The Database Parse Tool helps you quickly extract, organize, and customize data from large public datasets. Instead of downloading entire databases or sifting through unnecessary fields, this tool lets you focus only on the information you actually need, making research, analysis, and project-building much faster and cleaner.

To use it, select a category such as animals, birds, cars, or countries, then choose an available database within that category. Enter any search filters you want and click Load Available Columns to view all the fields the database provides. Pick the columns you need, press Build Custom Table, and finally download your customized dataset as a CSV file.

Data comes from the greek word nose and base comes from the latin word that roughly translates to salad. While the nordic word query means to hoard, or hoarder. So enjoy the nose salad hoarder tool! The more you know...

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Database Parse Tool Reference Data

1. CSV (Comma-Separated Values)

File Extension: .csv
Description: CSV is a simple, plain-text, tabular data format where each row is a record and each column is separated by commas. It is the most common format for databases, spreadsheets, and exports due to its universal compatibility.

Pros:
   Readable by nearly all software and programming languages.
   Lightweight and easy to parse.
   Great for custom filtering and table building.

Cons:
   No data types (everything is text).
   No nested structures or metadata.

Use Cases:
   Public datasets, spreadsheets, exports from APIs or apps.

2. JSON (JavaScript Object Notation)

File Extension: .json
Description: JSON is a structured text format that supports nested data (objects, arrays, lists). Ideal for APIs, modern web services, and datasets that include categories or hierarchical information.

Pros:
   Supports nested fields and complex structures.
   Lightweight and easy to process in code.
   Works with most public APIs.

Cons:
   Not ideal for large tabular datasets.
   Requires flattening before column selection.

Use Cases:
   API responses, animals/birds/countries metadata, configuration datasets.

3. SQL (Structured Query Language Dump)

File Extension: .sql
Description: SQL dumps contain full database structures, table definitions, and raw data. They are complete snapshots of databases and require a database engine to restore or interpret.

Pros:
   Includes full relational structure (tables, keys, relationships).
   Ideal for very large datasets.

Cons:
   Must be imported into a database server to use.
   Not instantly readable by the browser or JS tools.

Use Cases:
   Scientific datasets, government data dumps, archive imports.

4. Parquet

File Extension: .parquet
Description: A high-performance, column-based, compressed data format used for analytics and large-scale processing. Originally designed for big data platforms (Hadoop, Spark).

Pros:
   Extremely efficient for large datasets.
   Column-based → faster queries and filtering.
   Smaller size than CSV/JSON.

Cons:
   Not readable without a specialized library.
   Not ideal for beginners or simple web tools.

Use Cases:
   Big data, analytics dashboards, large public archives.

5. NDJSON (Newline-Delimited JSON)

File Extension: .ndjson
Description: A streaming-friendly format where each line is its own JSON object. Great for logs, large API exports, and datasets processed one record at a time.

Pros:
   Efficient for large, continuous datasets.
   Works well with APIs and log analyzers.
   Easy to append or chunk.

Cons:
   Must be parsed line-by-line.
   Not ideal for nested or relational structures.

Use Cases:
   Event streams, animal sightings logs, server logs, sequences.

6. GeoJSON

File Extension: .geojson
Description: A JSON-based format designed specifically for geographic and spatial data (points, shapes, polygons). Common in mapping datasets and environmental databases.

Pros:
   Represents real-world locations, shapes, and boundaries.
   Fully readable by mapping tools.

Cons:
   Complex shapes can produce large files.
   Requires flattening before tabular conversion.

Use Cases:
   Country borders, wildlife ranges, GIS datasets, spatial mapping.

Other Useful Notes for Database Parsing

Field Types & Structure:
   CSV = flat rows/columns.
   JSON/NDJSON = nested objects that may need flattening.
   Parquet = column-based and optimized for large datasets.

Ideal Use by Category:
   Animals/Birds/Countries → JSON or CSV.
   Cars & Specs → CSV, SQL, Parquet.
   Maps & geolocation → GeoJSON.

File Size Expectations:
   CSV: small → large.
   JSON: small → medium.
   SQL: medium → huge.
   Parquet: large → enormous (but compressed).

Filtering & Column Selection Tips:
   Use CSV for simple tabular filtering.
   Flatten JSON when you need sub-fields as columns.
   Parquet is best when datasets exceed millions of rows.