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Market-data lake

lake.stockindustries dataset schema

Column types and research semantics for NexusTrade’s lake.stockindustries dataset. Read the supported schema before submitting a bounded SQL query.

Query name
lake.stockindustries
Storage partition
snapshot
Columns
130
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Columns and types

VARCHAR

  • ticker
  • symbol
  • name
  • description

BOOLEAN

  • 3dPrinting
  • advertising
  • aerospace
  • agriculture
  • airline
  • alternativeEnergy
  • analytics
  • art

Showing 12 of 130 recorded rows. The complete table is below.

Current logical schema for lake.stockindustries

Inspect the complete lake.stockindustries schema

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

tickerVARCHAR
symbolVARCHAR
nameVARCHAR
descriptionVARCHAR
3dPrintingBOOLEAN
advertisingBOOLEAN
aerospaceBOOLEAN
agricultureBOOLEAN
airlineBOOLEAN
alternativeEnergyBOOLEAN
analyticsBOOLEAN
artBOOLEAN
artificialIntelligenceBOOLEAN
augmentedRealityBOOLEAN
autoInsuranceBOOLEAN
automotiveBOOLEAN
autonomousTransportationBOOLEAN
batteryTechnologyBOOLEAN
bioinformaticsBOOLEAN
biotechnologyBOOLEAN
blockchainBOOLEAN
cannabisBOOLEAN
cleanEnergyBOOLEAN
clothesAndApparalBOOLEAN
cloudComputingBOOLEAN
computationalBiologyBOOLEAN
computationalChemistryBOOLEAN
constructionBOOLEAN
consumerElectronicsBOOLEAN
consumerGoodsBOOLEAN
cruiseBOOLEAN
cryptocurrencyBOOLEAN
customerEngagementBOOLEAN
cybersecurityBOOLEAN
dataVisualizationBOOLEAN
databaseBOOLEAN
defenseBOOLEAN
digitalHealthBOOLEAN
digitalMarketplaceBOOLEAN
digitalSignatureAndAuthenticationBOOLEAN
ecommerceBOOLEAN
edtechBOOLEAN
educationBOOLEAN
electricVehicleBOOLEAN
energyBOOLEAN
enterpriseSoftwareBOOLEAN
entertainmentAndMediaBOOLEAN
fashionAndApparelBOOLEAN
financialServicesBOOLEAN
foodAndBeverageBOOLEAN
foodDeliveryBOOLEAN
forestryBOOLEAN
frackingBOOLEAN
gamblingBOOLEAN
gamingBOOLEAN
gasBOOLEAN
goldBOOLEAN
graphicDesignBOOLEAN
graphicsCardBOOLEAN
hardwareBOOLEAN
healthInsuranceBOOLEAN
healthcareBOOLEAN
homeInsuranceBOOLEAN
homeSecurityBOOLEAN
hospitalityAndTravelBOOLEAN
immunotherapyBOOLEAN
informationTechnologyBOOLEAN
investingBOOLEAN
iotBOOLEAN
jewelryBOOLEAN
lifeInsuranceBOOLEAN
logisticsAndSupplyChainBOOLEAN
luxuryGoodsBOOLEAN
manufacturingBOOLEAN
materialScienceBOOLEAN
medicalDevicesBOOLEAN
medicineBOOLEAN
messagingBOOLEAN
miningAndNaturalResourcesBOOLEAN
mobileApplicationBOOLEAN
moviesBOOLEAN
musicAndAudioBOOLEAN
nanotechnologyBOOLEAN
nutritionBOOLEAN
oilBOOLEAN
oncologyBOOLEAN
outdoorAndRecreationalEquipmentBOOLEAN
paymentsBOOLEAN
personalAndHouseholdGoodsBOOLEAN
petCareBOOLEAN
pharmaceuticalsBOOLEAN
phonesBOOLEAN
printingBOOLEAN
productivityToolsBOOLEAN
publishingBOOLEAN
quantumComputingBOOLEAN
realEstateBOOLEAN
recreationalVehicleBOOLEAN
renewableEnergyBOOLEAN
researchBOOLEAN
retailBOOLEAN
rideShareBOOLEAN
roboticsBOOLEAN
saasBOOLEAN
securityBOOLEAN
semiconductorBOOLEAN
silverBOOLEAN
smartDevicesBOOLEAN
socialMediaBOOLEAN
socialNetworkBOOLEAN
softwareBOOLEAN
solarEnergyBOOLEAN
spaceExplorationBOOLEAN
sportsAndFitnessBOOLEAN
sportsBettingBOOLEAN
streamingBOOLEAN
technologyBOOLEAN
telecommunicationsBOOLEAN
transportationBOOLEAN
utilitiesBOOLEAN
vaccinesBOOLEAN
veterinaryBOOLEAN
videoConferencingBOOLEAN
videosBOOLEAN
virtualRealityBOOLEAN
wasteManagementAndRecyclingBOOLEAN
waterPurifactionBOOLEAN
waterTreatmentBOOLEAN
wearableBOOLEAN
windEnergyBOOLEAN

Current logical schema for lake.stockindustries

Query lake.stockindustries 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

Alias of stock_industries.

Keep this table for compatibility with an existing query. Start a new task with the linked preferred schema.

Bounded stockindustries preview

Query example, not executed here. Inspect the actual returned rows and dates before treating it as evidence.

This compatibility preview uses the declared fields. Prefer the linked current reference for a new research task.

SQL
SELECT "ticker", "symbol", "name", "description", "3dPrinting"
FROM lake.stockindustries
WHERE "ticker" IN ('AAPL')
ORDER BY "ticker", "symbol", "name", "description", "3dPrinting"
LIMIT 20;

Submit the stockindustries 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 stockindustries 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-stockindustries-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

Alias of stock_industries.

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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