Kyvos Semantic Layer: Bringing Speed, Simplicity, and Scalability to Your Data Lakehouse

Today’s data can be compared to the vast, deep and largely untapped ocean. Only 2% of this ocean is utilized by organizations and the rest remains underutilized. Why? Most organizations today struggle with data silos centered around separate data lake and data warehouse architectures for different use cases. While data warehouses and lakes were groundbreaking solutions in their time, the day-by-day growing variety, velocity and volume of data left them strained and insufficient to meet modern business needs.

To tackle data silos and inefficiencies, enterprises moved to data lakehouses. But did data lakehouse architectures solve all their data challenges or did it come with its own set of complexities? Let’s explore in this blog the evolution of data lakehouse and the critical role of a semantic layer in uncovering its full potential.

Evolution of Data Lakehouse

In the 1980s, when data volumes generated were easily manageable and data architectures were less complicated than they are now, businesses started seeking data-driven insights. They moved beyond relational databases to systems capable of mustering data from operational databases and storing it in a centralized repository to provide analytical insights. These systems, or data warehouses, were first-generation cloud storage platforms offering structured, clean datasets with predefined schemas for data analytics. However, they were not designed to handle semi-structured or unstructured data. As data volumes grew in variety and velocity, processing them and generating insights became more challenging and costly.

Data lakes were one step ahead of data warehouse architecture, enabling businesses to store structured, semi-structured and unstructured data in their native formats. It became a scalable, cost-effective storage solution to manage large-scale raw datasets. Many organizations used it as a staging ground where all kinds of data lived before being transformed into an analytics-ready format.

However, these organizations still need a separate data warehouse environment to serve their historical analytics and BI use cases. This results in data silos and multiple data copies, numerous governance models and inconsistencies across both environments. Apart from this, another challenge encountered while using data lakes was a lack of transactional support—the ability to maintain data consistency and integrity during concurrent updates and writes for reliable data quality.

Soon, organizations realized they needed a single unified, flexible platform for querying all types of data, bringing the ease of use, performance, reliability and governance features of a data warehouse architecture directly to the data lake.

In 2020, the data lakehouse architecture became the next big thing. A cost-effective data management architecture that offers a unified, reliable platform for storing and analyzing all data types, eliminating the need for separate storage platforms. It comes without the extra baggage of data swamp that arises in traditional data lakes due to disorganized, duplicated data dumped by multiple teams and conflicting definitions without any standardized process.

Through data lakehouse, businesses can directly access data for analysis without moving it to a separate warehouse. They can integrate a lakehouse with all the popular BI tools, allowing users to run ad-hoc queries and generate reports directly from it. Another key feature that comes with this architecture is it enables businesses to enforce security policies, such as role-based access control (RBAC), encryption and auditing, directly on a lakehouse to provide secure data access to authorized users.

While the data lakehouse solved the long-standing issues of data silos and inefficiencies by providing a unified, cost-effective platform for all data types, the struggle of the organizations didn’t just end here. With organizations moving towards a data-driven culture, even data lakehouse architecture presented challenges in providing democratized data access, usability and performance due to the inherent complexity of managing diverse and voluminous data pipelines, domains and analytics processes. Let’s see how these challenges necessitate the adoption of the semantic layer.

Adding a Semantic Layer to a Data Lakehouse

Although data lakehouse architecture centralizes data from various sources and supports multiple use cases, lack of unified access and management across domains becomes a complex issue. Each domain may have its own requirements related to access permissions, governance policies and performance which makes the experience of users disjointed. Another challenge is data lakehouse often doesn’t provide clear definitions, relationships and contextual explanations, which makes it hard for users from multiple domains such as finance, marketing, sales to interpret and use the data. In this architecture, the raw data might be stored in tables and columns or any other format, but it lacks semantic context. This absence of context makes it difficult for data teams to interpret relationships between the data points to derive correct insights.

In addition, users without technical expertise often encounter problems exploring, querying and visualizing data effectively in a data lakehouse. While BI tools facilitate these tasks, they also need a clear understanding of the underlying data structure and relationships. Without a semantic layer, there is no way to ensure consistency in data definitions, metrics, and relationships. This hinders the user’s experiences when interacting with data and creates confusion, leading to incorrect interpretations.

Making this data easily understandable and actionable for businesses is where the semantic layer truly shines. It enhances user accessibility by translating complex data into business-friendly concepts.

