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Before beginning the data migration process, thoroughly understand the data schema and structure in Convex.dev. Identify all relevant tables, fields, and data types. This understanding will help in accurately mapping the data to the Apache Iceberg format.
Use Convex.dev’s built-in features or APIs to extract data. This can typically be done by writing custom scripts or using command-line tools to export the data. Ensure data is exported in a common format like CSV, JSON, or Parquet, which can be easily processed further.
Once the data is extracted, transform it to match the schema expected by Apache Iceberg. This involves data type conversions, reformatting dates and timestamps, and ensuring that the data adheres to any constraints required by the target schema in Iceberg.
Prepare the Apache Iceberg environment to receive the data. Install and configure Apache Iceberg on your data storage system, ensuring it is properly integrated with your query engine (e.g., Apache Spark, Presto, or Hive). Define the schema and partitioning strategy for the tables you plan to create in Iceberg.
Write scripts or use command-line tools to load the transformed data into Apache Iceberg. This usually involves using a data processing engine like Apache Spark to read the transformed data files and write them into Iceberg tables. Ensure data consistency and validate that all records have been transferred correctly.
After loading the data, perform a thorough data integrity and consistency check. Query the Iceberg tables to ensure that the data is complete and accurately reflects the original dataset from Convex.dev. Compare record counts, field values, and metadata to confirm successful migration.
Once data integrity is confirmed, optimize the Iceberg tables for performance by compacting small files and optimizing data layout. Implement security measures such as access controls and encryption to protect the data within Iceberg, ensuring compliance with your organization’s data governance policies.
By following these steps, you can manually move data from Convex.dev to Apache Iceberg, ensuring a successful migration 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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