> For the complete documentation index, see [llms.txt](https://documentation.scrapin.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.scrapin.io/dataset/basics/editor.md).
# Delivery Format
We provide flexible access to our data, so your team always gets the information in the right format, at the right time. You can choose between 4 different flat file datasets format.
| Format | Pros | Cons |
| ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- |
| **JSON** | - Human-readable and widely used- Nested structures supported- Great for APIs and web data | - Large file sizes (not space-efficient)- Slower to parse on big datasets- Harder to process in batch (not line-delimited) |
| **JSONL** (JSON Lines) | - Line-delimited, easy to stream and process- Works well with big data pipelines (can split files easily)- Still human-readable | - Less widely adopted than plain JSON- No built-in schema enforcement- Slightly less convenient for nested arrays than JSON |
| **CSV** | - Very simple and widely supported- Compact for tabular data- Easy to open in Excel/Sheets | - No support for nested/complex data- No types (everything is text)- Ambiguities with delimiters, quoting, encoding |
| **Parquet** | - Highly compressed and columnar (great for analytics)- Efficient for large-scale queries (only reads needed columns)- Schema + data types preserved- Optimized for big data tools (Spark, Hive, Snowflake) | - Not human-readable- Higher overhead for small datasets- More complex libraries needed to read/write |
***
### What does the data look like?
When you choose to export or receive data, you can select different formats. Each format represents the data in a different way. Here’s what you can expect:
#### **1. JSON (JavaScript Object Notation)**
```json
{
"id": 1,
"name": "Alice",
"email": "alice@example.com",
"skills": ["Python", "SQL"]
}
```
**Key points:**
* A **human-readable text file** used almost everywhere.
* Stores data as objects with keys (`"name"`, `"email"`) and values.
* Can handle lists, nested objects, and complex structures.
**When to use it:**
* Great for structured data that needs to be read or shared easily.
* Widely used in APIs and integrations.
#### **2. JSONL (JSON Lines)**
**What it looks like:**
```json
{"id": 1, "name": "Alice", "email": "alice@example.com"}
{"id": 2, "name": "Bob", "email": "bob@example.com"}
{"id": 3, "name": "Charlie", "email": "charlie@example.com"}
```
**Key points:**
* Each **line in the file** is its own JSON object.
* Easy to process line by line, especially with large files.
* Lighter and faster than a big JSON array for bulk data.
**When to use it:**
* Perfect for **large-scale data processing** (Spark, Elasticsearch, etc.).
* Useful for **logs, events, or streaming-style data**.
#### **3. CSV (Comma-Separated Values)**
**What it looks like:**
```csv
id,name,email
1,Alice,alice@example.com
2,Bob,bob@example.com
3,Charlie,charlie@example.com
```
**Key points:**
* A **simple text table**, with each row as a record and each column separated by a comma (sometimes semicolon).
* Opens easily in Excel, Google Sheets, or any spreadsheet tool.
* Doesn’t support nested or complex data—everything is flattened into rows and columns.
**When to use it:**
* Best for **simple tabular data**.
* Ideal if you want to quickly view and manipulate your data in Excel.
#### **4. Parquet**
**What it looks like:**
* You **cannot open a Parquet file in a normal text editor**.
* It’s a **binary, compressed format** optimized for storage and analytics.
**Example (using Python/Pandas):**
```python
import pandas as pd
df = pd.read_parquet("data.parquet")
print(df.head())
```
Output (table view):
```
id name email
0 1 Alice alice@example.com
1 2 Bob bob@example.com
2 3 Charlie charlie@example.com
```
**Key points:**
* Stores data with proper types (numbers, strings, dates, etc.).
* Very efficient in terms of **size** and **query performance**.
* Supported by all major data tools (Snowflake, BigQuery, Spark, etc.).
**When to use it:**
* Best for **large datasets**.
* Ideal for **analytics pipelines** and **machine learning workloads**.
***
---
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