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First, export your DynamoDB table data to Amazon S3. You can use AWS Data Pipeline or AWS Glue to define a job that extracts data from DynamoDB and writes it to an S3 bucket in a CSV or JSON format. Ensure you have the necessary permissions and the S3 bucket is configured correctly.
Install and configure the AWS Command Line Interface (CLI) on your local machine. Use the `aws configure` command to set your AWS credentials and default region. This setup is essential for accessing and downloading the data from S3 to your local system.
Use the AWS CLI to download the exported data from your S3 bucket to your local machine. Execute a command like `aws s3 cp s3://your-bucket-name/path/to/data . --recursive` to transfer the files. This step ensures you have a local copy of the data that you can process before loading it into Teradata.
Depending on the format (CSV or JSON) and structure of your data, you might need to transform it to match the schema of your Teradata tables. Use scripting languages like Python or shell scripts to clean, format, and transform the data. Ensure that the data types and structures are compatible with Teradata Vantage.
Install Teradata client tools on your local machine, such as Teradata SQL Assistant or Teradata Studio. These tools will allow you to interact with your Teradata Vantage environment and facilitate data loading.
Use the Teradata FastLoad or Teradata Parallel Transporter (TPT) utility to load the transformed data into your Teradata Vantage tables. Prepare a FastLoad or TPT script specifying the local data files, target table, and necessary load parameters. Execute the script to begin the data transfer process.
After loading the data, run validation queries in Teradata to ensure that the data has been transferred correctly and completely. Check for discrepancies in row counts, data types, and specific values between the source data and the target tables. Make any necessary adjustments or re-loads if inconsistencies are found.
By following these steps, you can effectively move data from DynamoDB to Teradata Vantage 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:
