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Begin by ensuring that your data source is in a readable format, such as CSV, JSON, or any structured format that can be easily parsed. If your data source is a database, export the data into one of these formats. Ensure that the data is clean and structured properly to match the schema you will use in DynamoDB.
Log in to your AWS Management Console. Ensure you have the necessary permissions to create and manage DynamoDB resources. If you don't have an AWS account, sign up for one. Familiarize yourself with the AWS CLI (Command Line Interface) as it will be essential for uploading data to DynamoDB.
Navigate to the DynamoDB service in the AWS Management Console and create a new table. Define the primary key (partition key and optional sort key) according to your needs. Choose the appropriate read/write capacity mode (on-demand or provisioned) based on your expected data usage.
Download and install the AWS CLI on your local machine. After installing, configure it using the `aws configure` command, providing your AWS Access Key ID, Secret Access Key, default region, and output format. This setup will allow you to interact with AWS services directly from your terminal.
Write a script in your preferred programming language (e.g., Python, Node.js) to read your data source and convert it into DynamoDB's JSON format. DynamoDB requires specific data types like `S` for string, `N` for number, etc. Ensure each item in your script matches the structure and types defined in your DynamoDB table.
Use the AWS CLI or SDKs to batch write data into DynamoDB. For example, with the AWS CLI, use the `aws dynamodb batch-write-item` command. Since batch writes are limited to 25 items per request, ensure that your script handles this by splitting the data into appropriate chunks and iterating over them to load all items.
Once the data is loaded, verify that it has been accurately transferred by querying the DynamoDB table. Use the AWS Management Console or AWS CLI to perform queries or scans to check the data. Ensure the data matches the source in terms of structure and content, and troubleshoot any discrepancies as necessary.
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.
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:
