Modern businesses are generating and leveraging more data than ever before, with 97% of organizations reportedly investing in big data and AI initiatives to drive decision-making. At the same time, data teams are under pressure to manage costs, as 81% of IT leaders have reported mandates to reduce cloud spending. This situation presents a significant challenge: how to balance the need for powerful data platforms with cost-efficiency.

With the evolution of data storage options—data warehouses, data lakes, and the emerging data lakehouse—organizations now have more choices to meet their unique data needs. But how do these storage options differ, and why is a data lakehouse becoming a popular choice for organizations? 

Let’s break down what a data lakehouse is, how it compares to traditional data warehouses and data lakes, and why it could be the right solution for your business. 

What is a Data Lakehouse? 

A data lakehouse is a hybrid data platform that merges the best of data warehouses and data lakes to address their limitations. While data warehouses handle structured data efficiently for business intelligence (BI) and reporting, and data lakes store vast amounts of raw, unstructured data for machine learning (ML) and analytics, a lakehouse combines both in a unified architecture. This allows businesses to store, manage, and process diverse data types—structured, semi-structured, and unstructured—within a single platform. 

Data Lakehouse vs. Data Lake vs. Data Warehouse 

Before we dive into the specifics of a data lakehouse, it’s essential to understand how it differs from traditional data lakes and data warehouses, both of which have played significant roles in modern data architecture.

• Data Warehouse 

A data warehouse is a centralized repository that stores structured data processed through ETL (Extract, Transform, Load) operations. It is designed for high-performance SQL-based analytics and used for business intelligence (BI) and reporting purposes. However, while data warehouses provide fast querying for structured data, they are less flexible in handling unstructured or semi-structured data. They also tend to be expensive when dealing with large-scale datasets, as they require data to be pre-processed and stored in a structured format. 

• Data Lake 

On the other hand, a data lake is a storage repository that can hold large amounts of raw, unprocessed data—both structured and unstructured. Data lakes are highly flexible and are often used for advanced analytics, machine learning, and AI-driven applications. However, they come with challenges related to data quality, governance, and performance. Without proper management, data lakes can become “data swamps”—repositories of poorly organized data that are difficult to navigate and extract insights from. 

• Data Lakehouse 

The data lakehouse combines the best features of both data warehouses and data lakes. It provides the low-cost storage and flexibility of a data lake, while also incorporating the structured data management and ACID transactions (Atomicity, Consistency, Isolation, Durability) of a data warehouse. This hybrid approach allows businesses to store diverse data types and run a wide range of workloads, from business intelligence to machine learning, all within the same platform. 

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured data	Structured, semi-structured, unstructured	All data types
Data Processing	ETL (schema-on-write)	Schema-on-read	Schema-on-read and schema-on-write
Cost	High	Low	Moderate
Workloads	BI, Reporting	Advanced analytics, ML	BI, ML, Advanced analytics
Transaction Support	ACID-compliant	Limited or none	ACID-compliant
Scalability	Limited	High	High
Governance	Strong	Limited	Strong

Key Features of a Data Lakehouse 

The data lakehouse is packed with features that address the needs of modern organizations, helping them efficiently manage and extract value from their data: 

Feature	Description
Single Data Platform	Offers a unified platform for storing structured, semi-structured, and unstructured data. This eliminates the necessity of having distinct architectures for different data types, thus reducing complexity and cost.
Open Data Architecture	Stores data in formats like Apache Parquet and ORC, enabling data teams to process data using different tools and engines. This prevents reliance on a single vendor and enhances interoperability with other systems.
Transactional Support	Similar to conventional databases, data lakehouses supports ACID transactions (Atomicity, Consistency, Isolation, Durability). This guarantees that data stays accurate and dependable, even when multiple users are accessing or updating data at the same time.
Decoupled Storage and Processing	Enables individual scaling of storage and processing resources and offers cost savings and scalability advantages. This is especially useful for extensive datasets when you aim to reduce the over-provisioning of resources
Governance and Data Quality	Maintains governance policies and data quality standards to guarantee reliable, secure, and GDPR-compliant data. This is done by utilizing functions such as schema validation, auditing, and real-time monitoring.
High-Performance Querying	Enhances query performance by leveraging columnar file formats (such as Parquet) and indexing methods. This enables quick searches through extensive datasets, matching the pace of traditional data warehouses.
Integration with BI Tools	By providing direct access to data, it simplifies the integration with business intelligence tools, thus reducing the reliance for time-consuming ETL processes.

How Does a Data Lakehouse Work? 

This single platform is designed to address the limitations of both data lakes and data warehouses by combining their key benefits. This architecture enables businesses to store, manage, and analyze vast amounts of data—whether structured, semi-structured, or unstructured—more effectively and at a lower cost. Here’s how it works:  

• Data Sources

Data in modern enterprises comes from multiple sources: enterprise applications, databases, IoT devices, mobile apps, and more. These sources feed into the lakehouse’s storage layers, capturing both structured and unstructured data in its raw form.

