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Ensure you have the AWS Command Line Interface (CLI) installed and configured on your system. You can download it from the AWS website. Use the `aws configure` command to set up your credentials. Verify access to DynamoDB by listing tables with `aws dynamodb list-tables` to confirm connectivity.
Use the AWS CLI to export data from your DynamoDB table. You can use the `aws dynamodb scan` command to retrieve all items from the table. Save the output to a JSON file for easier processing. For example, use:
```
aws dynamodb scan --table-name YourTableName --output json > dynamodb_data.json
```
Download and install Typesense, a powerful open-source search engine. Follow the installation instructions on the [Typesense GitHub page](https://github.com/typesense/typesense) for your operating system. Once installed, start the Typesense server using the command line.
Typesense requires data in a specific format, typically as a JSON array of objects, where each object represents a record. Write a script (in Python, Node.js, etc.) to transform the DynamoDB JSON data into the required format. Ensure each record includes a unique identifier field since Typesense requires it.
Before importing data, define a schema for your collection in Typesense that matches the structure of your transformed data. Use the Typesense API to create this schema via a POST request. Include fields such as `name`, `type`, and `facet` if necessary. For example:
```json
{
"name": "your_collection",
"fields": [
{"name": "id", "type": "string"},
{"name": "title", "type": "string"},
{"name": "description", "type": "string"}
]
}
```
Use a script to read the transformed data and send it to Typesense via its API. The data should be posted to the `/collections/{collection_name}/documents/import` endpoint. Ensure your script handles batch imports if the dataset is large, as Typesense has limits on the size of individual import requests.
After importing, verify the data integrity by querying the Typesense collection. Use the Typesense search API to perform a few sample queries, ensuring that the data is searchable and returns expected results. Adjust your schema or re-import data if discrepancies are found.
Following this guide, you will have successfully migrated data from DynamoDB to Typesense 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: