Data masking is a cutting-edge data security technique that allows you to copy a dataset but obfuscate sensitive data or information. You can use this data for a wide range of purposes, such as training and testing.
Data masking is an effective technique to create a fake but meaningful version of your business data. The primary purpose of using AWS DMS is to mask and protect your data. At the same time, this technique enables you to provide a functional alternative when you don’t need real data.
In addition, it allows you to set authorization protocols, meaning your IT team or personnel in your organization will view data when they need it. You can use AWS MDS to mask your sensitive data effectively, including personally identifiable data, intellectual property, financial data, including credit card information, and healthcare data. Read on!
Safe and Secure Data Migration
AWS DMS is a powerful tool that allows you to migrate data to AWS safely and securely without complications. You can use this tool for data migrations, especially to and from open-source databases. Research shows that AWS DMS can streamline data migration and masking operations for businesses.
In addition, you can keep the source database operational during the process of migration, leading to reduced application downtime. Whether you want to perform homogenous or heterogeneous migrations, you can use AWS DMS because it supports both.
SQLite expression-based Data Transformation
AWS DMS data replication involves transformations based on SQLite expression data, allowing for accurate masking of data fields. You can use this tool for data replication from the PostgreSQL cluster to Amazon S3.
AWS MDS loads data into the target endpoint. However, before AWS MDS performs this operation, it masks the data. On the other hand, you can also pull data from the source endpoint and load it into the replication instance.
Accurate Connection to Source Data
AWS DMS is an effective tool that enables you to use a replication instance. The purpose is to connect the source data store, read, format, and load your data into a target data store. Bear in mind that masking occurs before loading the data into the target using the replication instance.
Select the Key Pair
It is crucial to use the Cloud Formation template, requiring you to select the key pair, such as Amazon EC2. You can use the EC2 instance for key configuration in the public subnet. Likewise, you can make this instance in the private subnet to connect to the Aurora Cluster. We recommend having a key in the region for the accurate deployment of the template. You can also create a new key pair in case you don’t have a key pair available.
Create a Cloud Formation Stack
The next step is to create a cloud formation stack. The process takes about 15 minutes. Make sure you use the resource tab to view the resources on the console. Focus on Aurora Source End Point Address on the output tab. Likewise, you have to focus on the S3 bucket. It stores the masked or replicated dataset from the AWS DMS that you can use as an output. Other crucial things to focus on are:
- Connect to the source database
- Populate the source table
- Start the replication process
- Query the dataset