There is a new concept in data architecture called a data lakehouse that combines the features of data lakes and data warehouses. By providing a more integrated and scalable solution for managing and analyzing data, data lakehouses are a modern attempt to address the limitations of both traditional data warehouses and data lakes.
What is a Data Lakehouse?
A data lakehouse is a modern data architecture with the scalability and flexibility of data lakes and the performance and reliability of data warehouses. With a data lakehouse, you can store raw and structured data in a single platform while supporting real-time analytics, advanced machine learning workflows, and business intelligence tools.
A data lakehouse also guarantees consistency and reliability, making it a powerful solution for diverse data workloads. It achieves this by integrating schema enforcement and ACID transaction support.
How Does a Data Lakehouse Work?
A data lakehouse can unify the strengths of data lakes and data warehouses. It works by using low-cost cloud object storage for flexible data management, enabling you to store structured, semi-structured, and unstructured data.
With features like compute-storage separation and transactional support, the data lakehouse architecture also provides efficient querying, analysis, and data processing.
Your organization can scale compute resources independently and take advantage of schema enforcement to maintain data integrity across multiple workflows.
The Architecture of a Data Lakehouse
The architecture of a data lakehouse incorporates various elements. Here are 5 of the main aspects:
- Unified Storage Layer: A data lakehouse stores all data types—structured, semi-structured, and unstructured—on a single platform.
- Compute and Storage Separation: It offers scalability and cost efficiency by decoupling compute resources from storage.
- Transactional Integrity: It ensures consistency with ACID-compliant transactions.
- Schema Enforcement: It enforces data structure at read or write operations for accuracy and reliability.
- Metadata Management: It simplifies easy data discovery, governance, and faster query processing.
Key Components of a Data Lakehouse
What separates data lakehouses from other types of data management frameworks is their use of layers when injecting and organizing data. Every layer serves a particular purpose and can be accessed by combining the appropriate tools to create a hybrid and efficient data retrieval and management system. Data lakehouse architecture consists of five layers:
- Ingestion layer: This part of the data lakehouse architecture is used for data extraction. This data can come from many sources, such as transactional, relational, and NoSQL databases, data streams, and APIs.
- Storage layer: The data is then stored to be further processed should the need arise. However, the person managing the database can pull data specifically from this layer.
- Metadata layer: A defining element of a data lakehouse, data coming to this layer can further be cached, zero-copy cloned, indexed, and extracted, be it through ACID transactions or other means. This layer allows for schema enforcing and declining any data that doesn’t fit, making it similar to the ETL process in warehouses.
- API layer: It allows applications to interface with the lakehouse. Here lie various interfaces that allow the user to connect outside applications to the lakehouse to more efficiently query data.
- Data consumption layer: The user-friendly layer of the lakehouse includes tools and applications that communicate with the database using the above mentioned APIs, such as business analytics tools or machine learning frameworks.
The Evolution from Data Lakes to Data Lakehouses
Data lakes revolutionized data storage by offering a repository for raw and unstructured data. However, their lack of transactional support and governance posed challenges for analytics and business intelligence.
In contrast, data warehouses excel at structured data processing yet struggle with scalability and cost-effectiveness when handling large, diverse datasets.
The data lakehouse evolved to address these limitations by offering the flexibility of data lakes while incorporating the reliability, schema enforcement, and performance of data warehouses.
Why More Businesses Are Adopting the Data Lakehouse Model
The data lakehouse model bridges the gap between raw data storage and actionable insights.
For example, it reduces infrastructure costs through unified storage, enhances analytics with schema enforcement and metadata management, and accelerates AI/ML workflows with high-performance querying and processing.
10 Main Benefits of a Data Lakehouse
The data lakehouse offers transformative benefits for modern enterprises and represents a paradigm shift for modern enterprises. That’s because it blends the features of data lakes and data warehouses.
These benefits drive operational efficiency, boost innovation, and improve decision-making across industries:
1. Faster Time to Insights
Data lakehouses excel at reducing query latency through their combination of real-time data processing and schema enforcement. This ensures rapid data access for quicker, data-driven decisions.
The speed of insight generation becomes a significant competitive advantage, whether for responding to market trends, operational adjustments, or customer behavior analysis.
2. Improved Data Governance
Data lakehouses offer robust governance frameworks that centralize control over data access, permissions, and usage policies.
This ensures adherence to regulatory standards like GDPR or HIPAA while reducing the risks of data breaches and non-compliance penalties. You can even streamline audits and maintain transparency in data operations.
3. Enhanced Data Quality and Reliability
Data lakehouses provide trustworthy data storage and processing if you incorporate Atomicity, Consistency, Isolation, Durability (ACID). compliance and schema enforcement. This integrity minimizes errors, ensures consistency, and develops confidence in the insights derived from the data.
4. Advanced Analytics Capabilities
Data lakehouses are purpose-built to support a broad spectrum of analytical workloads. They enable integration with business intelligence (BI) tools, facilitate predictive analytics, and provide robust environments for artificial intelligence (AI) and machine learning (ML) workflows.
5. Better Support for AI/ML Workflows
One of the standout features of a data lakehouse is its ability to handle diverse data types—including raw, semi-structured, and structured formats.
This versatility makes it an ideal environment for training machine learning models, conducting exploratory data analysis, and running complex algorithms at scale. In addition, data scientists and engineers can work within a unified platform without moving data across systems.
