Columns and types
VARCHAR
- indicator
TIMESTAMP
- date
DOUBLE
- value
Current logical schema for lake.economic
Inspect the complete lake.economic schema
| Column | Type |
|---|---|
| indicator | VARCHAR |
| date | TIMESTAMP |
| value | DOUBLE |
No matching rows. Clear the filter to see all records.
Current logical schema for lake.economic
Query lake.economic 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
Macro indicator series (long form): one row per indicator per date.
Discover macro series in a date window
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.
A row is one indicator and date, not a yearly aggregate. Copy an actual returned indicator name when narrowing the query, then establish that series unit before interpreting value. No named series or value is assumed here.
SELECT indicator, date, value
FROM lake.economic
WHERE date >= TIMESTAMP '2026-09-01'
AND date < TIMESTAMP '2026-09-19'
ORDER BY indicator, date
LIMIT 20;Submit the economic 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 economic 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-economic-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
Macro indicator series (long form): one row per indicator per date.
Long-form macro series: one row per (`indicator`, `date`). Filter by `indicator` and pivot in SQL rather than expecting a column per series.
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