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SQLified is a browser-based tool that converts CSV, TSV, and other delimited data files into clean, import-ready SQL —CREATE TABLE plus batched INSERT statements — for PostgreSQL, MySQL, SQLite, and SQL Server.
Unlike free one-off converters that choke around 100K rows, SQLified is built for production-scale loads: it reliably handles files of 1,000,000+ rows. It does smart type inference (INT/BIGINT, NUMERIC scale, dates, booleans, currency), lets you override any column's type, primary key, and nullability, and emits correctly chunked INSERT batches per dialect (including SQL Server's 1000-row limit and MySQL packet limits) so the output imports cleanly the first time.
Free to use for everyday conversions; Pro unlocks the largest files, batched output tuning, and an ad-free experience. A product of Octet Software.
DrawSQL is a simple, beautiful database diagram editor for developers to 🚧 create, 💬 collaborate and 👀 visualize their entity relationship diagrams.
SQLified
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SQLified's answer
Our ability to infer data types, and produce massive insert statements quickly across multiple dialects.
SQLified's answer
After using many online tools and being disappointed, I realized there was a need for a file that can create clean, type aware sql for bulk inserts. The product saves me tons of time every month dealing with large data sets in the payment industry and has reduced frustrations in dealing with them.
SQLified's answer
There are numerous competitors, but none that do what SQLified does well: this is type inference, and creating runnable SQL script for extremely large inserts in multiple dialects.
SQLified's answer
SQLified is designed for the solo developer, or the analytical employee dealing with large data sets, csv, or delimited files every day, and struggling with bulk insert. I want to eliminate that struggle so the real work can be done.
SQLified's answer
Our tools are simple, effective, and require little processing power. We store no data; all work is "ephemeral" and done on the users machine. We do not track, store, or maintain any datasets whatsoever in regards to whatever is converted on the site. We are also not trying to be something we are not. We do what we do, which is flat file to SQL dialect conversion, and we do it well.
Based on our record, DrawSQL seems to be more popular. It has been mentiond 12 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
With this, I went for designing the db. I went to http://drawsql.app/ and created my first draft. Then exported the DDL and did a bit of back and forth with AI. This is the final draft of the database:. - Source: dev.to / 10 months ago
So I started designing the DB using this cool tool. The project has 2 tables, users and categories . The user can create many categories as he wants so the first approach I took was creating a third table, a union table to store user_id and category_id. With this solution the users are able to create x numbers of categories and we can see assign the category to the user. - Source: dev.to / over 1 year ago
Once you have generated the SQL code, you can convert it into a relational schema (the graphical table model) using DrawSQL. This tool offers:. - Source: dev.to / over 1 year ago
DrawSQL makes it easy for teams to collaborate on creating and maintaining schema diagrams. With a single source of truth, there's no need for manually syncing diagram files between different developers and offline tools anymore. Source: about 3 years ago
To be honest, since you are just getting started, I think you should reconsider simplifying this app to begin with. Built something easier and get some more experience before jumping in the ocean. Maybe start by focusing only on the parent company and sub-companies. However, I strongly recommend you to try and make a diagram of your database with relations and columns as it can you a lot of time. I personally use... Source: about 3 years ago
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