6. Simplified Schema Management
Dynamic schema application in data lakehouses ensures you can process and analyze data without sacrificing structural integrity. Therefore, it supports your changing business needs, integrates new data sources seamlessly, or adjusts schemas to meet emerging requirements.
7. Cost-Effectiveness
The separation of computing and storage, mixed with low-cost cloud object storage, makes data lakehouses a more economical choice than traditional data systems. You can scale resources as needed, eliminating the expense of maintaining over-provisioned infrastructure.
Furthermore, putting data processing and storage functions into a single platform reduces overhead and operational complexity.
8. Scalability and Flexibility
Data lakehouses will handle vast volumes of data and support diverse workloads. This scalability accommodates growing data needs without compromising performance. Moreover, the flexibility to process various data formats with tools and platforms ensures the data lakehouse adapts to changing technological landscapes.
9. Unified Data Platform
The data lakehouse eliminates silos by combining the capabilities of a data lake and a data warehouse. As a result, your teams across departments—analytics, operations, or R&D—can access a single source of truth to enhance collaboration and alignment on strategic goals.
10. Support for Hybrid and Multi-Cloud Environments
Data lakehouses are often cloud-native, enabling seamless integration with hybrid or multi-cloud architectures. They also give you the benefits of diverse cloud providers while maintaining centralized control and minimizing latency for globally distributed operations.
How Data Lakehouses Differ from Data Lakes and Data Warehouses
Although defined as an amalgamation of a data lake and a data warehouse, the data lakehouse architecture differs slightly from its predecessors, integrating both approaches into something new.
The data warehouse method is highly organized. The data going in must be of a certain make and structure, and on entry, it is sequestered into the correct denominator, by which it can be sorted later. This allows for a tight-knit dataset that does not offer any variety other than what is inputted. This allows for easier data extraction by users, but it limits the scope when using that data for statistics or machine learning.
A data lake on the other hand prioritizes storage over structure. The types of data that can be found in a data lake can be both unstructured and structured, allowing a wider variety of data contained in a set. This is beneficial to AI training as well as business analytics which need as much data as possible to make a more detailed result.
A data lakehouse architecture layers these two approaches. The data ingestion and storage layers of a data lakehouse function like a data lake, while the metadata layer is effectively a warehouse down to the ETL protocol it applies to the ingested and stored data. A data lakehouse differs from both because all the layers in it can be accessed, and data can be pulled from them. This offers a good balance of data diversity and structure.
When to Use a Data Lakehouse Over Other Architectures
A data lakehouse is the best choice when you need a solution that balances flexibility and performance.
However, it’s especially beneficial for organizations handling diverse data types—structured, semi-structured, and unstructured—while demanding real-time analytics capabilities.
Unlike traditional architectures, a data lakehouse allows for independent scaling of compute and storage resources, which ensures cost efficiency.
Furthermore, industries leveraging AI and machine learning workflows find data lakehouses indispensable. The architecture supports raw and processed data, so it’s ideal for both exploratory analysis and model training.
Popular Data Lakehouse Tools
The most popular data lakehouse tool is Delta Lake. It’s an open-source software and table format that acts as a storage layer and allows users to build a data lakehouse using said software as a base. This format builds on Apache Spark, another popular framework, and provides ACID transactions, schema evolution, and transactional consistency.
While not open source and operating under the Apache License, Apache Iceberg is a more high-performance open table format. Due to this close-knit license, it’s available to more data processing tools, offering a more bespoke experience. It is much simpler and friendlier to use, bringing the simplicity of SQL queries to big data.
Boomi Data Integration offers an even easier-to-use and tailor-made experience, supporting over 200 different connectors, all with their already-integrated protocols. This software is also a closed system, but it almost completely eliminates the use of APIs in the protocol, resulting in high-performance and efficient data transfers.
Challenges and Considerations in Adopting Data Lakehouse
Data lakehouses are a very new framework, and many people who work in data management or even software development are still unfamiliar with it. This can delay the implementation of this technology and its eventual testing to iron out the bugs.
Data engineers are also wary of completely moving their databases to this new system, as new skills may need to be mastered to operate a data lakehouse effectively, and new protocols could potentially cause them to lose precious data.
Industry Use Cases for Data Lakehouses
Data lakehouses are transformative across various industries due to their versatility. In financial services, they enable real-time fraud detection and risk analysis for quick responses to market changes.
Likewise, the healthcare sector benefits from accelerated genomic analysis and effective patient data management. This paves the way for personalized medicine.
Retail and e-commerce leverage data lakehouses for enhancing recommendation systems and optimizing inventory management.
Similarly, in manufacturing, they power real-time equipment monitoring and predictive maintenance, boosting operational efficiency.
Future Trends in Data Lakehouse
The future of data lakehouses will revolve around increased interoperability, which will allow seamless integration with third-party analytics and AI tools. As businesses demand faster insights, advancements in real-time processing will drive innovation in data lakehouse architectures.
Open standards adoption is expected to rise, which will provide compatibility across platforms and tools.
Enhanced governance capabilities—including robust data lineage tracking and compliance measures—will become critical when organizations navigate complex regulatory environments.
Additionally, data lakehouses will play a pivotal role in scaling AI workflows and supporting increasingly sophisticated machine-learning models.
While it is a new data management architecture, the data lakehouse is already proving itself as the leading framework in the data industry. Combining both the aspects of data warehouses and data lakes and building on them has made this open table format a force to be reckoned with when it comes to performance, scalability, and data extraction.