What Is a Data Lakehouse?

What Is a Data Lakehouse?

A data lakehouse is a data analytics architecture that combines the flexibility of data lakes, which are great for storing and analyzing unstructured data, with the management features and predictability of traditional data warehouses. They do this by blending storage, governance, and compute in an architecture that supports structured, semistructured, and unstructured data as well as various types of workloads, such as data engineering, AI/ML, and analytics, while also offering data persistence guarantees.

Consider a data lakehouse when your business needs to unify structured and unstructured data to enable collaborative analytics and support scalable AI and machine learning use cases.

Key Takeaways

• Data lakehouses enable data analytics on large, varied data sets by combining the flexibility of data lakes with the performance and data management features of data warehouses.
• AI models and agents often rely on real-time access to data lakehouses to provide up-to-date corporate information in their outputs.
• Data lakehouses support structured, semistructured, and unstructured data in many different formats and are most often built using cost-effective cloud storage.

Data Lakehouse Explained

A data lakehouse is a data architecture that uses open, columnar file formats, like Parquet, and open table formats, such as Apache Iceberg, Delta Lake, or Hudi, to allow organizations to store large amounts of data in open formats, directly on object storage. Lakehouses also provide the tools teams need to cleanse data, enforce schemas, and support ACID transactions.

Because data lakehouses can serve a wide variety of data uses, including business intelligence (BI), machine learning and AI, and real-time analytics, from a single platform, they minimize the need to duplicate and move data.

Data Lakehouse vs. Data Lake vs. Data Warehouse

A data lake, the “lake” in lakehouse, is a low-cost storage repository used primarily by data engineers, but also by business analysts and other types of end users. The data lake stores all types of raw data from various sources and makes it available to be explored in original formats.

A data warehouse, the “house” in lakehouse, is different from a data lake in that a data warehouse stores processed and structured and semistructured data, curated for a specific purpose, in a specified format. This data is typically queried by businesspeople, who use the prepared data in analytics tools for reporting and predictions. More recently, large language models (LLMs) that can write SQL code are enabling natural language queries to be executed in some data warehouses.

A data lakehouse combines these two architectures. Now, data can be easily moved between the low-cost and flexible storage of a data lake over to a data warehouse and vice versa. All the raw data in a company’s data lake is now accessible to the data warehouse management tools used for implementing schema and governance and to the machine learning and artificial intelligence systems used to extract intelligence from data.

The result is a data repository that integrates the affordable, unstructured nature of data lakes with the robust preparedness of a data warehouse.

How Do Data Lakehouses Work?

Traditional data warehouses have long provided the reliability and performance companies need for reporting on and analyzing current and historical data, but they can struggle with scale and complexity when faced with unstructured content from diverse sources. Data lakes, in contrast, offer affordable storage for a multitude of data types but can lack the governance, consistency, and performance that enterprise analytics demand.

The data lakehouse model overcomes these limitations by introducing a transactional storage layer atop the data lake, which often resides in bulk cloud storage. This model lets companies process massive data sets efficiently while helping enforce consistency, ensure data integrity, and enable schema evolution. AI models and AI agents further enhance lakehouse environments by automating data cataloging, anomaly detection, and dynamic resource allocation, all while preserving data quality and enforcing compliance rules.

Capabilities of Data Lakehouses

Data lakehouses can act as the ultimate repository of enterprise data and facilitate analysis of all sorts. Here are some capabilities you’ll find.

• Accessible, consistent data can facilitate AI model development, training, and deployment.
• Centralized data security and access controls reduce the risk of data loss or exposure and provide for policy and regulatory compliance.
• Petabyte-scale storage and vast compute resources mean data lakehouses can accommodate high-growth enterprises with massive volumes of data and demanding use cases.
• Support for both structured and unstructured data enables diverse use cases across analytics, AI, and reporting.
• Support for reliable and consistent transactions make data lakehouses suitable for demanding operational and analytical workloads.
• A unified architecture minimizes data duplication by eliminating the need for separate storage for different analytics environments.

