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While data migration to the cloud might sound simple, an enterprise needs to draw up a carefully laid-out plan that neither jeopardizes compliance nor runs up a big bill.
A business might move data to the public cloud for a number of reasons, such as access to storage, security, analytics and other services that might not be feasible on premises. But, regardless of the end goal, an AWS data migration is a multistep process. An enterprise must first securely and cost-efficiently migrate data to AWS, and then choose from a range of storage, data lake and database services, such as Amazon DynamoDB or Amazon Relational Database Service -- and it must do so carefully.
Follow these five tips for a safe and quick AWS data migration, and then find the best storage and management options after the move.
Keep sensitive data secure
Security is the one of the most important considerations for an AWS data migration, especially when it involves sensitive information. It's important to know how your security approach can put data at risk during the move and be especially mindful of things like network vulnerabilities.
Once your data gets to the cloud, it's important to encrypt it with tools such as AWS Key Management Service. You should also monitor your environment and log API calls to better understand who accesses your workloads and data and how they do it.
One method to back up sensitive data is a pilot-light approach. Once the data backs up to the cloud, an organization can then use AWS and third-party cloud tools for additional security features that aren't available on premises, while also making that data redundant.
Establish a direct connection to the cloud
With AWS Direct Connect, users can create a dedicated, consistent network connection between the cloud and their colocation facility or data center. This connection enables users to more quickly and securely upload data to the cloud than they could with the public internet.
The Direct Connect service is a major component of hybrid cloud deployments, and AWS continues to add new features to it. Direct Connect now supports data distribution across multiple AWS regions, which enables enterprise IT to connect from one location and push the traffic to any other region connected to the gateway. Direct Connect still has its limitations, but, by and large, it's a helpful migration tool.
Wade into data lake waters
Before you pursue an AWS data migration, consider a data lake architecture as a way to store your unstructured data. AWS offers tools to architect data lakes, though the process requires a thorough understanding of multiple cloud services.
Ultimately, if you gain knowledge of services like S3, Athena, Glue and Elastic MapReduce (EMR), you can process and query big data to gain insights from it. Keep in mind that this can be a pricy endeavor, so closely monitor costs -- especially as it can be difficult to estimate charges ahead of time.
New features for NoSQL databases
NoSQL databases, such as Amazon DynamoDB, can store unstructured data in carefully designed tables for easy access. As a fully managed service, DynamoDB also scales capacity as needed, and a pair of new features helps bring it up to par with other NoSQL competitors.
Amazon recently added DynamoDB improvements that enhance data resiliency. DynamoDB Global Tables enables developers to deploy tables across multiple regions, as well as write a table in one region and have it replicated and synchronized in other regions. DynamoDB Backup and Restore reduces the time it takes to perform backups on an EMR cluster.
Bring your database up to scale
Amazon Relational Database Service, which includes the Amazon Aurora engine, handles tasks for a variety of relational databases, including provisioning, patches, backup and recovery.
As it did with DynamoDB, AWS introduced features that enhance Amazon Aurora capabilities. Aurora Serverless can help enterprises solve the difficult and cumbersome process of scaling relational databases. Meanwhile, the Aurora Multi-Master feature helps create a fault-tolerant database configuration. Multi-Master enables Aurora to scale write requests, with no application downtime in the event of a database or availability zone outage.