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https://github.com/vaticle/typedb-driver-examples

TypeDB Driver Example Projects and Tutorials
https://github.com/vaticle/typedb-driver-examples

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TypeDB Driver Example Projects and Tutorials

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# TypeDB Driver Examples



[![Factory](https://factory.vaticle.com/api/status/vaticle/typedb-driver-examples/badge.svg)](https://factory.vaticle.com/vaticle/typedb-driver-examples)



## Table of contents



- [Examples in this repository](#examples-in-this-repository)

- [Data visualisation](#data-visualisation)

- [Additional examples](#additional-examples)



## Examples in this repository



This repository includes examples that showcase usage of TypeDB Clients in reading from and writing to a TypeDB 

database.



### [Biology: Catalogue of Life](biology/catalogue_of_life)



[Catalogue of Life](https://www.catalogueoflife.org/) is a database of over 4.5 million currently known taxa in biology,

compiled from over a hundred different sources. The example showcases simple data preparation and a sample configuration

file for loading a large taxonomic dataset using [TypeDB Loader.](https://github.com/typedb-osi/typedb-loader)



### [Commerce: Bookstore](commerce/bookstore)



The bookstore example uses Python to showcase migration of data into TypeDB and executing queries on this data.



The data of the Books, Users and Orders loaded from the `.csv` files in the [data](commerce/bookstore/python/data) 

directory.



Read the [README](commerce/bookstore/README.md) file for instructions. Check [the schema](commerce/bookstore/schema.tql)

or the initial [dataset](commerce/bookstore/python/data) for additional information. All logic accessible in the script

files in the [python](commerce/bookstore/python) directory.



### [Finance: Fraud Detection](finance/fraud_detection)



The Fraud Detection example uses Quarkus, the Supersonic Subatomic Java Framework in order to let us present results with GraphQL.

In this project, we use TypeDB to represent a queryable database of relevant fraud detection data about some transactions between Cardholders and Companies.



### [Gaming: XCOM Project](gaming/xcom)



The XCOM 2 example contains a database of interdependent research tasks in the game XCOM 2, featuring automatic

inference of available research based on completed tasks and available items. See [the schema](gaming/xcom/schema.tql)

for the examples of reasoner rules inferring attributes.



### [Software: GitHub](software/github)



The GitHub example showcases migration of heavily interconnected data from a live repository on GitHub or from a Vaticle

GitHub snapshot, and provides a visual interface to explore some sample queries.



### [Telecom: Phone Calls](telecom/phone_calls)



TypeDB officially supports clients for Java, Node.js and Python. Learn more about [TypeDB Clients](http://docs.vaticle.com/docs/client-api/overview).



The Phone Calls example showcases basic migration of a database of customers of a fictional telecom company and calls

they make in various formats (CSV, JSON, and XML) and expressive TypeQL queries using

the [Java,](telecom/phone_calls/java) [Node.js,](telecom/phone_calls/nodejs) and [Python](telecom/phone_calls/python)

clients.



#### Data migration



- Java: [CSV](telecom/phone_calls/java/CSVMigration.java) | [JSON](telecom/phone_calls/java/JSONMigration.java)

  | [XML](telecom/phone_calls/java/XMLMigration.java)

- Node.js: [CSV](telecom/phone_calls/nodejs/migrateCsv.js) | [JSON](telecom/phone_calls/nodejs/migrateJson.js)

  | [XML](telecom/phone_calls/nodejs/migrateXml.js)

- Python: [CSV](telecom/phone_calls/python/migrate_csv.py) | [JSON](telecom/phone_calls/python/migrate_json.py)

  | [XML](telecom/phone_calls/python/migrate_xml.py)



#### Query examples



- [Java](telecom/phone_calls/java/Queries.java)

- [Node.js](telecom/phone_calls/nodejs/queries.js)

- [Python](telecom/phone_calls/python/queries.py)



## Data visualisation



After loading the data for any of the examples, you can use

[TypeDB Studio](https://github.com/vaticle/typedb-studio/releases) to explore the graph structure of the database.



## Additional examples



There are some examples in other repositories that are recommended for more advanced users.



### TypeDB Bio



[TypeDB Bio](https://github.com/vaticle/typedb-bio) is a collection of knowledge graphs of biomedical data.



TypeDB Bio provides an intuitive way to query interconnected and heterogeneous biomedical data in one single place. The

schema that models the underlying knowledge graph alongside the descriptive query language, TypeQL, makes writing

complex queries an extremely straightforward and intuitive process. Furthermore, the automated reasoning capability of

TypeDB, allows TypeDB Bio to become an intelligent database of biomedical data that infers implicit knowledge based on

the explicitly stored data. TypeDB Bio can understand biological facts, infer based on new findings and enforce research

constraints, all at query (run) time.



### TypeDB CTI



[TypeDB CTI](https://github.com/typedb-osi/typedb-cti) is an open source threat intelligence platform for organisations

to store and manage their cyber threat intelligence (CTI) knowledge. It enables threat intel professionals to bring

together their disparate CTI information into one database and find new insights about cyber threats.