CSV Tools

Sample JSONL File

A downloadable sample JSONL file with 30 fictional employee records, one JSON object per line — built for testing streaming parsers and log-style pipelines.

Data JSONL Text format
Download JSONL

sample-employees.jsonl

Data Preview

30 rows × 7 columns
1nameemaildepartmentrolesalarystart_dateoffice
2Marcus Chenmarcus.chen@example.comEngineeringSenior Software Engineer1550002019-03-15San Francisco
3Priya Sharmapriya.sharma@example.comEngineeringStaff Engineer1780002019-06-01San Francisco
4David Kimdavid.kim@example.comEngineeringSoftware Engineer1250002021-01-10New York
5Rachel Torresrachel.torres@example.comEngineeringEngineering Manager1680002020-02-20San Francisco
6James Okaforjames.okafor@example.comEngineeringJunior Developer920002024-06-15Austin
7Lena Vogtlena.vogt@example.comEngineeringDevOps Engineer1400002022-04-01New York
8Amir Patelamir.patel@example.comEngineeringBackend Engineer1320002023-01-09London
9Sofia Lindbergsofia.lindberg@example.comDesignLead Designer1450002019-09-12New York
10Carlos Riveracarlos.rivera@example.comDesignUX Designer1120002021-07-20San Francisco
11Hannah Beckerhannah.becker@example.comDesignUI Designer1050002022-11-01London
12Yuki Tanakayuki.tanaka@example.comDesignProduct Designer1180002023-03-14San Francisco
13Olivia Martinolivia.martin@example.comMarketingVP of Marketing1650002019-04-22New York
14Ethan Brooksethan.brooks@example.comMarketingContent Strategist950002021-10-05Austin
15Nina Kowalskinina.kowalski@example.comMarketingSEO Specialist880002022-08-15New York
16Daniel Ochoadaniel.ochoa@example.comMarketingMarketing Analyst910002023-05-20Austin
17Samira Hassansamira.hassan@example.comMarketingSocial Media Manager820002024-01-08London
18Tyler Washingtontyler.washington@example.comSalesSales Director1580002019-11-30New York
19Jessica Huangjessica.huang@example.comSalesAccount Executive1100002020-06-14San Francisco
20Ryan O'Brienryan.obrien@example.comSalesAccount Executive1050002021-03-22London
21Fatima Al-Rashidfatima.alrashid@example.comSalesSales Development Rep720002023-09-01Austin
22Kevin Dupontkevin.dupont@example.comSalesSolutions Engineer1350002022-01-17San Francisco
23Megan Stewartmegan.stewart@example.comSalesAccount Manager980002024-03-11New York
24Laura Chenlaura.chen@example.comHRHR Director1480002019-08-05New York
25Brian Nakamurabrian.nakamura@example.comHRHR Business Partner1050002020-12-01San Francisco
26Chloe Duboischloe.dubois@example.comHRRecruiter780002022-05-23London
27Angela Morettiangela.moretti@example.comHRPeople Operations850002023-07-10Austin
28Isaac Fernandezisaac.fernandez@example.comEngineeringFrontend Engineer1280002022-09-19New York
29Sarah Mitchellsarah.mitchell@example.comDesignDesign Systems Lead1380002020-04-06San Francisco
30Omar Farahomar.farah@example.comEngineeringQA Engineer950002024-02-12London
31Natalie Parknatalie.park@example.comMarketingGrowth Manager1080002021-11-28San Francisco

