Let’s get one thing straight up front: Today, it’s not a question of “if” you’ll move to the cloud for enterprise data management, it’s a question of when.
Migrating a data warehouse/data lake from a legacy environment requires a huge upfront investment in resources and time. It doesn’t have to be this way, but it’s important to understand that there’s a lot to consider before migrating.
Companies must take a strategic approach if they want to streamline this process and ultimately reduce costly man-hours through rapid deployment.
On-premises vs. cloud
Let’s clear up some questions first. On-premises data warehouses/data lakes collect, store, and analyze data on on-premises servers, resulting in the need for hardware infrastructure management. However, local data is not always a viable option.
Cloud-based enterprise data storage offers flexibility and capabilities not typically found in on-premises solutions, but requires a strategic approach.
Over the past couple of years, organizations have started moving their data warehouses to the cloud. Here’s why:
Security and data protection: Is the main driver for cloud migration.
Next data upgrade: First of all, it involves moving data from old databases to modern ones.
Advance costs: On-premises data storage requires upfront hardware infrastructure costs. When working with cloud data storage, these costs are not necessary.
Fixed costs: The cloud offers a low pay-as-you-go model, while companies with traditional data warehouses have to deal with upgrade and maintenance costs.
Performance: Cloud data warehouse architectures use an extract, load, transform (ELT) process to make data processing much faster than on-premise options.
Flexibility: Cloud data storage is designed to work with large data formats and structures. Traditional relational options are simply designed to integrate similarly structured data.
Scale: The elasticity of the cloud allows companies to scale large data sets. Additionally, cloud storage options can be scaled down as needed. It is not easy for businesses to do this with traditional approaches.
Enterprise data warehouse (EDW) migration challenges.
Moving data from on-premises storage to the cloud creates several challenges that are important to understand.
When moving to a cloud-based EDW platform, companies must consider important migration and design implications. To minimize downtime, a seamless integration strategy must be developed for all EDW functions being moved to the cloud.
In this way, it becomes possible to transfer data to a new environment without interrupting other local processes.
Data transfer to the cloud
Transferring large amounts of data from one repository to another can often take a long time. When attempting to migrate an EDW, it is critical to clearly define the datasets and scopes early in the process.
This method provides an optimal connection for data movement. Accurate project timelines for data migration timeframes are also important.
Data integration and access
Enabling the flow of data from on-premises storage to the cloud has traditionally required an Extract, Transform and Load (ETL) process.
The various ETL tools must be tested to ensure proper operation and support in the cloud, as well as the features required to integrate with the EDW’s own cloud technologies. The final step is to recreate the transformations that create the final data models in the new cloud environment.
While the initial investment in time, labor, and money may be large, in the long run it is much better to follow an ELT paradigm rather than an ETL one, as it will enable faster cloud-based EDW deployments.
Consider tools that help you build a cloud-based EDW, as these tools will get your company up and running in record time. This is important because the cost of moving data can quickly add up if data movement is inefficient.
As cloud service providers offer cost-effective data storage prices, firms do not want to waste this opportunity to migrate again. It’s also a good idea to compress the data as much as possible before transferring it.
Developer experience versus new toolkits
Cloud-based EDWs provide flexibility and capabilities not typically found in on-premises solutions. They can often be challenging for developers who have to keep up with new features and constantly learn new features added by the cloud service provider.
While it’s true that migrating any IT service can be a daunting prospect, EDWs present an additional risk because they can disrupt business continuity.
When migrating to a cloud EDW, some key factors need to be analyzed and planned before migration, so with this in mind, the emergence of new rapid deployment technologies means that cloud DWs/lakes can now be created in hours and set up in a couple of weeks to start delivering significant information in record time.
This significantly lowers the total cost of ownership of a cloud DW/lake compared to traditional methods that typically took a year or two to get up and running before meaningful insights could be extracted from the data. The latter has been shown to increase costs by another 75%.
Given the demand for real-time data from enterprises, the next step is to set up a continuous change data collection pipeline that will continuously feed the cloud DW/lake with timely data.
A typical data warehouse contains large amounts of data spanning many business areas. Migrating all the data at once would almost guarantee failure.
Organizations need to take incremental steps to successfully move their data warehouse to the cloud, especially when making significant design changes. A phased approach allows a company to continue to operate on-premises data storage while cloud data storage is online.