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Install and configure the Google Cloud SDK on your local machine to interact with Google Cloud Storage. You can download it from the official Google Cloud website and follow the setup instructions to authenticate and configure it with your Google Cloud account.
Use the `gsutil` command-line tool, part of the Google Cloud SDK, to download data from your Google Cloud Storage bucket to your local machine. For example, use the command `gsutil cp gs://your-bucket-name/data-file.json /local/path/` to copy a JSON file from the bucket to a local directory.
Set up a Typesense server on your local machine or a server you manage. You can download the latest release from Typesense’s official GitHub repository. Follow the installation instructions provided to get the server running. Ensure you have the necessary dependencies, like `curl`, to interact with the Typesense API.
Once Typesense is installed, configure it by editing the `typesense-server-config.json` file. Set the appropriate port and API key settings. Start the Typesense server using the command `./typesense-server --config=typesense-server-config.json`.
Ensure that your data is in the correct format for Typesense. Typesense requires data in JSON format, with each document containing fields that match the schema you plan to use. Validate and, if necessary, transform your data file to fit the schema requirements of your Typesense collection.
Use the Typesense API to create a collection. You can achieve this by sending a POST request to the Typesense server with your schema definition. Use a command-line tool like `curl` or a simple script in a language like Python to define the collection schema, specifying fields and their types.
Insert the data into the newly created Typesense collection by making API calls. You can write a script in Python or use `curl` to send batch or individual POST requests with your data. Ensure that your requests are authenticated using the API key configured earlier. Confirm successful data insertion by querying the collection.
This guide should allow you to move data from Google Cloud Storage to Typesense 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.
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.
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