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Begin by exporting your data from Gridly. Navigate to the project or grid you want to export, and use Gridly's built-in export functionality. Choose a format like CSV or JSON as these are easily manageable for manual data handling and compatible with Typesense.
Set up your local environment to handle the data processing. Install necessary tools like a text editor for JSON/CSV files and ensure you have Python installed, as it will be helpful for data manipulation.
Open the exported file and verify the data structure. Clean the data by ensuring fields are correctly formatted and no extraneous data is present. Ensure consistency in field names and data types, as this will be critical for smooth indexing in Typesense.
Set up a local or remote instance of Typesense. Download and install Typesense according to your operating system's requirements. Once installed, configure the server by setting up the necessary keys and authentication for data import.
Use a script, preferably in Python, to transform your cleaned data into a format compatible with Typesense. This involves creating a JSON schema that matches the Typesense index schema, including fields like `name`, `type`, and `facet`.
Before importing data, create a collection in Typesense. Use the Typesense API to define your collection schema, specifying the fields and their types, which should match the transformed data structure from the previous step.
With the collection ready, import the transformed data into Typesense using the Typesense API. Write a script to read the transformed data file and use batch operations to index the data into the created collection. Verify that the data import is successful by querying the Typesense API to check the indexed records.
By following these steps, you should be able to move your data from Gridly to Typesense efficiently without relying on 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.
Gridly is a cloud-based headless CMS for multilingual game-as-a-service projects with an open API, browser-based spreadsheet UI, and built-in functions to handle localization and frequent updates. It is a collaborative system for users of any technical ability. Gridly is spreadsheet for multi-language content tailor-made for games and digital products. By connecting development, design, and localization teams and their tools, Gridly serves as a single source of truth for faster content updates. Gridly improves collaboration and streamlines content management and localization for your games or apps.
Gridly's API provides access to various types of data that can be used to manage and organize content for web and mobile applications. The following are the categories of data that Gridly's API gives access to:
1. Content data: This includes all the content that is stored in Gridly, such as text, images, videos, and audio files.
2. Metadata: This includes information about the content, such as the date it was created, the author, and any tags or categories associated with it.
3. User data: This includes information about the users who access the content, such as their login credentials, preferences, and activity history.
4. Analytics data: This includes data about how users interact with the content, such as page views, clicks, and engagement metrics.
5. Configuration data: This includes settings and configurations for the application, such as user permissions, access controls, and integration with other systems.
Overall, Gridly's API provides a comprehensive set of data that can be used to build and manage content-rich applications.
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?
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