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Begin by setting up the AWS Command Line Interface (CLI) on your local machine. This will allow you to interact with your DynamoDB tables. Ensure you have the necessary AWS credentials and permissions to access and read data from the DynamoDB tables. Configure the AWS CLI using the command `aws configure`, and input your AWS Access Key, Secret Key, region, and output format.
Use the AWS CLI or a script to export data from your DynamoDB table into a JSON file. You can achieve this by using the `scan` operation, which reads all the items in your table. Run a command like `aws dynamodb scan --table-name YourTableName --output json > dynamodb_data.json` to export data to a JSON file. Note that for large tables, pagination might be required, which can be handled in your script by using the `LastEvaluatedKey` feature to fetch data in segments.
Examine the JSON file to ensure it contains the correct data structure you intend to import into Convex. Make any necessary adjustments to the data format to match the requirements of Convex. This might involve transforming attribute names, data types, or nesting structures to align with your Convex schema.
If you haven't already, set up your Convex environment. This involves creating an account on the Convex platform and setting up a new project. Familiarize yourself with the Convex API and data model to understand how data is structured and accessed within Convex.
Develop a custom script in a language like JavaScript or Python to read the JSON file and use the Convex API to import data into your Convex database. Your script should iterate through each item in the JSON file and make an API call to insert the data into Convex. Ensure the data fields in your JSON match the Convex schema to prevent import errors.
Run your script to import the data from the JSON file into Convex. Monitor the process closely to catch any errors or issues that arise during the import. Consider implementing logging within your script to track the progress and identify any records that fail to import.
Once the import is complete, verify the data integrity within Convex. Check that all records have been imported correctly and that the data structure and content are as expected. You can do this by querying the Convex database and comparing the results with your original DynamoDB data to ensure consistency and accuracy.
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.
Amazon DynamoDB is a fully managed proprietary NoSQL database service that supports key–value and document data structures and is offered by Amazon.com as part of the Amazon Web Services portfolio. DynamoDB exposes a similar data model to and derives its name from Dynamo, but has a different underlying implementation.
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:
