
SQLizer
Table Format Converter
TableToSQL
OI ConvertCSV
Online Convert
Convertio
ExtendsClass SQL Converter
Convert CSV, TSV & delimited files to SQL — in your browser

Dune Analytics
CoinGecko
Messari
CryptoQuant
Coinglass
TradingView
CoinMarketCap
Blockchain analytics platform to identify rare opportunities
Which is more popular?
Based on our record, Nansen seems to be more popular. It has been mentioned 8 times since March 2021.
Website, pricing, platforms and company facts side by side.
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| Website | getsqlified.com | nansen.ai |
| Pricing | ||
| Company | 2025 | — |
| Listed in |
In their own words, as submitted to SaaSHub.


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...
No description of Nansen yet.
What each product offers, as listed by its team.


Possible disadvantages
Possible disadvantages
An editorial look at what each product does well and who it suits.


Overall verdict
Why this product is good
Recommended for
Overall verdict
Why this product is good
Recommended for
Nansen is recommended for crypto traders, investors, DeFi enthusiasts, hedge funds, analysts, and anyone looking to gain deeper insights into blockchain activities and trends. Novices might find the platform a bit overwhelming, but it is highly beneficial for those who are serious about leveraging blockchain data for strategic decisions.
Walkthroughs and reviews on video.
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Nansen Review: Should NFT Buyers Use It?
More videos
How often each product is chosen within a category, 0–100% relative to the other.


As answered by people managing SQLified and Nansen.
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.
Share your experience with using SQLified and Nansen. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.


Tracking SQLified since Jun 2026.
The whole design rested on one assumption. The data knocked it down in the first afternoon. This post covers what I assumed, what Nansen's labels showed, and which part of the product turned out to do the real work. - Source: dev.to / about 1 month ago
I built Sent Wrong to answer that one question. You paste the address you sent to (and your own, if you like). It runs up to ten calls against Nansen's API and gives you one of four routes, the evidence behind it, and a support ticket... - Source: dev.to / about 1 month ago
I built a tool to answer that. You type a ticker, and it asks Nansen's API for every token with that name across chains. Then it checks who actually holds and trades each one: labelled Smart Money, whales, top-PnL wallets, exchange flow,... - Source: dev.to / about 1 month ago
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