Enterprises of all sizes rely on data warehouses and data lakes to manage and analyze their data. But these traditional architectures have several limitations that can hinder companies from achieving desired business objectives. With this in mind, a new concept called “data lakehouse” has emerged as an attractive solution – combining the best features of both data warehouses and data lakes.
Adam Ronthal, vice president analyst for data management and analytics at Gartner, explains that data lakehouse is a convergence of data warehouses and data lakes with two main objectives: offering the right level of data optimization to serve the target audience and physically integrating the warehouse and lake environments. This unified platform allows companies to store all types of data—from structured business records to unstructured data like social media posts—in one place for both real-time analysis and machine learning applications, unlocking more useful insights and improved decision-making.
The move to a data lakehouse model is often considered to be challenging as it involves migrating existing data workloads, ensuring compatibility and security, and allocating sufficient time and resources for a smooth business transition. Establishing a virtualization layer across existing warehouse environments can minimize disruption and cost. After that, companies should prioritize their data needs based on budget, complexity, or analytics use cases in order to start migrating their data.
It’s important to note that a data lakehouse model is not suitable for everyone, and transition must be based on clear business goals. However, those who successfully make the transition can experience improved flexibility, scalability and cost savings, compared to legacy architectures.
Starburst is a company that specializes in helping enterprises with their data lakehouse migration process. Field Chief Data Officer Adrian Estala advises small businesses to set up a virtual layer to maintain existing solutions and ensure business continuity before moving forward. Additionally, companies should continuously assess and test their lakehouse to make sure it is suitable for their needs.
Overall, data lakehouses offer an effective and efficient way for companies to overcome the limitations of traditional architectures and gain better access to valuable data. With the help of data lakehouse, companies can prepare for a successful data journey and get ahead of the competition.