Raw File Contents

sample-employees.jsonl
{"name": "Marcus Chen", "email": "marcus.chen@example.com", "department": "Engineering", "role": "Senior Software Engineer", "salary": 155000, "start_date": "2019-03-15", "office": "San Francisco"}
{"name": "Priya Sharma", "email": "priya.sharma@example.com", "department": "Engineering", "role": "Staff Engineer", "salary": 178000, "start_date": "2019-06-01", "office": "San Francisco"}
{"name": "David Kim", "email": "david.kim@example.com", "department": "Engineering", "role": "Software Engineer", "salary": 125000, "start_date": "2021-01-10", "office": "New York"}
{"name": "Rachel Torres", "email": "rachel.torres@example.com", "department": "Engineering", "role": "Engineering Manager", "salary": 168000, "start_date": "2020-02-20", "office": "San Francisco"}
{"name": "James Okafor", "email": "james.okafor@example.com", "department": "Engineering", "role": "Junior Developer", "salary": 92000, "start_date": "2024-06-15", "office": "Austin"}
{"name": "Lena Vogt", "email": "lena.vogt@example.com", "department": "Engineering", "role": "DevOps Engineer", "salary": 140000, "start_date": "2022-04-01", "office": "New York"}
{"name": "Amir Patel", "email": "amir.patel@example.com", "department": "Engineering", "role": "Backend Engineer", "salary": 132000, "start_date": "2023-01-09", "office": "London"}
{"name": "Sofia Lindberg", "email": "sofia.lindberg@example.com", "department": "Design", "role": "Lead Designer", "salary": 145000, "start_date": "2019-09-12", "office": "New York"}
{"name": "Carlos Rivera", "email": "carlos.rivera@example.com", "department": "Design", "role": "UX Designer", "salary": 112000, "start_date": "2021-07-20", "office": "San Francisco"}
{"name": "Hannah Becker", "email": "hannah.becker@example.com", "department": "Design", "role": "UI Designer", "salary": 105000, "start_date": "2022-11-01", "office": "London"}
{"name": "Yuki Tanaka", "email": "yuki.tanaka@example.com", "department": "Design", "role": "Product Designer", "salary": 118000, "start_date": "2023-03-14", "office": "San Francisco"}
{"name": "Olivia Martin", "email": "olivia.martin@example.com", "department": "Marketing", "role": "VP of Marketing", "salary": 165000, "start_date": "2019-04-22", "office": "New York"}
{"name": "Ethan Brooks", "email": "ethan.brooks@example.com", "department": "Marketing", "role": "Content Strategist", "salary": 95000, "start_date": "2021-10-05", "office": "Austin"}
{"name": "Nina Kowalski", "email": "nina.kowalski@example.com", "department": "Marketing", "role": "SEO Specialist", "salary": 88000, "start_date": "2022-08-15", "office": "New York"}
{"name": "Daniel Ochoa", "email": "daniel.ochoa@example.com", "department": "Marketing", "role": "Marketing Analyst", "salary": 91000, "start_date": "2023-05-20", "office": "Austin"}
{"name": "Samira Hassan", "email": "samira.hassan@example.com", "department": "Marketing", "role": "Social Media Manager", "salary": 82000, "start_date": "2024-01-08", "office": "London"}
{"name": "Tyler Washington", "email": "tyler.washington@example.com", "department": "Sales", "role": "Sales Director", "salary": 158000, "start_date": "2019-11-30", "office": "New York"}
{"name": "Jessica Huang", "email": "jessica.huang@example.com", "department": "Sales", "role": "Account Executive", "salary": 110000, "start_date": "2020-06-14", "office": "San Francisco"}
{"name": "Ryan O'Brien", "email": "ryan.obrien@example.com", "department": "Sales", "role": "Account Executive", "salary": 105000, "start_date": "2021-03-22", "office": "London"}
{"name": "Fatima Al-Rashid", "email": "fatima.alrashid@example.com", "department": "Sales", "role": "Sales Development Rep", "salary": 72000, "start_date": "2023-09-01", "office": "Austin"}
{"name": "Kevin Dupont", "email": "kevin.dupont@example.com", "department": "Sales", "role": "Solutions Engineer", "salary": 135000, "start_date": "2022-01-17", "office": "San Francisco"}
{"name": "Megan Stewart", "email": "megan.stewart@example.com", "department": "Sales", "role": "Account Manager", "salary": 98000, "start_date": "2024-03-11", "office": "New York"}
{"name": "Laura Chen", "email": "laura.chen@example.com", "department": "HR", "role": "HR Director", "salary": 148000, "start_date": "2019-08-05", "office": "New York"}
{"name": "Brian Nakamura", "email": "brian.nakamura@example.com", "department": "HR", "role": "HR Business Partner", "salary": 105000, "start_date": "2020-12-01", "office": "San Francisco"}
{"name": "Chloe Dubois", "email": "chloe.dubois@example.com", "department": "HR", "role": "Recruiter", "salary": 78000, "start_date": "2022-05-23", "office": "London"}
{"name": "Angela Moretti", "email": "angela.moretti@example.com", "department": "HR", "role": "People Operations", "salary": 85000, "start_date": "2023-07-10", "office": "Austin"}
{"name": "Isaac Fernandez", "email": "isaac.fernandez@example.com", "department": "Engineering", "role": "Frontend Engineer", "salary": 128000, "start_date": "2022-09-19", "office": "New York"}
{"name": "Sarah Mitchell", "email": "sarah.mitchell@example.com", "department": "Design", "role": "Design Systems Lead", "salary": 138000, "start_date": "2020-04-06", "office": "San Francisco"}
{"name": "Omar Farah", "email": "omar.farah@example.com", "department": "Engineering", "role": "QA Engineer", "salary": 95000, "start_date": "2024-02-12", "office": "London"}
{"name": "Natalie Park", "email": "natalie.park@example.com", "department": "Marketing", "role": "Growth Manager", "salary": 108000, "start_date": "2021-11-28", "office": "San Francisco"}

