GitHub search tool 2023-01-12
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Data Explorer

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Explored GitHub events w/ NL queries & SQL.
Generated by ChatGPT

Data Explorer is an AI-powered tool that allows users to easily and quickly explore GitHub event data. It is built with Chat2Query, an AI-powered SQL generator in TiDB Cloud, and uses GH Archive to collect and archive data since 2011.

Data Explorer enables users to ask questions in natural language and automatically generate SQL queries. The query results are then displayed visually, helping users to discover insights in the data quickly.Data Explorer can be used to explore any dataset, not just GitHub data.

It is capable of handling complex analytical queries, and is optimized to store and serve massive amounts of data. Users can input their question in natural language and Data Explorer will generate the relevant SQL query and output the results visually.

It can also suggest popular questions near the search box to help users explore faster.Data Explorer has limitations, including a lack of context and domain knowledge, and an inability to produce the most efficient SQL statement for large and complex queries.

To help the AI understand your query intention, it is recommended to use clear, specific phrases in your question. Users can also use query templates near the search box to start their exploration.

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Data Explorer was manually vetted by our editorial team and was first featured on January 18th 2023.
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Pros and Cons

Pros

Explore GitHub event data
Built with Chat2Query
Utilizes GH Archive
Natural language SQL generation
Visual query results
Handles complex analytical queries
Optimized for massive data
Question suggestions for faster exploration
Query templates for initial exploration
Capable of exploring any dataset
Optimized for large-volume data
Real-time data updates
Offers pay-as-you-go pricing model
Can analyze up to 5 billion events
Interactive query in 2D and 3D
Live data updates
Query results showcased visually
Supports trend analysis
Comparison between different datasets
Frequent query suggestions
Quick discovery of insights
No SQL knowledge required
Custom dataset import
Historical data from 2011
Database stats tracking
Possible to report issues
Supports complex queries
Shows data in ranking format
Shows language related trends
Handles multiple workload types

Cons

Lack of context understanding
No domain knowledge
Inefficient SQL generation
Sometimes service instability
Limited by dataset
No results in misinterpreted questions
Incorrect SQL queries
Chart generation failure
Unable to handle excessive requests
Limited to 15 questions per hour

Q&A

What is Data Explorer?
How does Data Explorer work?
Is Data Explorer limited to only GitHub data?
Can I use Data Explorer with my own dataset?
What kind of questions can I ask with Data Explorer?
How can Data Explorer help a normal user explore GitHub event data?
What are some limitations of Data Explorer?
What is special about the SQL generation in Data Explorer?
Why are the query results visually displayed in Data Explorer?
What is the role of Chat2Query in Data Explorer?
How is TiDB Cloud used in Data Explorer?
What type of data does Data Explorer use?
Does Data Explorer suggest popular questions to users for exploring faster?
Does Data Explorer only work with structured questions?
How does Data Explorer handle complex analytical queries?
Is Data Explorer optimized for large amounts of data?
What if the AI doesn't understand my question in Data Explorer?
What is the relationship between Data Explorer and TiDB Cloud?
Why can't Data Explorer produce the most efficient SQL statements for large queries?
How does Data Explorer use GitHub API and GH Archive data?

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