How Kyvos Makes Semantic Lakehouse Secure, Scalable, Accessible and Cost-Effective

Imagine a world where analysis on a billion-scale dataset can be performed at the speed of light, insights are instantly accessible and organizations can unleash the power of all their data, regardless of its format. This data utopia might seem like a dream, but it’s getting real now. IDC estimates there will be 55.7 billion connected IoT devices (or “things”) by 2025, generating almost 80B zettabytes (ZB) of data. However, with this data explosion, traditional data warehouses and data lakes struggle to keep up.

Challenges Posed by Data Warehouses and Data Lakes

The objective of the data warehouse is to help decision-makers get analytical insights by mustering data from operational databases and storing it in a centralized repository. However, as time progressed, organizations faced some challenges while using them. The platform coupled computing and storage into an on-premises system, propelling enterprises to provision and pay for the peak load and data under management, leaving them with hefty bills as datasets grow. Organizations then started to dump all their unstructured data into data lakes, a schema-on-read architecture that stores data in any format at a low cost. But quality and governance become an issue when volumes and variety of raw data increase.

In the era of big data, businesses realized the potential of untapped data. They started focusing on unlocking the potential value hidden within unstructured data – messy, raw information gathered from social media posts, sensor readings, website surveys, call center interactions, email interactions, etc. It could help them understand customer behavior, predict trends and optimize operations. However, the traditional data storage models weren’t a good fit to keep pace with this data deluge.

This led to the development of cloud providers like Amazon Web Services (AWS) offering data lake solutions as a cheap repository for raw data. This data is copied and loaded to a separate relational cloud data warehouse for analysis, which then feeds it to OLAP cubes or business intelligence (BI) tools like Tableau or Power BI. As a result, the same data is stored in two places: data lakes in its raw form and processed and used for analysis from the cloud data warehouse. This duplication led to several challenges:

• Storing the same data twice becomes expensive.
• The time taken to process and transfer data to the warehouse often makes it stale compared to the data lake.
• Analysts can never have access to complete data stored in the lake and ultimately, they have no choice but to ask questions based on the limited data subset stored in the warehouse.
• Managing and scaling separate systems raises complexity and potential security risks.
• Given these drawbacks, organizations need a solution that can provide the benefits of both a data lake and an analytics warehouse so that they don’t have to store data twice.

Given these drawbacks, organizations need a solution that can provide the benefits of both a data lake and an analytics warehouse so that they don’t have to store data twice.

The Rise of Data Lakehouse

In 2020, Ben Lorica, Ali Ghodsi, Reynold Xin, Matei Zaharia and Michael Armbrust, introduced the concept of data lakehouse. It is a cost-effective data management system that stores massive volumes of data. A lakehouse incorporates the best of both worlds: the structure and performance of a data warehouse with the flexibility and scalability of a data lake. Apart from this, the architecture also includes features like:

• High-speed, scalable query engines to directly query the raw data stored in the data lake.
• ANSI standard SQL interface to make it compatible with most analytics tools.
• ACID transactions to ensure data consistency and integrity during updates.
• Versioning to keep track of changes made over time.

Because of these features, data lakehouse architecture is a better approach than traditional data warehouses and data lakes in terms of flexibility, scalability and cost-effectiveness without data duplication. But every coin has two sides; upon closer inspection, proponents concluded that data lakehouse also has some limitations:

• Inconsistent query performance.
• Absence of a user-friendly semantic layer that abstracts the physical complexities of data structure.
• The absence of a single version of truth generates data silos in BI layers and becomes a data security and governance nightmare.

If organizations truly want to embrace data lakehouse, they need to implement a semantic layer in their architecture.

Need for a Universal Semantic Layer

Business users of large organizations often use multiple BI tools to analyze data, as each business unit or group prefers having its own version of the truth. Their data is scattered across various data sources and is externally cached in reporting tools, which increases data movement and produces conflicting results due to siloed copies. This becomes a data governance nightmare as organizations can’t prevent unauthorized data access, even from the people inside the firewall. Therefore, imposing a single standard for consuming and driving analytics becomes challenging.

Organizations can overcome this challenge by adding a semantic layer to their architecture to bridge the gaps in functionality that data lakehouses currently possess. Here is what the semantic layer can do for organizations:

Provides a single version of truth: The semantic layer defines business terms and their relationships in one place and provides a unified view of data scattered across various systems.

