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There I Went VS mlsql

Compare There I Went VS mlsql and see what are their differences

There I Went

See your Google Maps Timeline again: a map, statistics, days per country and a shareable year-in-review card, made in your browser with nothing uploaded.

Rating
0 reviews
Pricing
Free
Promote on VibeVoted
Alternatives Is it good?
mlsql

Infer SQL queries from plain-text questions and table headers.

Rating
0 reviews
Promote on VibeVoted
Alternatives Is it good?
warmup.rocks
warmup.rocks keeps your CDN cache warm in every edge location worldwide: Cloudflare, Fastly, Akamai, CloudFront and more. Warming from 42 countries, hit-ratio analytics. sponsored

Which is more popular?

Based on our record, mlsql seems to be more popular. It has been mentioned 2 times since March 2021.

social mentions
0 vs 2
Maps popularity
100% vs 0%
alternatives listed
8 vs 1

Base details

Website, pricing, platforms and company facts side by side.

There I Went
mlsql
Website thereiwent.com github.com
Pricing
Free
—
Platforms
Web
—
Listed in
Travel & Location Maps Travel Tools Travel Tool Web App Web Mapping +4
Education LMS Online Learning Web App +1

About There I Went and mlsql

In their own words, as submitted to SaaSHub.

There I Went
mlsql

Google took Timeline off the web, so your location history now only lives on your phone, one day at a time. There I Went reads the Timeline export from your phone (Android Timeline.json, iPhone location-history.json, or an old Google Takeout zip) and shows all of it at once. A map of every place...

Read more about There I Went

No description of mlsql yet.

Features and specs

What each product offers, as listed by its team.

There I Went 5 features
mlsql 0 features
  • Nothing uploaded
    Your Timeline file is read in your own browser and never sent anywhere
  • No account needed
    Free, no sign-up
  • Days per country
    Days and nights in each country, with 183-day flags and a Schengen 90/180 check
  • Excel export
    Every visit and journey as an Excel spreadsheet
  • Works offline
    Keeps working with Wi-Fi off, plus a downloadable interactive map file

No features have been listed yet.

Analysis

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

There I Went
mlsql

No analysis of There I Went yet.

Overall verdict

  • MLSQL is a solid open-source unified platform that blends SQL with machine learning, making data engineering and ML workflows accessible through a single, declarative language. It's a good choice for teams looking to streamline big data and ML pipelines without switching between multiple tools.

Why this product is good

  • It unifies data processing and machine learning under a single SQL-like syntax, lowering the learning curve for data teams.
  • Built on top of Apache Spark, it leverages a proven distributed computing engine for handling large-scale data.
  • Open-source and actively developed, allowing customization and community-driven improvements.
  • Supports the full ML lifecycle including data preprocessing, training, and deployment within one workflow.
  • Reduces the need to context-switch between separate ETL, analytics, and ML tools.

Recommended for

  • Data engineers and data scientists who prefer SQL-centric workflows
  • Teams working with big data on Apache Spark
  • Organizations wanting to unify ETL and machine learning pipelines
  • Companies seeking an open-source alternative to fragmented ML tooling
  • Analysts looking to build ML models without deep programming expertise

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
There I Went
mlsql
100% 100%
Maps
0% 0%
0% 0%
LMS
100% 100%
100% 100%
Travel & Location
0% 0%
0% 0%
Education
100% 100%

User comments

Share your experience with using There I Went and mlsql. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

There I Went 0 mentions
mlsql 2 mentions

Tracking There I Went since Oct 2026.

  • Download the files from Docker Container and locally edit them
    I am working on a project that requires me to take user input (as English) and return SQL queries. We will eventually be working with multiple datasets, which is why the valuenet over at https://github.com/paulfitz/mlsql ended up being... Source: almost 5 years ago
  • Clone and edit Docker Projects hosted on Github
    I found this great resource here: https://github.com/paulfitz/mlsql. Source: almost 5 years ago

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