Explainer: How Data Lakehouses Can Help Your Compliance Scheme

When it comes to storing your data, organization is important. But many companies aren’t that organized, and as a result, data often ends up in what’s known as a data lake—or, as Venture Beat calls it, “a broader repository that stores data in its raw or natural format.” 

Data lakes can be difficult for an organization to navigate and utilize. This is why organizations will often opt to utilize a “data lakehouse." This organizational method, which Forbes calls a hybrid mechanism, solves this by adding “layers of optimization to make the data more broadly consumable for gathering insights.” In other words, a lakehouse takes the highly-organized reporting and data analysis tools of a data warehouse platform and applies them to unstructured data in a lake format.  

Data that has been processed by a data lakehouse may be used to provide an organization with enhanced flexibility, scalability, cost savings, and exploration capabilities when compared to legacy architecture. That’s according to VentureBeat, which also notes that such a scheme can promote an ease of use for “applications such as artificial intelligence and machine learning[,]” and permit the organization to utilize that data for “real-time analysis, data democratization, and improved business outcomes via data-driven decisions.” 

But while there are many benefits to a data lakehouse, there are struggles that an organization could face when implementing these data processing structures. 

According to VentureBeat, organizations with existing architectures for storing data face the difficult task of migrating data in a legacy format to a new data lakehouse. This can be costly, prolonged, and disruptive to business operations.  

To avoid this, Adrian Estala, field chief data officer at Starburst, told VentureBeat that the use of a “phased migration approach” developed by your organization “should minimize business disruption and prioritize data assets based on your analytics use cases[.]” 

A phased migration starts, according to Estala, by establishing a “virtualization layer across existing warehouse environments, building virtual data products that reflect the current legacy warehouse schemas.” Then, these products can be used “to maintain existing solutions and ensure business continuity.” 

Once the existing data processes are secured, your organization should “prioritize moving datasets based on cost, complexity or existing analytics use cases.” Ronthal agreed with Estala’s recommended processes, and further promoted a “continuous assessment and testing” approach that begins with migrating the most complex data to ensure that the new data lakehouse is established in accordance with your organization’s expectations and needs. 

To learn more about data lakehouses and assess the value that one could add to your organization, you can download the comprehensive five-part eBook on the ItProToday website.