Schema

Field Type Description
name string Employee full name.
email string Work email address on the fictional example.com domain.
department string One of Engineering, Sales, Marketing, HR, or Design.
role string Job title within the department.
salary number Annual salary in USD, stored as a JSON number (integer).
start_date date Hire date as an ISO 8601 string (YYYY-MM-DD).
office string Office location — San Francisco, New York, Austin, or London.

About the JSONL Format

This sample holds 30 employee records as JSON Lines: each line is a complete, self-contained JSON object, and lines are separated by \n. There is no enclosing array, no commas between records, and no pretty-printing — compactness and independence per line are the whole point.

That one structural decision is what makes JSONL the default format for logs, data exports, and ML training sets:

  • Streamable. A consumer parses line by line with constant memory, regardless of file size. Nothing needs to buffer the whole document the way parsing a JSON array does.
  • Appendable. Adding record 31 is a file append — no seeking back to move a closing ].
  • Splittable and greppable. head -5, grep London, wc -l, and split all operate correctly on record boundaries because record boundaries are line boundaries. Try wc -l on this file: 30 lines, 30 records.
  • Failure-isolated. A corrupt line breaks one record, not the file. Robust readers skip it and continue.

The records themselves are identical to the JSON sample in this section — same seven keys, salary as a number, start_date as an ISO 8601 string — so the two files are a matched pair for testing that your code handles both container conventions. The strict rules of the format: UTF-8 encoding, one JSON value per line, and no literal newlines inside a record (embedded newlines must be escaped as \n within JSON strings, which keeps the line-per-record invariant true).

All eleven formats in this section express the same 30 employee rows. To flatten this file into a table, use JSONL to CSV; CSV to JSONL goes the other way, emitting one object per row.

FAQ

4 questions
Does downloading this file send any data to a server?
No. The sample is a static file served directly by the site — nothing is uploaded, processed, or inspected. The JSONL and CSV tools linked below run entirely in your browser too.
What's the difference between JSONL and a JSON array?
A JSON array is one document that must be parsed whole; JSONL is one independent JSON document per line. That means JSONL can be streamed, appended to, split, and processed with line tools like grep and head — but the file as a whole is not valid JSON, so JSON.parse on the full text will fail by design.
Is JSONL the same as NDJSON?
Effectively yes. JSON Lines and NDJSON (newline-delimited JSON) describe the same convention: UTF-8 text, one JSON value per line, separated by \n. Tools and file extensions (.jsonl, .ndjson) are interchangeable in practice.
Is the employee data real?
No. All 30 records are fictional, and every email address uses the reserved example.com domain, so the file is safe for demos, tests, and documentation.

Work With JSONL Files 5 tools

JSONL to CSV

Convert JSONL (NDJSON / JSON Lines) data into CSV format

CSV to JSONL

Convert CSV rows into JSONL — one JSON record per line, also known as NDJSON, JSON Lines, or newline-delimited JSON

JSON to CSV

Convert JSON arrays to downloadable CSV

CSV to JSON

Convert CSV files to clean, structured JSON

CSV Converter

Every CSV conversion tool on the site, searchable in one place.