Transcript
Clumio Backtrack for Amazon DynamoDB offers powerful, in-place, point-in-time recovery of full tables or specific partitions within tables. If a DynamoDB table has been corrupted or tampered with, you can leverage Clumio's recovery capabilities to recover your data back to a consistent state. In this demo, let's simulate restoring a critical DynamoDB table with Clumio Backtrack for Amazon DynamoDB. Starting in Amazon DynamoDB, click on the Inventory List table. Click Explore Table Items. Notice that in our Inventory List table, the Inventory ID INV001 does not exist. We don't know what product it was or how many we had in stock. So we have to restore this table back to a good previous state when that inventory item existed. Let's head to Clumio to start our restore. In Clumio, we go to Restore, DynamoDB Tables, and browse our inventory. And here's our Inventory List table. It looks like the last secure vault backup was an hour ago, but we need to restore to even before that, when the inventory item still existed. Since today is the 11th of July, we'll go one day back to the 10th. We'll select Restore DynamoDB Table, choose Custom, and we'll do an in-place restore. We get a warning saying that our data will be overwritten. And then if you notice under Configurations, we have point-in-time restore enabled. And we're restoring our table to 12 a.m. on July 10th. If everything looks good, click Restore. Our restore will start, and we can track its progress under Tasks. And after some time, the restore is finished. And now we can go back into AWS to make sure that our table data looks correct. Click on Inventory List, Explore Table Items, and if you scroll down, it looks like Inventory ID 001 is back, the product was a motherboard, and we have 100 in stock. Now if we scroll down, notice that our INV001 Inventory ID primary key is back. It shows we have 100 motherboards in stock in our Sacramento warehouse. With Clumio's point-in-time recovery capabilities, we can quickly recover our DynamoDB tables from tampering due to security threats or plain old user error. This approach minimizes business disruption while ensuring data integrity, allowing you to address data corruption issues quickly and efficiently so your applications can continue serving customers with minimal downtime. Thanks for watching.
