Organizations today generate large volumes of structured and unstructured data from their online and offline touchpoints. As emails, social feeds and videos become sources of valuable data, the BI tools that have traditionally provided analytics and reporting on structured data now struggle to keep up with the variety, scale and complexity of modern data.
In addition, with evolving business needs, users—with or without technical knowledge—have come to need analytics and insights more quickly. This need for data democratization poses an additional and unending burden on IT and data teams to facilitate BI and AI capabilities. The need of the hour is to adopt an architectural approach that can handle modern data and serve user demands—such as a semantic lakehouse powered by semantic intelligence.
What is Semantic Lakehouse?
A semantic lakehouse is a complete data system that puts a unified semantic layer on top of a data lakehouse for scalability and performance. In simple words, a semantic lakehouse is like a giant book library (data lake) that has been organized and catalogued like a bookstore (data warehouse) where all the labels, sections and subsections are clearly defined and have rules that everyone understands (semantic layer). A semantic layer within a data lake centralizes metrics (how a business calculates numbers or statistics related to sales, profit and loss, etc.), hierarchies (by country, state, product category, or more) and business logics, the rules for comprehending data. This layer powers semantic intelligence—the capability to derive meaning, relationships and contextual understanding from business data. The semantic layer provides consistency to data meaning across all data touchpoints, systems and teams so that users can trust the results and work more efficiently.
The semantic lakehouse is critical for business intelligence tools because it makes them faster and more reliable. That’s because BI tools and Gen AI systems need well-organized and clean data to perform efficiently. A semantic lakehouse provides these tools ready-to-use data where business definitions and logics have already been applied. Since it standardizes all definitions, Gen AI systems get accurate data to process into summaries, narratives, analyses and reports. But most importantly, it optimizes performance at scale because it is built on a data lake and combined with warehouse-like performance, which delivers fast query speed. It enables BI dashboards to load quickly, giving AI systems instant and faster results.
Semantic Lakehouse: The Best Bet for Uniting AI and BI
The semantic lakehouse can smartly pre-aggregates data such as averages or sums much before receiving any AI query or user query. When answering queries, it scans only the useful subset of data instead of scrutinizing the entire dataset every time. This helps avoid slow and large requests on raw, unfiltered data, while optimizing computation. The semantic lakehouse can further intelligently fine tune these aggregates to make them more efficient.
The semantic lakehouse also adds context by storing metric logics such as sales, revenue, discounts and exposes them as retrievable objects through semantic models. This helps apply all KPIs and their definitions across all BI tools, so everyone gets the same numbers. The result is reflected in faster dashboards, instant AI responses and the capability to handle more volume of users and queries without affecting speed.
One of the biggest advantages of a semantic lakehouse is in the way it acts as a unified platform for BI and AI. It combines BI dashboards and AI models where both human users and AI bots speak the same language due to predefined metrics. The system can hence serve as a single unified analytics platform for ad hoc analysis, Gen AI and predictive models.
Semantic lakehouse is also particularly relevant in the context of using retrieval-augmented generation (RAG) workflows. RAG is a process or technique that optimizes the output of large language models (LLMs) and enhances the accuracy of generative AI models using data retrieved from relevant sources. The semantic lakehouse uses metadata to describe fields or relationships, and the semantic layer provides business meaning and logic. This combination of semantic intelligence and contextual data modeling improves the accuracy and relevance of LLM responses. In addition, RAG workflows within the lakehouse facilitate access to precise data for AI models so they produce more accurate results. In short, a lakehouse with a semantic layer enhances AI-readiness of the system by powering LLMs and natural language interfaces and creates reliable business data.
Now that businesses are using Gen AI-powered business analytics tools, it has become imperative to balance empowering non-technical users to analyze data on their own while ensuring the safety and security of sensitive data. A semantic lakehouse strikes this balance through centralized metrics and role-based access control. The lakehouse and the semantic layer integrate with identity management systems and implement rules at query time. This ensures that users get to see only authorized data based on their respective roles. Users can query data via AI chats, yet only within the governed semantic layer.
From Vision to Impact: Real-World
An application of a semantic lakehouse can be found in an online and offline retailer whose data is spread across multiple systems. For instance, sales data may be stored in Salesforce, inventory in SAP, customer data in a legacy SQL database and so on. As each department queries its own data using its own terminology, which leads to inconsistent KPIs and delayed insights. The lakehouse can consolidate all this fragmented data on a single unified platform. The semantic layer defines uniform metrics and hierarchies and users can get access to this unified semantic model via BI tools or natural language tools like AI copilots. The result is faster, more accurate self-service analytics with centralized logic and data reusability.
Final Thoughts
With infinite business data at the disposal of organizations, the ability to unite systems, processes and users is critical to success. Semantic lakehouse has the power to multiply and combine the power of AI and BI, while facilitating self-service analytics on the entirety of enterprise data. With the capability of handling both deep human queries and AI-generated requests, semantic lakehouses—empowered by semantic intelligence— are fast becoming critical for modern architectures.
This article is offered to you by Kyvos Insights.