🐅 Tiger Data
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FAQ & Quick Start

🔥 Frequently Asked Questions

What format are datasets in?

All datasets are provided in Apache Parquet format (with CSV fallback). Parquet is 10x faster than CSV for queries and takes 50% less storage space. It's the industry standard for data warehouses and ML pipelines.

import polars as pl df = pl.read_parquet("tiger-data.parquet") print(df.shape) # (1168684, 38)
How fresh is the data?

Most datasets update daily or hourly. Enterprise datasets like EU B2B Risk update monthly. Trading signals refresh in real-time. Each dataset page shows exact update frequency and last refresh time.

Can I use this commercially?

Yes! All Tiger Data datasets come with a commercial license. You can build products, SaaS applications, and sell derived insights. No attribution required (though appreciated!).

What if I need custom data?

Enterprise tier includes custom dataset requests (1 per year). We can create bespoke datasets from official sources or combine multiple sources for your specific use case. Email admin@tigerdata.store to discuss.

Is there a free trial?

Yes! We offer free sample files (usually 1000+ rows) so you can test format, schema, and quality before buying. Full datasets are available immediately after checkout via Stripe.

How is data sourced and verified?

100% real data from official sources: Eurostat, OECD, ECB, INSEE, World Bank, Open-Meteo, etc. Every record includes provenance metadata (source + timestamp). All data is SHA256 checksummed and validated for quality. Zero synthetic data.

⚡ Quick Start (3 Minutes)

1️⃣
Purchase Dataset
Browse our catalog, select a dataset, and checkout securely via Stripe
2️⃣
Load Data
Use Polars, Pandas, Spark, or DuckDB to read Parquet file
3️⃣
Build Models
Data is pre-cleaned and documented. Start modeling immediately

💻 Code Examples

Python (Polars):

import polars as pl # Load dataset df = pl.read_parquet("EU_B2B_Risk.parquet") # Preview print(df.head()) print(df.schema) # Quick stats print(df.describe()) # Filter & analyze france = df.filter(pl.col("country") == "FR") print(france.group_by("sector").agg(pl.col("risk_score").mean()))

Python (Pandas):

import pandas as pd df = pd.read_parquet("France_RealEstate.parquet") df.describe() df[df['region'] == 'Ile-de-France'].groupby('year')['price'].mean().plot()

DuckDB SQL:

SELECT country, AVG(risk_score) as avg_risk, COUNT(*) as company_count FROM 'EU_B2B_Risk.parquet' GROUP BY country ORDER BY avg_risk DESC

🆘 Still Have Questions?

Email us: admin@tigerdata.store
Support available 24/7 for all purchase questions