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Begin by installing and configuring the AWS Command Line Interface (CLI) and the Databricks CLI on your local machine. This setup will allow you to interact with both AWS services and your Databricks environment directly from the command line. Ensure that you have the necessary permissions for DynamoDB and Databricks within your AWS and Databricks accounts.
Use the AWS Data Pipeline or AWS CLI to export the data from your DynamoDB table to Amazon S3. You can do this by creating a Data Pipeline that reads from DynamoDB and writes the output to an S3 bucket in CSV or JSON format. This step allows you to stage your data in a format that can be easily consumed by Databricks.
Log into your Databricks environment and configure your cluster. Ensure that the cluster has access to the necessary AWS credentials to read from the S3 bucket. This typically involves setting up an IAM role with S3 read permissions and attaching it to your Databricks cluster.
Use the Databricks environment to load the data from S3. You can use PySpark or Scala within a Databricks notebook to read the data. For instance, you can use `spark.read.csv()` or `spark.read.json()` depending on the format of the data exported from DynamoDB. This step involves creating a DataFrame in Databricks that holds the staged data from S3.
Once the data is loaded into a DataFrame in Databricks, perform any necessary data transformations. This could involve data cleaning, filtering, joining with other datasets, or reformatting the structure to fit your desired schema in the Databricks Lakehouse.
After transforming the data, write the DataFrame to your Databricks Lakehouse. Use the appropriate DataFrame writer method, such as `write.format("delta")`, to store the data in Delta Lake format. You can specify the target database and table name as needed, ensuring that your data is properly organized within the Lakehouse.
Finally, verify the data transfer by querying the new data in the Databricks Lakehouse to ensure accuracy. Additionally, optimize the data storage by running `OPTIMIZE` commands on your Delta Lake tables to compact small files and improve query performance. This step ensures that your data is both accurate and efficiently stored for future analytics operations.
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:
