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How to Quickly Convert to Convert Insert SQL to JSONLines Format Online Format?

1. Upload, paste, or extract Insert SQL from web pages with multiple input methods supported

Paste INSERT SQL statements or upload .sql files. The tool intelligently parses SQL syntax and extracts table data, supporting multiple SQL dialects and complex query statement processing.

2. Modify Insert SQL using our professional online table editor

Edit data using our professional online table editor. Supports deleting empty row data, removing duplicate rows, transposing data, sorting by rows, regex find & replace, and real-time preview. All changes will automatically convert to JSONLines Format format with simple and efficient operation and precise reliable results.

3. Copy or download JSONLines Format with multiple export options supported

Generate standard JSONLines format with each line outputting a complete JSON object. Suitable for streaming processing, batch import, and big data analysis scenarios, supporting data validation and format optimization.

Note: Our online conversion tool uses advanced data processing technology, runs completely in the browser, ensures data security and privacy, and does not store any user data.

What is SQL format and its application scenarios?

.sql

SQL (Structured Query Language) is the standard operation language for relational databases, used for data query, insert, update, and delete operations. As the core technology of database management, SQL is widely used in data analysis, business intelligence, ETL processing, and data warehouse construction. It's an essential skill tool for data professionals.

What is JSONLines format and its application scenarios?

.jsonl .json .jsonline

JSON Lines (also known as NDJSON) is an important format for big data processing and streaming data transmission, with each line containing an independent JSON object. Widely used in log analysis, data stream processing, machine learning, and distributed systems. Supports incremental processing and parallel computing, making it the ideal choice for handling large-scale structured data.

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