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Ensure that the AWS Command Line Interface (CLI) is installed and configured on your machine. This tool will allow you to interact with AWS services, including DynamoDB, from your command line. Use the command `aws configure` to set up your access key, secret key, region, and output format.
Use the AWS CLI to scan the entire source table and export the data to a JSON file. The command looks like this:
```
aws dynamodb scan --table-name SourceTableName --output json > source_data.json
```
This command retrieves all items from the source table and stores them in a JSON file named `source_data.json`.
If necessary, modify the JSON file to match the data schema of the target table. Ensure that the attribute names and data types conform to what is expected by the target table. You can use any text editor or script to make these changes.
If the target table does not already exist, create it using the AWS CLI. Ensure that its key schema, attribute definitions, and provisioned throughput match your requirements:
```
aws dynamodb create-table --table-name TargetTableName --attribute-definitions AttributeName=DataType --key-schema AttributeName=KeyType --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=5
```
Replace the placeholders with your specific attribute names, data types, and key types (HASH or RANGE).
Use a scripting language like Python to read the data from the JSON file and insert it into the target table using the `batch-write-item` operation. This operation allows you to insert multiple items at once, efficiently handling larger datasets.
```python
import boto3
import json
# Initialize a session using Amazon DynamoDB
dynamodb = boto3.resource('dynamodb', region_name='your-region')
# Load data from JSON file
with open('source_data.json') as json_file:
items = json.load(json_file)['Items']
# Specify the target table
table = dynamodb.Table('TargetTableName')
# Batch write items
with table.batch_writer() as batch:
for item in items:
batch.put_item(Item=item)
```
Make sure to replace `'your-region'` and `'TargetTableName'` with your specific values.
Execute the batch import script you wrote in the previous step. This will read the data from the JSON file and write it to the target DynamoDB table. Monitor the process to ensure that all items are transferred successfully.
After the data transfer is complete, verify that the data in the target table matches the data in the source table. You can perform a scan operation on the target table to check for consistency:
```
aws dynamodb scan --table-name TargetTableName
```
Compare the results to ensure the data integrity and completeness of the transfer.
By following these steps, you can efficiently move data from one DynamoDB table to another using AWS native tools 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: