[INFOGRAPHIC] 6 Important Preparations for Cloud Data Lake Migration

[INFOGRAPHIC] 6 Important Preparations for Cloud Data Lake Migration

Migrating your data lake to the cloud is not a project that should be tackled hastily, although we all wish it could be! It’s a long-term strategy that requires thoughtful planning and a phased approach. For a successful transition, you must take several factors into consideration. This blog (and the corresponding infographic) reviews six important preparations for your cloud data lake migration. 

Tip #1: Know Before You Go

Before you can even define your use cases or determine the full scope of your project, you must know what is in your existing data lake. Data lakes are often being fed by various applications and technologies, so untangling all of that and understanding where data is originating and how it’s flowing through your data lake is crucial. Automated solutions can help you tackle this without having to manually trace the data’s lineage. 

Tip #2: Assess User Impact

Once you know which applications and technologies are populating your on-premises data lake, it’s important to trace which of those systems your teams are using and how they’re using them. Don’t plan to migrate a use case when teams need access to that business-critical data. The goal is to minimize the data lake migration’s negative impact on users, so choose a time to do the migration that makes sense for minimizing disruptions. Estimate the scope and timing of all potential service disruptions and communicate them proactively to those affected. You may also want to consider updating documentation or training materials that will help teams with the transition. 

Tip #3: Prioritize by Use Case

Not every workload in your data lake is still relevant to your business, and some are more important than others. Focus initial efforts on the use cases that could most benefit from the cloud transition. Rank the importance of data sets for different use cases – executive reporting, security, marketing, etc. Determine which ones are best suited for the cloud based on performance, cost, and usage. What outcomes are you trying to achieve with each use case? Which ones could you implement sooner to start seeing tangible ROI benefits? As each use case is successfully deployed to the cloud, you can start to consider others that make sense to queue next. 

Tip #4: Plan Your Security

On-premises data security protocols will differ from those in cloud or hybrid environments. Maintaining your data governance and security is crucial when migrating to a cloud data lake. Which security and regulatory requirements are offered by your chosen cloud vendor? Is data encrypted both at rest and in transit? How will the security protocols of the cloud provider blend with your own standards?

Not only must you understand what the cloud vendor is providing in the way of security, but your organization should also take its own security precautions. That means backing up your data before starting the migration in case you need to restore it. It also means defining permissions for who in your organization will have access to the data lake and defining a new security architecture to match.

Tip #5: Mind the Metadata

You don’t want your data lake turning into a data swamp. Metadata management prevents that and ensures data is stored and converted appropriately. The storage component is particularly important if you are using transient clusters (computes on-demand) in your data lake because metadata will automatically be deleted by the cloud provider once that transient cluster is shut down. Some of today’s data lake solutions have metadata management in place, but many organizations adopt a separate data lake management platform to automate the capture of technical, operational, and business metadata. 

Tip #6: Upskill DevOps

As you plan your cloud data lake migration, you must also assess how your internal teams can support the new infrastructure. DevOps for an on-premises environment will differ from the cloud, and although many vendors offer training, it’s still necessary to consider where skills gaps may occur. For example, your team will need to know how to deploy code and start and stop clusters in the data lake. Take stock of your current DevOps skillsets and get teams tuned into the necessary training and upskilling as early as possible. If the need arises to hire someone new, it’s best to realize that earlier on in the process so you can factor hiring and training into your strategy. 

Migrating to a cloud data lake is exciting and a huge step forward in reducing the cost of on-premises data storage and improving performance. Attack the project thoughtfully and in phases for the best results. If you need help preparing data for your data lake, reach out to our team. We offer data services around data lake and warehouse management.