How Does a Semantic Layer Work in Data Lakehouse Architecture

Let’s understand with an example, imagine data stored in two tables. The first table lists employees by fields like employee ID, full name and department. Another table describes the designation of the employees. Looking at these two tables, one might identify that Employee A works for the marketing department and Employee B reports to Employee A. However, what these tables lack is semantic context as they don’t explicitly describe relationships like “manager,” “direct report,” or “colleague.”

In this case, if a business user queries this data for insights, such as who are the colleagues of Employee A?” or “Which employees does Employee A manage?”, it would be challenging to answer these questions. Without a semantic layer, the raw data doesn’t have the business logic or domain-specific understanding needed to answer these nuanced questions.

Enterprises can bridge this gap by harmonizing data models with standardized concepts and vocabularies. A semantic data model with a pre-defined set of business rules and relationships allows organizations to apply common logic without altering the underlying data or its structure.

For instance, in the above example, a semantic layer can clarify that if an employee reports to a manager, then the employee is a “direct report,” and the employees of the same department who don’t report to each other are “colleagues”. These rules give meaning to raw data and turn it into a rich, human-understandable context. With a semantic layer in the data lakehouse ecosystem, users can go beyond the limitations of the original data structure and connect, interpret and utilize data in a way that aligns with real-world business logic and provides context-rich answers.

How Kyvos Semantic Layer Makes Analytics Easier

Kyvos semantic layer unifies high-performance analytics with rich semantics, enabling enterprises to accelerate every BI and AI initiative. It offers infinite scalability, lightning-fast query performance and cost-efficient data processing while supporting both structured and unstructured data. Kyvos organizes, simplifies and accelerates data consumption by establishing a unified and logical view of enterprise-wide data. It defines all the metrics, access rules and calculations in one place, eliminating the need for users to have technical expertise and instead empowering them to derive consistent and accurate insights.

Here are some of the advantages of using Kyvos:

Optimize Performance and Achieve Seamless Scalability

While data lakehouse are designed to handle large datasets, they often struggle with performance and scalability when it comes to analyzing complex, unstructured data. Organizations can solve this challenge with Kyvos’ scalable and distributed architecture. With its AI-powered smart aggregation technology, users can create massively scalable data models, that can be used multiple times to process large and complex datasets and deliver sub-second responses. The ML-powered smart recommendation engine analyzes query patterns and provides intelligent recommendations for an optimal set of aggregates to be built for an optimized data model. Since the data is processed in advance, the response times of all the queries are reduced, improving the performance.

Experience Multi-Dimensional Analytics

Since data in a data lakehouse is stored in its raw form, it might be stored at varying levels of granularity (e.g., transaction-level data vs. summary-level data). So, when organizations try to aggregate this data for analysis without a semantic layer, inconsistencies can arise especially in complex scenarios like “drill-across” queries, where data from different levels of granularity needs to be combined accurately. Without proper handling, overcounting or loss of detail can occur, leading to incorrect insights.

Kyvos combines data from different granularities in a way that ensures accuracy and supports approximate (to improve performance) and accurate (for precise results) distinct counts so that businesses can accurately track unique entities, such as customers, without overcounting them.

Kyvos allows multidimensional analysis by creating semantic models that process calculations on massive datasets and enable MDX, DAX, SQL or OLAP4J querying that delivers instantaneous responses. As the entire data is processed in advance, all queries—standard or ad-hoc, old or new—are served equally fast.

Robust Data Security and Governance on Data Lakehouse

Data lakehouse often contains a mix of blended data which is accessed by a variety of users with different roles and permissions and if robust security measures are not in place, then sensitive data might get exposed to breaches and misuse. While data lakehouses come with some security and governance capabilities, they often lack granular access control at individual level, support data masking and features like SSO and LDAP integration.

Kyvos addresses these gaps by providing comprehensive security features to keep sensitive data safe, trusted and compliant. With its three-tiered security architecture, organizations can enforce solid guardrails at multiple levels to prevent external threats and unauthorized access from within the company’s firewalls. As it sits between analytics tools and data platforms, Kyvos ensures complete data protection across the stack. It offers role-based access control (RBAC) at group or individual levels for digging into granular details. Additionally, Kyvos supports data encryption to safeguard data at rest and in transit and it also integrates seamlessly with third-party encryption tools.

Final Thoughts

The evolution from data warehouse architecture to data lakes and eventually to data lakehouse reflects how organizations have come a long way toward a better, faster and more unified data management. With the growing data volumes and complexity, a data lakehouse without a semantic layer often falls short in delivering seamless accessibility, usability and consistency.

With Kyvos, organizations can truly democratize analytics and enhance the performance, security and accessibility of data lakehouses while maintaining a competitive edge in an increasingly complex digital landscape.