Simplifies data pipelines by eliminating the need for multiple data copies: A semantic layer sits on top of the data lake to translate queries into a format the data lake understands. It allows analysts and business users to access raw data directly, simplifying data pipelines and eliminating the need for duplication.

Offers a centralized point for defining data access, security policies and data quality rules: The semantic layer acts as a gatekeeper to deliver consistent governance and enforcement across all data sources. It can mask sensitive data and provide role-based access control to enhance data security even from users inside the firewall.

Reduces operational complexity by keeping the data in the data lake: With a semantic layer in their architecture, organizations don’t need to manage multiple data stores, reducing operational complexity. Instead, they can leverage the scalability and cost-effectiveness of the data lake for all their needs.

Modern enterprises need a data management solution that can combine the benefits of a data lakehouse and a semantic layer. Introducing the emerging concept that can kill two birds with one stone: semantic lakehouse.

What is a Semantic Lakehouse?

A semantic lakehouse is fundamentally a data lakehouse augmented with a semantic layer, a perfect cure for fragmented data and a lack of centralized control. It acts as a unified data platform that combines the scalability and cost-efficiency of a data lake with the well-defined structure and governance capabilities of a semantic layer. By enforcing data quality standards and access controls, semantic layer ensures data integrity while facilitating secure, compliant data usage across the organization. This way, organizations can empower enterprise-wide users to leverage data efficiently, gain deeper insights for informed decision making.

Adding semantic layer on top of a data lakehouse can help organizations eliminate the need to include data warehouse altogether in modern data architecture. Kyvos’ Gen AI-powered semantic layer with Databricks lakehouse platform can be a perfect solution to support all of the organization’s data, analytics and AI workloads. It can be extended to any visualization layer, regardless of the BI tool.

Databricks compute engine is very powerful, while Kyvos has the inbuilt capabilities to build data models on extremely large and complex datasets. Working together, they enable enterprises to quickly aggregate all of their data and build data models on the cloud. As these data models are not limited by the size of data or level of granularity, users can get interactive responses on a previously unimaginable scale.

Kyvos also seamlessly integrates with popular data lake platforms such as Amazon S3, ADLS Gen 2, GCP and many more. The platform can further deliver the following benefits:

Democratized data access

Kyvos centralizes and standardizes data logic in one place so domain users across the enterprise get a consistent view of all the data. With its ability to create data models, Kyvos’ distributed scale-out architecture combines domain-level data products into an AI-powered semantic layer while enabling seamless query execution on raw and aggregated data. It empowers organizations with limitless scalability to store as much data as needed and deliver consistently high-speed querying without compromising on performance even for higher concurrency, data volumes and complexities.

Instant insights on billion-scale datasets

Kyvos’ revolutionary AI-powered smart aggregation approach enables intelligent processing of all combinations in advance and stores the resultant aggregates in the cloud. Queries become lightweight, so the dashboard is refreshed in sub-seconds when a user fires a query. Additionally, the platform provides a unified repository called an analytical data store for both raw and aggregated data, eliminating the need to duplicate data and maintain separate data warehouses.

Improved data security and governance

Kyvos offers a three-tiered security model while supporting standard frameworks and protocols. It integrates with enterprise security infrastructures along with support for external authentication and single sign-on. The platform offers role-based access control (RBAC) at user and group levels and allows easy plug-ins for third-party encryption tools to ensure data security at all layers. Kyvos also offers powerful security, whether data is in transit or at rest, along with advanced custom security configurations.

Reduced compute cost

Kyvos offers a price-performant querying approach, where it processes all the possible calculations in advance and creates data models that are stored in the cloud. As a result, user queries don’t undergo a huge amount of heavy processing at run time. Minimal resources are consumed per query, enabling users to run unlimited queries on a massive amount of data without incurring additional costs. To further reduce computing costs, Kyvos’ schedule cluster scaling allows users to scale up or down querying capability or increase and decrease query engines depending on the load.

A semantic lakehouse architecture with Kyvos’ Gen AI-powered semantic layer can deliver a streamlined approach to data management, empowering quick data accessibility. The platform can help organizations unlock the true potential of the semantic lakehouse architecture and leverage it for actionable insights. As data continues to be the cornerstone of modern businesses, they provide a robust foundation for data-driven success.

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