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First, ensure that you have the AWS Command Line Interface (CLI) installed and configured on your system. Use the `aws configure` command to set up your AWS credentials (Access Key, Secret Key) and region. Verify your setup by listing tables with `aws dynamodb list-tables`.
Use the AWS CLI to scan and export data from your DynamoDB table. Execute a command like:
```
aws dynamodb scan --table-name YourDynamoDBTableName --output json > dynamodb_data.json
```
This command will export the entire table content into a JSON file. For large datasets, consider exporting in segments or using the `--limit` parameter to handle data in batches.
The data extracted from DynamoDB is in JSON format, which might not directly align with the schema expected by Starburst Galaxy. Use a scripting language like Python to transform this JSON data into a CSV or another structured format. Python's `pandas` library can be particularly useful for this transformation.
If you haven’t already, create a Starburst Galaxy account and set up your workspace. Ensure that you have the necessary permissions and a compatible environment to load data. Configure any required schemas or catalogs within Starburst Galaxy to match the structure of your transformed data.
Use Starburst Galaxy’s built-in capabilities to load data. If your data is in CSV format, you can use SQL commands within Starburst Galaxy to load this data. An example SQL command might look like:
```sql
LOAD DATA LOCAL INFILE 'transformed_data.csv'
INTO TABLE YourTargetTable
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '\n';
```
Ensure that the table schema in Starburst Galaxy matches the structure of your CSV file.
After loading the data into Starburst Galaxy, perform validation to ensure data integrity and correctness. Run SQL queries to compare sample records, check data types, and confirm that no data is missing or misaligned compared to the source.
For ongoing synchronization between DynamoDB and Starburst Galaxy, consider writing a script or developing a small application to automate this ETL process. Use your system’s task scheduler (like cron jobs on Unix-based systems) to run this script at regular intervals, ensuring that your data remains up-to-date.
By following these steps, you can manually move data from DynamoDB to Starburst Galaxy without relying on third-party connectors, while ensuring flexibility and control over the data transfer process.
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:
