Data Lake vs. Data Warehouse and Why Kyvos Semantic Layer Beats Both

Every business today is sitting on a data fortune, yet without the right tools they are not able to use their data to its full extent. That’s what happens when businesses amass data at breakneck speed and store it in an unstructured way. The more data they collect, the harder it gets to sift through it. The potential is there, but finding useful information in an unorganized system is like searching for a needle in a haystack—except that the haystack keeps growing and the needle keeps changing shape.

To manage their data deluge, there are three storage architectures that organizations can choose from. First, there are data lakes that boast vast inexpensive storage for raw, unstructured data. Then there are data warehouses that bring order with structured storage making it easier for users to pull reports and analyze trends. Lastly, data lakehouses combine the flexibility of data lakes with the structure and performance of data warehouses.

All three storage systems hold a place in the data ecosystem, but they may not suffice when it comes to turning data into insights. Let’s see in the next section what their challenges are.

Data Lake vs. Data Warehouse vs. Data Lakehouse: Why Do They Fall Short?

Enterprises need speed, simplicity and scalability in their analytics to make strategic decisions faster. But most storage systems struggle to deliver on these fronts.

Data warehouses provide better data management, but they come with high costs because they need high processing power to keep data in a structured format for analysis. Organizations have to predefine how data should be stored and formatted, which involves designing schemas, defining relationships between data points and setting rules for how the data should be processed. This process requires expensive infrastructure, storage and ongoing maintenance along with licensing fees. If the business needs to modify its structure later or new data sources come in, scaling up becomes difficult due to a rigid design. This makes it hard to adjust and often requires infrastructure upgrades which adds more cost and effort.

Organizations then turned to data lakes to overcome warehouse challenges and get more flexibility. The data in a data lake is stored in its raw, unprocessed form without a predefined schema. The lack of structure makes it difficult for systems to locate, filter and retrieve information, as they have to scan the whole data without clear indexing. This is why the queries get slow. Additionally, as data volumes grow, maintaining quality and governance also become an issue. Without standardized formats or built-in validation, inconsistencies arise and duplicate or incomplete data can accumulate, which makes it difficult to track data lineage and enforce access controls.

Next in line are the lakehouse that combines the structure and high performance of a warehouse with the flexibility and scalability of a data lake. However, as organizations started using lakehouses for large-scale datasets, they came across new performance challenges. Since raw data is stored in open formats like Parquet or Delta, queries involve multiple joins, filtering and aggregations at run-time, increasing the processing time. Lakehouses also don’t provide intelligent caching mechanisms for frequently used queries. Every time a user fires a query, the system has to process it from scratch, which requires scanning massive data and further slows down performance.

The Need for a Semantic Layer

None of these systems offers a semantic layer. Without a unified, easy-to-understand layer on top of raw data that standardizes definitions, teams are often left navigating complex datasets.

Lakehouses allow direct access to data and different teams may define key metrics differently to create their own interpretations, causing data silos and conflicting reports. Also, without a semantic layer, the architecture doesn’t facilitate stringent governance policies.

Since this layer provides materialized views of complete data, the real-time reporting speeds up. But since there is no such thing happening in lakehouse, every query has to compute aggregations on the fly. This also creates a barrier for non-technical users as they find it difficult to navigate raw data and have to rely on technical people to write queries.

These challenges call for a holistic approach—a lakehouse architecture with semantic layer.

What is Semantic Lakehouse Architecture?

A semantic lakehouse is a modern data architecture that blends in the best of all three worlds. It combines the scalability and flexibility of data lakes, the high performance of data warehouses and metadata enrichment. The addition of a semantic layer to a lakehouse simplifies data access by standardizing business definitions and metrics across the organization. It allows enterprises to define security and governance policies to ensure sensitive data is secure at all times.

So basically, a semantic lakehouse resolves all the challenges posed by data lakes, data warehouses and lakehouses. However, enterprises need to select the right platform that not only delivers scalability, governance and accessibility but also offers speed, cost-efficiency and rich data modeling.

Kyvos: The Semantic Layer

Kyvos is a semantic layer that accelerates every AI and BI initiative by combining the power of high performance with rich semantics. The platform delivers lightning-fast analytics and AI initiatives at infinite scale, with reduced data platform costs.

Whether the data is structured or unstructured, Kyvos creates scalable data models using its AI and ML capabilities. The platform connects with any data source and removes data silos to provide seamless access without data movement and delivers hyper-speed analytics across AI and BI applications.

Kyvos also offers built-in connectivity for Excel, Power BI, Tableau and MSTR. Users can work with their existing tools while our semantic layer maintains standardized metric definitions and calculations across all tools. With native support for Microsoft Azure, GCP and AWS, Kyvos ensures seamless integration across cloud and on-prem environments.

One of the most differentiating features of Kyvos is its semantically rich ultra-wide data models that can handle over 1,000 attributes, 60+ dimensions and thousands of measures. The platform supports thousands of pre-calculated KPIs and drill-down capabilities that go beyond 25 levels.

With our advanced capabilities, enterprises can analyze all their data to the lowest level of granularity without performance issues. Kyvos also supports advanced hierarchical analysis with support for parent-child relationships, alternate hierarchies with custom rollups as well as unbalanced and ragged hierarchies for more precise calculations.

While delivering powerful analytics, the platform also ensures that data remains protected at every level. Kyvos also automates the entire process—from data ingestion to building optimized models, eliminating the need for manual maintenance, updates and audits. Our built-in visualization capabilities through Kyvos viz enable users to explore full breadth of their data, unlike other BI tools that struggle to support such ultra-wide data models.

Kyvos reduces reliance on cloud data warehouses that charge based on queries. It creates price-performant data models that allow organizations to process any amount of data without worrying about escalating costs. The platform also provides horizontal scaling and runtime cost savings, which makes analytics even more affordable for businesses.

Choosing Kyvos can help businesses handle the complexities of ever-growing data and turn them into opportunities for innovation and business success.

Contact our experts to learn more about our offerings and get more insights into how we deliver faster, more scalable and cost-effective analytics to everyone.