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Start by setting up the AWS Command Line Interface (CLI) on your local machine. Use the AWS CLI to extract data from your DynamoDB table. You can do this by executing a scan operation, which retrieves all items from the table. Save the output in a JSON format for easy manipulation. The command might look like this:
```bash
aws dynamodb scan --table-name YourDynamoDBTableName --region YourRegion > dynamodb_data.json
```
Once you have your JSON file, parse and transform the data to match the schema of the destination TiDB database. This involves extracting relevant fields and restructuring them as needed. You may use a scripting language like Python or JavaScript to automate this process. Ensure data types are compatible with TiDB's requirements.
Install and configure TiDB on your target environment if not already set up. Ensure that your TiDB server is running and accessible. You may need to configure user permissions and network settings to allow data insertion.
Before importing data, create tables in TiDB that correspond to the tables in DynamoDB. Use the transformed schema from step 2 to define the structure. You can do this by executing SQL `CREATE TABLE` statements on your TiDB instance.
Convert the transformed JSON data into SQL `INSERT` statements. You can write a script to iterate over each item in the JSON file and generate corresponding SQL commands. Ensure that each SQL statement accurately reflects the data types and constraints of your TiDB tables.
With your SQL `INSERT` statements ready, execute them on the TiDB database. You can use a database client like MySQL CLI or a programmatic approach using a script that connects to TiDB and executes the statements. Monitor for any errors and ensure all data is inserted successfully.
After the data has been inserted into TiDB, perform a thorough check to ensure data integrity and consistency. Compare row counts and sample records between DynamoDB and TiDB to confirm that the migration was successful. Run queries to test data retrieval and ensure everything is functioning as expected.
By following these steps, you can manually transfer data from DynamoDB to TiDB 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.
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:
