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Begin by cloning the GitHub repository that contains your data. This can be done using the Git command line tool. Open your terminal, navigate to the directory where you want to clone the repository, and run `git clone `. This step ensures you have a local copy of the data files.
Download and install Typesense on your local machine. Visit the [Typesense installation guide](https://typesense.org/docs/guide/install-typesense.html) for detailed instructions. You can choose between using a Docker image or installing it directly on your system depending on your preference.
Examine the data files from your cloned GitHub repository and convert them into a format compatible with Typesense. Typesense supports JSON and CSV formats. Ensure each data entry is structured with fields that match the schema you plan to use in Typesense.
Create a schema for your Typesense collection. This schema defines how the data will be structured, including fields, data types, and any indexes. You can define this schema in a JSON file or directly in a script. Ensure the fields in your schema correspond with the fields in your prepared data.
Launch the Typesense server using the command `typesense-server --data-dir /path/to/data` in your terminal. This starts the Typesense server, making it ready to accept data. Make sure the server is running before proceeding to the next step.
Use the Typesense API to create a collection based on the schema you defined. This can be done using a curl command or a simple script. Here is an example using curl:
```
curl -X POST "http://localhost:8108/collections" \
-H "X-TYPESENSE-API-KEY: " \
-H "Content-Type: application/json" \
-d ''
```
Replace `` with your Typesense API key, and `` with your schema JSON.
Finally, load your formatted data into the newly created Typesense collection. This can be done using a script or curl commands. If your data is in JSON format, you can use the following curl command for each data entry:
```
curl -X POST "http://localhost:8108/collections//documents" \
-H "X-TYPESENSE-API-KEY: " \
-H "Content-Type: application/json" \
-d ''
```
Replace `` with the name of your collection, `` with your Typesense API key, and `` with each JSON data entry.
By following these steps, you will have successfully moved your data from GitHub to Typesense without using any third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
GitHub is a renowned and respected development platform that provides code hosting services to developers for building software for both open source and private projects. It is a heavily trafficked platform where users can store and share code repositories and obtain support, advice, and help from known and unknown contributors. Three features in particular—pull request, fork, and merge—have made GitHub a powerful ally for developers and earned it a place as a (developers’) household name.
GitHub's API provides access to a wide range of data related to repositories, users, organizations, and more. Some of the categories of data that can be accessed through the API include:
- Repositories: Information about repositories, including their name, description, owner, collaborators, issues, pull requests, and more.
- Users: Information about users, including their username, email address, name, location, followers, following, organizations, and more.
- Organizations: Information about organizations, including their name, description, members, repositories, teams, and more.
- Commits: Information about commits, including their SHA, author, committer, message, date, and more.
- Issues: Information about issues, including their title, description, labels, assignees, comments, and more.
- Pull requests: Information about pull requests, including their title, description, status, reviewers, comments, and more.
- Events: Information about events, including their type, actor, repository, date, and more.
Overall, the GitHub API provides a wealth of data that can be used to build powerful applications and tools for developers, businesses, and individuals.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
