Columns and types

BIGINT

VARCHAR

ticker
taxonomy
concept
unit

DOUBLE

TIMESTAMP

periodStart
periodEnd
filed
availableAt

BOOLEAN

isInstant

Showing 12 of 20 recorded rows. The complete table is below.

Current logical schema for lake.sec_facts

Inspect the complete lake.sec_facts schema

Filter rows

Scroll sideways to compare all columns. Select a heading to sort.


cik	BIGINT
ticker	VARCHAR
taxonomy	VARCHAR
concept	VARCHAR
unit	VARCHAR
val	DOUBLE
periodStart	TIMESTAMP
periodEnd	TIMESTAMP
filed	TIMESTAMP
availableAt	TIMESTAMP
isInstant	BOOLEAN
fy	BIGINT
fp	VARCHAR
form	VARCHAR
availabilitySource	VARCHAR
frame	VARCHAR
rawArchiveKey	VARCHAR
rawSha256	VARCHAR
sourceFilingUrl	VARCHAR
accession	VARCHAR

Current logical schema for lake.sec_facts

Query lake.sec_facts using the column names and types below. The query validator and engine use this logical catalog; a field absent from an older shard returns typed NULL.

What this table contains

Raw SEC XBRL facts, one row per (concept, period, filing). Use when a needed concept is not a column on the statement tables.

Inspect a reported XBRL value with units

Query example, not executed here. The dates and filters define the proposed research scope; returned rows and current coverage must be checked in your workspace.

Read concept and unit together with val. periodEnd describes the accounting period; availableAt controls eligibility for a historical decision. Multiple filing versions require explicit selection before aggregation.

SQL

SELECT ticker, taxonomy, concept, unit, val, periodEnd, availableAt, accession
FROM lake.sec_facts
WHERE ticker = 'AAPL'
  AND availableAt >= TIMESTAMP '2026-09-01'
  AND availableAt < TIMESTAMP '2026-09-19'
ORDER BY availableAt, taxonomy, concept, accession
LIMIT 20;

Python SQL workflow ↗TypeScript SQL workflow ↗

Submit the sec_facts example and read back its parts

Run the exact SQL through the Python SDK

Save this page's SQL as query.sql and the runner below as query.py. Use a registered key with lake scope from Developers. The runner submits the displayed sec_facts query once, saves its operation, waits on that same ID, saves the returned manifest and downloads its Parquet parts.

This submission has not been executed for the reference page. Inspect the SQL, dates and resource limits before running it. Reuse the request key only for this identical SQL and limits; use a new key when changing them.

Shell

python3 -m pip install nexustrade==1.42.0
export NEXUSTRADE_API_KEY="YOUR_LAKE_SCOPED_KEY"
python3 query.py

Python

import json
from pathlib import Path
from nexustrade import NexusTradeClient

# Save the SQL displayed on this page as query.sql.
sql = Path("query.sql").read_text()
client = NexusTradeClient.from_environment()
operation = client.create_lake_query(
    {"query": sql, "limits": {"timeoutSeconds": 30, "maxRows": 50,
                              "maxResultBytes": 1048576}},
    idempotency_key="reference-sec_facts-20261012-v1",
)
Path("query-operation.json").write_text(json.dumps(operation, indent=2))
finished = client.wait_for_lake_query(operation["id"])
if finished["status"] != "completed":
    raise RuntimeError(finished)
manifest = client.get_lake_query_manifest(operation["id"])["result"]
Path("query-manifest.json").write_text(json.dumps(manifest, indent=2))
print(json.dumps(manifest, indent=2))
for part in manifest["parts"]:
    Path(f"query-{part['part']}.parquet").write_bytes(
        client.download_lake_query_part(operation["id"], part["part"], max_bytes=1048576)
    )

Create a lake-scoped key ↗Read Parquet results with Python ↗

Coverage and timing

Inspect source coverage and timing before using the rows