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Begin by ensuring your Convex.dev environment is properly set up. Log into your Convex.dev account and navigate to the database section where your data is stored. Familiarize yourself with the data structures and the API endpoints that provide access to the data you intend to migrate.
Use the Convex.dev API to export your data. Typically, this involves sending HTTP GET requests to the relevant API endpoints to fetch data in a JSON format. Make sure to authenticate your requests if necessary and handle pagination if your dataset is large.
Convert the JSON data exported from Convex.dev into CSV format. This can be done using a scripting language like Python. Use a script to iterate through the JSON objects and write the data into CSV files. Libraries such as `json` and `csv` in Python can be used to simplify this process.
Download and install DuckDB on your local system. DuckDB is available for various operating systems, and you can follow the installation instructions on the official DuckDB website to set it up. Ensure that DuckDB is correctly installed by running a few basic queries in its interactive shell.
Launch DuckDB and create a new database to store your data. Define the schema of your tables to match the structure of the CSV files. This involves creating tables with the appropriate columns and data types that correspond to the data you're migrating.
Use DuckDB's `COPY` command to import your CSV data into the newly created tables. This can be done directly from the DuckDB command line interface. Specify the path to the CSV files and ensure that the data types in the CSV match those specified in the DuckDB schema.
Once the data import is complete, run queries to verify that the data in DuckDB matches the original data in Convex.dev. Check for consistency in record counts, data types, and content to ensure that the migration process was successful and the data integrity is maintained.
By following these steps, you can effectively move data from Convex.dev to DuckDB without relying on 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.
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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