| name | ts-load-source-data |
| description | Load source data (CSV, Tableau download, manifest) into a warehouse. Infers schema, generates synthetic data for schema-only sources, and provisions tables. Snowflake supported; Databricks planned. |
Load Source Data
Load CSV data into a warehouse for ThoughtSpot to connect to. Supports four input
modes: CSV directory, Tableau Cloud download output, manifest JSON, and schema-only
with synthetic data generation.
Ask one question at a time for dependent decisions. Batch independent questions.
References
Prerequisites
- Snowflake profile configured — run
/ts-profile-snowflake if not
ts CLI installed: pip install -e tools/ts-cli (v0.15.0+)- For
method:python profiles: pip install snowflake-connector-python
- For
method:cli profiles: snow CLI installed and configured
- Source data accessible on disk (CSV files, download output, or manifest JSON)
Step 0 — Overview
On skill invocation, display this plan:
ts-load-source-data — load source data into a warehouse for ThoughtSpot.
Input modes
1 CSV directory — a folder of .csv files, one table per file
2 Tableau download — output JSON from ts tableau download
3 Manifest JSON — explicit schema + data file paths
4 Schema only — column definitions without data → generate synthetic sample data
Steps
- Identify source data (path + auto-detect mode) ........ you provide
- Select target warehouse (Snowflake) ................... auto (v1)
- Select Snowflake profile .............................. you choose
- Specify target location (database, schema) ............ you provide
- Schema review (inferred types, confirm/override) ...... you confirm
- Load data ............................................. auto
- Summary + next steps .................................. auto
Step 1 — Identify Source Data
Ask: "Provide the path to your source data — a directory of CSV files, a JSON file
from ts tableau download, or a manifest JSON."
Run ts load infer --source <path> to auto-detect and display:
Source type: {csv_dir | tableau_download | manifest | schema_only}
Tables found: {N}
If schema_only:
No data files found — this is a schema-only source.
Would you like to generate synthetic sample data? (Y/n)
If yes, how many rows per table? [100]:
Step 2 — Select Target Warehouse
v1 supports Snowflake only. Display:
Target warehouse: Snowflake
When Databricks support is added, prompt: Load into Snowflake or Databricks?
Step 3 — Select Snowflake Profile
Read ~/.claude/snowflake-profiles.json. Show:
Snowflake profiles:
1. {name} — {method_label} — {account_or_connection}
2. {name} — {method_label} — {account_or_connection}
Select a profile (enter number or name):
For method_label: method: python + auth: key_pair → python / key pair,
method: python + auth: password → python / password, method: cli → Snowflake CLI.
Step 4 — Specify Target Location
Ask: "Target database name:" and "Target schema name:"
Offer defaults if available from source metadata (e.g., Tableau download may have
the datasource name as a schema hint).
Step 5 — Schema Review
Display the inferred schema from Step 1 as a table:
Table: {TABLE_NAME} ({row_count} rows from {file})
# Column Name DB Column Name Inferred Type
1 Row ID ROW_ID INTEGER
2 Order Date ORDER_DATE DATE
3 Sales SALES FLOAT
4 Customer Name CUSTOMER_NAME VARCHAR(384)
Type overrides? Enter column # and new type (e.g. "1 VARCHAR(20)"), or confirm (Y):
Repeat for each table. Save the confirmed schema as a manifest JSON for reproducibility.
If schema-only + user accepted synthetic data in Step 1, run ts load generate here
with the confirmed schema.
Step 6 — Load Data
Run ts load snowflake with the confirmed schema:
ts load snowflake --source <path> --profile <name> \
--database <DB> --schema <SCH> --if-exists error
Show progress per table:
Loading into {DB}.{SCH}...
DUNDERMIFFLINSALESTABLE 42 rows ✓ created
CUSTOMERSTABLE 150 rows ✓ created
Step 7 — Summary
Display the load result:
Load complete.
Database: {DB}
Schema: {SCH}
Profile: {profile_name}
Tables loaded:
{TABLE_NAME} {rows} rows {columns} columns
Next steps:
• Create a ThoughtSpot connection to {DB}.{SCH}
→ /ts-object-connection-create (when available)
• Build ThoughtSpot objects over these tables
→ /ts-convert-from-tableau (if migrating from Tableau)
Changelog
| Version | Date | Summary |
|---|
| 1.0.0 | 2026-06-26 | Initial release — Snowflake loading with schema inference and synthetic data generation |
