n FROM bauplan.my_table"
n_main = client.query(q, ref="main", max_rows=1)
n_dev = client.query(q, ref="<username>.<branch>", max_rows=1)

Export Results to File

Use CSV by default. Switch to Parquet when the result set exceeds ~1M rows.

# CSV (default)
client.query_to_csv_file(
    path="results.csv",
    query="SELECT col1, col2 FROM bauplan.my_table WHERE event_date >= '2026-01-01'",
    ref=ref,
    max_rows=1_000_000,
)

# Parquet (large results only)
client.query_to_parquet_file(
    path="results.parquet",
    query="SELECT col1, col2 FROM bauplan.my_table WHERE event_date >= '2026-01-01'",
    ref=ref,
    max_rows=10_000_000,
)

Query Safety Rules

• Every SELECT must include a LIMIT clause.
• Every SELECT must list columns explicitly. The only exception is SELECT * in Phase 2 (semantics step) where the goal is to see all columns in a small sample.
• Use max_rows as an additional SDK-level guardrail.
• Avoid wide scans when a filter can reduce data early.

Outputs

The exploration produces two artifacts:

1. data_explorer.py — the exploration script in its final state.
2. summary.md — structured summary of all findings, written in Phase 4.

Both live in the project root.

---

Reference

When unsure about a method signature, CLI flag, or concept, fetch the relevant doc page via WebFetch rather than guessing. Pages are markdown and LLM-friendly.

Python SDK: https://docs.bauplanlabs.com/reference/bauplan.md

Relevant concept pages:

• Tables: https://docs.bauplanlabs.com/concepts/tables.md
• Namespaces: https://docs.bauplanlabs.com/concepts/namespaces.md
• Data branches: https://docs.bauplanlabs.com/concepts/git-for-data/data-branches.md

Full doc index: https://docs.bauplanlabs.com/llms.txt

CLI: The bauplan CLI is self-documenting:

• bauplan --help — lists all available commands
• bauplan <command> --help — shows arguments and options for a specific command (e.g., bauplan query --help, bauplan table --help)

Validating generated Python: After writing or updating a Python script, run ruff check and ruff format to catch syntax errors and style issues, and ty to catch type errors — these verify the code compiles and the SDK calls are well-formed without executing it. Only run these if they are installed (check with which ruff / which ty).