The Importance of Semantics for Data Lakehouses

Data lakehouses would not exist — especially not at enterprise scale — without semantic consistency. The provisioning of a universal semantic layer is not only one of the key attributes of this emergent data architecture, but also one of its cardinal enablers.

In fact, the critical distinction between a data lake and a data lakehouse is that the latter supplies a vital semantic understanding of data so users can view and comprehend these enterprise assets. It paves the way for data governance, metadata management, role-based access, and data quality.

Without this semantic layer, data lakes are just proverbial data swamps.

With semantics, however, users access a host of benefits from the data lake architecture. Users can help themselves to scalable cloud storage and processing platforms, store all data for both transactional and analytics/BI use cases, and comprehensively query data to support modern machine learning and Artificial Intelligence applications.

Consequently, some of the most respected vendors in the data sphere — including Google and Amazon Web Services — are embracing this concept and delivering consumable options to their respective user bases.

The linked data approach of knowledge graphs is predicated on technologies that provide granular semantic understanding of data. These technologies excel at delivering a uniform semantic layer to make the data lake house a reality — and one of the best choices for managing data in the AI age.

The Data Warehouse Foundation

Bolstered by effectual semantics, data lakehouses are a combination of traditional data warehouses and data lakes. Data warehouses are used across the data landscape and have a number of strong points. They’re great at integrating data and provide semantic consistency for the above data governance and data quality factors mentioned. However, their principal point of weakness is they’re expressly designed for structured data and are difficult to use with the array of semi-structured and unstructured data necessary for today’s AI. Plus, they rely on conventional ETL methods based on copying data, which is costly and exacerbates data quality.

Data lakehouse users don’t need multiple data copies for transformation or traditional BI approaches, which boosts data quality. Moreover, these repositories work well on the semi-structured and unstructured data that’s ideal for building machine learning and AI applications, yet arduous to use with data warehouses. Semantic knowledge graph technologies are adept at harmonizing data of any variety (across formats, schema, and structure variations), while unifying the terminology describing them. Applying this advantage to data lake houses provides an excellent semantic layer with which the business can view and manipulate data assets.

Improving on Data Lakes

The chief boon of data lakes is that organizations can deploy them in the cloud and store all data — in their native formats — within them. They forsake the costly infrastructure and time-consuming ETL processes that are necessary to rigidly conform data to a single schema for integration in data warehouses. Additionally, the sheer variety of supported data works well for building machine learning models.

However, data lakes less integrate all of these disparate data sources than they collocate them. They don’t have mechanisms for addressing semantics, metadata consistency, and data governance, which is why the data swamp moniker arose.

Semantic Technologies

Data lakehouses preserve the above data lake boons while rectifying their shortcomings. They have an open architecture so firms can use whatever tools they want on the data stored in lakehouses. However, they deliver this advantage with a semantic consistency that’s perfect for reinforcing data governance and data quality. In this respect, the semantic technologies of knowledge graphs are commendable. They’re predicated on giving each individual datum a unique, machine readable identifier and characterize them with self-declarative semantic statements (triples) in business-friendly terminology.

Consequently, business users understand what these data mean, while those same triples are helpful for implementing role-based (or any other attribute) access to data to fortify data governance. Additionally, these semantic approaches involve uniform vocabularies and taxonomies to describe the concepts in data — both of which greatly improve the semantics for data assets and serve as a data quality starting point. These characteristics are excellent for metadata management; they ensure business departments or organizations use the same terms for uniform descriptions of data and their significance to business objectives.

A Perfect Match

Semantic technologies are also underpinned by uniform data models that naturally evolve to incorporate new schema, data sources, and business requirements, which is what data lakehouses need. The linked data approach allows for metadata and data lineage to be linked to these models to heighten these data governance mainstays. There’s not a better approach than these linked data technologies for implementing the semantic layer necessary for users to view and understand data, which is essential for making data lakehouses successful.