Benefits of Data Lakehouses

By acting as the primary storage and analytics systems for very large data sets, data lakehouses support advanced analytics, real-time decision-making, and AI model development, all while simplifying data infrastructure and governance.

Specifically, data lakehouses:

• Combine the flexibility of data lakes with the performance of warehouses. The data lakehouse architecture melds the flexibility of data lake object storage with the performance of a data warehouse. This allows organizations to handle both raw, unprocessed data and structured data within a single platform and support diverse analytics workloads.
• Enable real-time and batch processing. Data lakehouses support both streaming and batch data processing, helping organizations derive timely insights through real-time analytics while also accommodating diverse, in-depth processing needs on a single platform.
• Enhance analytics and machine learning integration. The data lakehouse architecture lets data scientists and analysts work with diverse data types. That allows for the development of AI-driven applications that often derive deeper and more accurate insights from accumulated data.
• Provide strong governance and security. A cloud-based data lakehouse provides robust data governance via encryption, access control, and auditing. This framework helps maintain regulatory compliance, protect sensitive data, and maintain stakeholder trust
• Reduce data duplication and movement. By providing a single repository for all data types, a lakehouse eliminates the need for multiple, separate data repositories. This reduces data duplication, helps reduce storage costs, and simplifies data management and maintenance, making it easier to maintain data consistency and accuracy.
• Scale cost effectively with cloud storage. Because the data lakehouse architecture is designed for cloud storage, which provides for elastic scaling to accommodate changing data volumes, organizations can achieve scalability. Cloud providers also help balance storage costs through tiering and lifecycle management.
• Support structured, semistructured, and unstructured data. One significant advantage of a data lakehouse is its ability to house virtually any data type—structured, semistructured, and unstructured. This lets organizations manage data from diverse sources, such as IoT devices, log files, transactional systems, and user feedback systems, and perform comprehensive analytics involving many data types.

Challenges of Data Lakehouses

A well-run data lakehouse adds value to any large, modern data analytics operation. But it does require planning, funding, time, and tuning. Here’s a look at the challenges of building and maintaining a data lakehouse.

• Complexity: A data lakehouse introduces new architectural layers that require planning and ongoing management. Achieving the benefits of both transactional consistency and scalable storage while integrating multiple analytics tools demands careful design and a well-defined governance model. All this adds up to complexity.
• Data quality issues: If data pipelines aren’t well managed, that can introduce inconsistencies and reduce the accuracy of, and trust in, analytics outputs. Automated validation tools and well-documented processes are key to preserving high data quality during the ingestion and transformation steps.
• Performance tuning requirements: Without careful optimization, running large-scale analytics across diverse data sets can introduce bottlenecks. Continuous tuning and monitoring are necessary to provide efficient processing and minimize latency, especially as data volumes grow and/or if the data lakehouse is spread over more than one cloud location or involves multiple providers.
• Providing consistent governance across diverse data types: Supporting both structured and unstructured data complicates access control, metadata management, and data lineage tracking. Maintaining consistent governance requires a unified policy framework and careful automation, including the integration of AI agents where appropriate.
• Security risks with mixed structured and unstructured data. Combining multiple data types heightens the importance of a comprehensive data protection strategy. Sensitive information can be harder to track and protect without strong access controls and continuous monitoring—fortunately, these are areas where AI-enabled solutions can provide support.
• Significant setup and integration: Implementing a lakehouse typically involves a large upfront investment in design, data migration, and integration with current data and analytics platforms. Organizations must also design new workflows, a task that often involves cross-functional teams and extensive testing.
• Skill gaps: Transitioning to a lakehouse architecture may require teams to acquire new expertise in data engineering, cloud services, and AI tooling. Budgeting time and money for training, upskilling, and cross-team collaboration are important for smooth adoption and ongoing operational success.

