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Begin by exporting your data from DynamoDB. Use the AWS SDK or CLI to scan your DynamoDB table. If your dataset is large, consider paginating your scan requests to avoid exceeding throughput limits. Save the exported data in a structured format such as JSON or CSV.
Prepare your environment to work with Apache Iceberg. Install Apache Iceberg and set up a compatible data processing engine like Apache Spark or Apache Flink, which can interact with Iceberg tables. Ensure your storage system (e.g., S3, HDFS) is configured to store Iceberg tables.
Once you have your data exported from DynamoDB, transform it to match the schema expected by your Iceberg table. DynamoDB is schema-less, so you may need to determine appropriate data types and structures for your Iceberg schema. This step may involve writing a script or using a data processing tool to map and convert data types.
With the data schema ready, create an Iceberg table using your selected data processing engine. Define the schema and partitioning strategy if needed. Use SQL-like syntax in Spark or Flink to create the table, specifying the storage location and format (e.g., Parquet or Avro).
Load the transformed data into the Iceberg table. Utilize the data processing engine to read the transformed data file(s) and write them into the Iceberg table. This might involve using Spark DataFrame API or Flink SQL to insert data into the table, ensuring that the data types and schema align with the Iceberg table definitions.
After loading the data, verify its consistency and integrity. Run queries on the Iceberg table to check for completeness and correctness of the data. Compare sample entries between the original DynamoDB data and the Iceberg table to ensure no data was lost or corrupted during the transformation and loading processes.
Finally, optimize and manage your Iceberg table for performance. Consider compacting small files, optimizing the data layout, and maintaining metadata. Use Iceberg's native functionalities to manage table versions, snapshots, and perform regular maintenance tasks to ensure optimal performance and scalability.
By following these steps, you can successfully move data from DynamoDB to Apache Iceberg without relying on third-party connectors or integrations, leveraging built-in capabilities and common data processing tools.
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:
