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Amazon DynamoDB: How to do a bulk upload to a DynamoDB table

by | Aug 26, 2021

One of the most common queries we get from our AWS customers is something along the lines of “how to do a bulk upload to a DynamoDB table?” We can help you with this!

As a part of our AWS Support Services, we often receive similar requests from our AWS customers.

Today, let’s see the steps followed by our Support Techs to help our customers to issue a bulk upload to a DynamoDB table.

 

Bulk upload to a DynamoDB table

 
We can upload data to DynamoDB in bulk by using any of the following options:
 

Using Amazon EMR:

 
We can upload data to DynamoDB with Amazon EMR and Apache Hive using the following steps:

  1. At first, weed to create an EMR cluster:
    Select emr-5.30.0 or later for Release.
    Select an option that includes Hive for Applications.

2. Then we have to create an external Hive table that points to the Amazon S3 location for our data.

3. Now create another external Hive table, and point it to the DynamoDB table.

4. To write data from Amazon S3 to DynamoDB, we can use the INSERT OVERWRITE command.
 

Using BatchWriteItem:

 
We can use the BatchWriteItem API operation to issue multiple PutItem calls simultaneously. Also to issue multiple parallel BatchWriteItem API calls to make the data load faster, we can use parallel processes or threads in our code.
 

Using AWS Data Pipeline:

 
We can use Data Pipeline to export to DynamoDB if the data is in Amazon S3. The Data Pipeline automates the process of creating an Amazon EMR cluster and exporting the data from Amazon S3 to DynamoDB in parallel BatchWriteItem requests. We don’t have to write the code for the parallel transfer while using Data Pipeline.
 

Using AWS DMS:

 
You can use AWS DMS to export data from a relational database to a DynamoDB table.

[Need help with more AWS queries? We’d be happy to assist]
 

Conclusion

 
To conclude, today we discussed the steps followed by our Support Engineers to help our customers to issue bulk upload to a DynamoDB table.

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