Columns and types
VARCHAR
- ticker
- dividendType
DOUBLE
- cashAmount
TIMESTAMP
- declarationDate
- exDividendDate
- recordDate
- payDate
BIGINT
- frequency
Current logical schema for lake.dividends
Inspect the complete lake.dividends schema
| Column | Type |
|---|---|
| ticker | VARCHAR |
| cashAmount | DOUBLE |
| declarationDate | TIMESTAMP |
| exDividendDate | TIMESTAMP |
| recordDate | TIMESTAMP |
| payDate | TIMESTAMP |
| frequency | BIGINT |
| dividendType | VARCHAR |
No matching rows. Clear the filter to see all records.
Current logical schema for lake.dividends
Query lake.dividends 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
Dividend events per ticker: cash amount and the declaration / ex / record / pay dates.
Inspect a year of dividend events
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.
cashAmount is the reported cash event amount. declarationDate, exDividendDate and payDate answer different timing questions; an ex-date filter does not itself establish when an announcement was knowable.
SELECT ticker, declarationDate, exDividendDate, payDate, cashAmount
FROM lake.dividends
WHERE ticker = 'AAPL'
AND exDividendDate >= TIMESTAMP '2025-01-01'
AND exDividendDate < TIMESTAMP '2026-01-01'
ORDER BY exDividendDate
LIMIT 20;Submit the dividends 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 dividends 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.
python3 -m pip install nexustrade==1.42.0
export NEXUSTRADE_API_KEY="YOUR_LAKE_SCOPED_KEY"
python3 query.py
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-dividends-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)
)
Coverage and timing
Inspect source coverage and timing before using the rows
Dividend events per ticker: cash amount and the declaration / ex / record / pay dates.
Inspect the returned source dates and nullable fields. For an event-based strategy, use its public availability date to place the observation on the research timeline.
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