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Begin by ensuring your AWS environment is set up correctly. This involves creating an IAM role with permissions to access DynamoDB and other required AWS resources. Make sure the role can also invoke AWS Lambda functions if you plan to use Lambda for processing the data.
Enable DynamoDB Streams on the table from which you want to move data. DynamoDB Streams capture changes to items in the table, providing a real-time log of these changes. You can choose either New Image, Old Image, Keys Only, or New and Old Images for the stream view type based on your data needs.
Create an AWS Lambda function that will be triggered by the DynamoDB Stream. This function will process the stream records and prepare them for publishing to Google Pub/Sub. Write code within the Lambda function to handle the incoming stream data, transform it if necessary, and prepare it for export.
Set up your Google Cloud environment by creating a Google Pub/Sub topic where the data will be published. Ensure that the Google Cloud service account used has the necessary permissions to publish messages to this topic.
Use the Google Cloud Python client library or the REST API to authenticate and interact with Google Pub/Sub from your AWS Lambda function. You'll need to securely manage and access your Google Cloud service account credentials, possibly by storing them in AWS Secrets Manager or AWS Systems Manager Parameter Store.
Implement the code within your Lambda function to publish the processed data to the Google Pub/Sub topic. Use the Google Cloud Pub/Sub libraries to format and send your messages. Ensure proper error handling and logging to track successful or failed message deliveries.
Set up monitoring and logging for both AWS Lambda and Google Pub/Sub to track the data flow and identify any issues. Use AWS CloudWatch to monitor Lambda execution metrics and Google Cloud Console to observe Pub/Sub activity. Optimize the process by adjusting Lambda memory and timeout settings and handling message retries appropriately.
By following these steps, you can move data from a DynamoDB table to Google Pub/Sub without relying on third-party connectors or integrations, utilizing built-in services and APIs from AWS and Google Cloud.
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:
