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The concept of a data lakehouse is an open data management architecture that combines the best aspects of a data lake and a data warehouse in a single platform.
A traditional data lake tends to be flexible and cost-effective by storing data in its raw or natural form typically unstructured or semistructured. A data warehouse is a more advanced repository of data for reporting and analysis that tends to store data that is more structured. The data has typically been cleansed or operationalized for better data quality, often the result of extract-transform-load (ETL) or extract-load-transform (ELT) operations.
Data analytics usage is increasingly widespread, and changing in nature. Those trends, coupled with the need to access large amounts of disparate data by many different users, mean that organizations need a new, more integrated approach to data access. A data lakehouse combines the best of data lakes and data warehouses, supporting business intelligence and machine learning technologies in one platform. The platform can store all types of data and provide it with a cloud-like, multiresource, and self-service interface for data scientists and other users.
The Dell Validated Design for Analytics — Data Lakehouse has been developed to address the needs of organizations deploying advanced analytics. It incorporates the concepts of a lakehouse architecture together with a container platform using decoupled compute and storage.
This document provides design guidance for data analytics infrastructure managers and architects by describing a predesigned, validated, and scalable architecture for advanced analytics on Dell hardware infrastructure.
This document describes a validated design and reference architecture for a data lakehouse platform that is integrated with a Kubernetes-based container platform. Together they address the needs of organizations deploying advanced analytics and AI workloads.
This design guide can be read alongside the associated white paper, Dell Validated Design for Analytics – Data Lakehouse. The white paper discusses the benefits of a data lakehouse compared to legacy data lakes and warehouses. It also provides a more general overview of the data lakehouse concept and its component technologies.
This document is intended for enterprises with data lakes or a data lake strategy interested in empowering their organizations to act more quickly, effectively, and efficiently on their data. Audience roles include:
A data lakehouse can assist more traditional analytics customers looking to modernize their data collection. It can also help analytics systems to get more value from their data or standardize their data for modern analytics workloads.