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Export your data from DynamoDB to Amazon S3 using the AWS Data Pipeline service. Configure a data export pipeline in AWS Data Pipeline to read from your DynamoDB table and write the data as JSON files to an S3 bucket. Ensure that you have appropriate IAM roles and policies set up to allow access between the services.
Once the data is in S3, you need to ensure it's in a format that's easy to load into Teradata. If necessary, transform the JSON data into CSV format, which is often more compatible with SQL-based systems like Teradata. You can use AWS Lambda or AWS Glue to perform this transformation, writing the reformatted files back to S3.
Use a secure protocol like SCP (Secure Copy Protocol) or SFTP (Secure File Transfer Protocol) to transfer the data from S3 to a local machine or intermediary server. This step involves downloading the data files from S3 to your local environment while ensuring the security and integrity of the data during the transfer.
Create staging tables in Teradata that mirror the structure of your original DynamoDB data. This involves defining the appropriate data types and schema in Teradata to accommodate the incoming data. Use the Teradata SQL Assistant or BTEQ (Basic Teradata Query) to load the data from the local machine or intermediary server into these staging tables.
With the data in staging tables, perform necessary transformations to fit the final schema in Teradata. This could involve data type conversions, normalization, or other SQL-based data transformations. Ensure that the data adheres to any constraints and business rules required by your Teradata environment.
Once the data is transformed and validated in the staging tables, insert it into the final destination tables in Teradata. Use SQL INSERT statements to move the data from staging to production tables, ensuring data consistency and integrity throughout the process.
After the data is fully loaded and integrated into Teradata, perform a thorough verification to ensure that all data has been accurately transferred. Check for discrepancies, missing records, or any data anomalies. Once verified, clean up by removing temporary files from S3 and any staging data that is no longer needed in Teradata to maintain a clean and efficient environment.
By following these steps, you can effectively transfer data from DynamoDB to Teradata 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?
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