1. AWS Account: Ensure you have access to an AWS account with permissions to access DynamoDB and Elasticsearch Service.
2. Elasticsearch Cluster: Set up an Elasticsearch cluster in AWS Elasticsearch Service or on your own servers.
3. Development Environment: Set up a development environment with AWS SDK (for the language of your choice), and Elasticsearch client libraries.

1. Choose a Method for Data Extraction:
   • DynamoDB Scan: Use the Scan operation to retrieve all items from the table.
   • DynamoDB Streams: If your data is continuously changing, consider using DynamoDB Streams to capture changes in real-time.
2. Write a Script to Extract Data:
   • Initialize the AWS SDK with your credentials and set the region.
   • Use the Scan API to retrieve the data from the DynamoDB table.
   • Paginate through the results if your dataset is larger than the maximum items returned in a single scan.

1. Data Mapping: Map the DynamoDB data types to the corresponding Elasticsearch data types.
2. Data Transformation: Write a script or a function to transform the data into JSON documents that are compatible with Elasticsearch.

1. Initialize Elasticsearch Client: Set up the Elasticsearch client using the necessary credentials and endpoint of your Elasticsearch cluster.
2. Create an Index: If not already created, define an index in Elasticsearch where the data will be stored.
3. Bulk Indexing: Use the Elasticsearch Bulk API to index multiple documents at once for better performance.

1. Combine the Steps: Combine the extraction, transformation, and loading steps into a single script or application.
2. Error Handling: Implement error handling to manage issues like network interruptions, throttling, or indexing errors.
3. Logging: Add logging to your application to track the progress and any issues that arise during the data transfer process.

1. Test with a Small Dataset: Before transferring all your data, test the process with a small subset to ensure everything works as expected.
2. Run the Data Transfer: Execute your script or application to transfer the data from DynamoDB to Elasticsearch.
3. Monitor the Process: Monitor the data transfer process for any errors or performance bottlenecks.

1. Check Data Count: Ensure the number of documents in Elasticsearch matches the number of items in DynamoDB.
2. Sample Data Verification: Query several items in Elasticsearch and compare them with the source data in DynamoDB to verify accuracy.

1. Automation: If your data needs to be updated regularly, consider automating the process with AWS Lambda functions triggered by DynamoDB Streams or scheduled events.

1. Remove Temporary Resources: If you created any temporary resources (like EC2 instances) for the data transfer, terminate them to avoid unnecessary charges.

Additional Considerations

• Security: Ensure that all data transfers are done securely, using encryption in transit and at rest.
• Throttling: Be mindful of read and write throughput limits on both DynamoDB and Elasticsearch to avoid throttling.
• Cost: Consider the cost implications of data transfer and storage in both services.