• Ingestion into the Data Lake

Once data is ingested, it is stored in different layers of the data lake based on the level of processing and refinement: 

Bronze Layer: Raw, unprocessed data is stored here. This is the first step of data ingestion, where all incoming data is saved in its original format. It serves as a reservoir for all raw data, which can be queried or transformed later. 

Silver Layer: Here, data is cleaned and transformed. It’s structured for better accessibility and analytics, preparing it for advanced use cases like machine learning or business reporting. 

Gold Layer: Data in this layer is highly refined and structured, often stored in a star schema format, which makes it easy to query and use in data warehouses. This is where clean, verified, and processed data is stored for high-performance business intelligence queries. 

• Machine Learning & Real-Time Analytics

After data has been processed in the silver layer, it can be used in machine learning models and real-time analytics. In this phase, models are trained, tested, and refined using structured and semi-structured data. The advantage of the data lakehouse is that this machine learning workload is directly integrated with the raw data, minimizing the need for moving data between different systems. 

• Compute and Data Processing

The compute layer in the data lakehouse is decoupled from storage, allowing organizations to scale compute and storage independently. This enables parallel processing, which boosts the performance of tasks such as querying and real-time data processing. By separating storage and compute, businesses can handle large datasets without worrying about over-provisioning or underutilizing resources, ensuring cost efficiency. 

• Data Consumption by End Users

Once the data has been processed and refined, it is ready for consumption by various teams within the organization. These users include: 

• Data Scientists who run complex machine learning models.
• Marketing Automation Teams that utilize customer data to optimize campaigns.
• Process Automation systems that use data for optimizing business operations.
• External Sharing platforms, where data can be shared with partners or other external stakeholders.
• Business Analysts who use the data for BI dashboards and reporting.

By using a data lakehouse, organizations can ensure that all users have access to high-quality, processed data for various use cases—whether it’s real-time analytics, business reporting, or machine learning. 

Why Do Organizations Need a Data Lakehouse? 

Here are the reasons why organizations need a data lakehouse: 

• Comprehensive Data Storage

Provide a unified environment for storing and processing structured, semi-structured, and unstructured data. This streamlines processes and guarantees equal data access for all teams, improving collaboration. 

• Reduces Data Silos 

Organizations frequently encounter fragmented data environments due to usage of an average of 976 applications for tracking customer data. Data lakehouse merges data in one place, eliminating silos and enabling teams from different departments to work with same information. 

• Enhances Operational Efficiency

Improves operational efficiency by offering a unified, scalable solution capable of managing large volumes of data without requiring additional computing power, ultimately leading to cost savings and enhanced efficiency for businesses.  

• Supports Advanced Analytics

Enables businesses to make more informed decisions through predictive analytics and personalization. Marketers have the ability to divide customers into segments and target them, while analysts can evaluate campaign performance and enhance marketing strategies using real-time data. 

• Simplifying data governance

One of the major challenges when managing data across multiple systems is maintaining data governance and ensuring compliance with industry regulations. Data lakehouse provides integrated governance tools to guarantee data integrity, security, and quality, enabling organizations to adhere to compliance standards and regulatory requirements with greater ease. 

• Improves Customer Insights:  

Allows businesses to achieve a 360-degree view of their customers by consolidating data from various interactions, forming a thorough profile. This aids companies in gaining a deeper understanding of customer actions and improving personalization, risk management, and fraud detection. 

Conclusion 

The data lakehouse is an innovative architecture that solves many of the challenges posed by both data lakes and data warehouses. It provides a unified, cost-efficient solution for managing all types of data, supporting both analytics and machine learning in a single platform. By combining the flexibility of a data lake with the governance and performance of a data warehouse, the data lakehouse enables businesses to make data-driven decisions faster and more efficiently. 

Ready to get the most out of your data? Contact KaarTech to learn how our team and technology partners can help you build a data lakehouse that meets your business needs. We’ll help you speed up implementation, reduce costs, and simplify the management of your data. Let us guide you in creating a solution that improves your data strategy and supports better decision-making. Reach out to us today! 

 

 

FAQ’s 

What is a Data Lakehouse? 

A data lakehouse is a unified platform that combines the capabilities of both data lakes and data warehouses, allowing organizations to store and process all types of data—structured, semi-structured, and unstructured—for analytics and machine learning. 

What are the differences between a Data Warehouse, Data Lake, and Lakehouse? 

A data warehouse is optimized for structured data and BI reporting, a data lake stores raw, unprocessed data for advanced analytics, while a lakehouse integrates both approaches, supporting diverse data types with added flexibility and governance. 

Why do businesses adopt a Lakehouse architecture? 

Businesses use this architecture to manage various data types efficiently, break down data silos, improve operational efficiency, and enable advanced analytics, all while reducing costs and maintaining scalability. 

How does this architecture enhance data management? 

It improves data management by offering strong governance, high-performance querying, and ACID transactions, ensuring data quality and security while enabling machine learning and real-time analytics without moving data between systems.