Data Lakehouse Architecture Layers

Constructing a data lakehouse that reliably delivers answers and predictions requires a mix of technologies at different layers of the tech stack. While cloud providers abstract much of this complexity, organizations do need to understand the mechanics.

1. Ingestion layer: The ingestion layer serves as the entry point for a data lakehouse, capturing data in real time or in batches from operational databases, streaming platforms, and other sources via APIs. An effective ingestion layer will support a range of formats and be able to pull raw data into the lakehouse securely and reliably.
2. Storage layer: At the core of the lakehouse, the storage layer provides scalable and cost-effective data storage. It handles structured tables, semistructured logs, and unstructured documents or media, creating a unified data foundation for analytics and AI applications.
3. Metadata and catalog layer: This layer maintains the essential context of the stored data. Catalogs register schemas, manage data lineage for transparency, and index data and files for rapid discovery and efficient query performance. AI agents can automate catalog updates and monitor metadata integrity.
4. Processing layer: The processing layer orchestrates data preparation, including cleansing, normalization, and transformation. It supports batch and real-time workloads and is where AI and machine learning pipelines are built or executed.
5. Governance and security layer: Governance and security span all layers of the architecture, enforcing data privacy, regulatory compliance, access control, and auditability. This layer manages access policies, tracks data lineage, and applies automated encryption and monitoring to safeguard sensitive information.
6. Serving layer: This layer focuses on making analytics-ready data available for downstream consumption. It optimizes data sets for performance and reliability, serving requests from business intelligence platforms, operational dashboards, and AI modeling frameworks.
7. Consumption layer: The consumption layer offers intuitive access to curated data for end users. Analysts, data scientists, and AI agents use this interface to generate reports, build visualizations, and construct predictive models—fueling actionable insights across the organization.

Data Lakehouse Use Cases

A data lakehouse is useful for any organization that wants to derive real-time, AI-driven insights from a large and diverse data set, as you’ll see in these real-world examples.

• Global retail: A powerful data lakehouse enables one global retailer to immediately connect in-store point-of-sale data to performance analytics and product inventory systems, making the information available to retail staff and corporate analysts. The lakehouse also strengthens the retailer’s delivery of online orders and allows customers to check orders and stock availability in near real time.
• Higher education: A data lakehouse enables this university to gather data from various sources supporting student admissions, recruitment, enrollment, and student progress and make insights available to a wide range of faculty and staff. The system allowed them to, for example, predict admissions offer acceptance rates to better plan future admissions cycles.
• Online selling: A dynamic online seller uses a data lakehouse to improve its ecommerce platform. The lakehouse architecture enables not just inventory and shipping logistics but allows brands to share targeted content with buyers and use AI to understand and share user-generated content posted on its platform.
• Professional services: Bevi, the innovative designer of smart water dispensers for commercial spaces, needed to replace its manual, disconnected field service process to support rapid growth.

Build Your Lakehouse on OCI

When you build your enterprise data lakehouse on Oracle AI Data Platform, you’ll be able to integrate and analyze data from diverse sources and quickly apply the latest AI innovations. The platform provides a potent mix of enterprise reliability and governance and open source flexibility. For example, you get native integration with Oracle Autonomous Data Warehouse, Oracle Analytics Cloud, and the OCI AI services portfolio on a platform that also supports open formats, such as Delta Lake, Parquet, and Iceberg. Drive your AI automation initiative with a comprehensive set of AI and ML services included in the data management system, including pretrained models that generate insights and predictions while helping to lower your operational overhead.

A data lakehouse equips organizations to harness the full value of their information in a scalable and governed fashion. With built-in support for analytics and AI—often driven by intelligent agents—lakehouses deliver performance, flexibility, and compliance for enterprise initiatives.

Data Lakehouse FAQs

What is the difference between a data lakehouse and a data warehouse?

A data warehouse is optimized for structured data and consistent reporting, while a data lakehouse combines those features with the scalability and flexibility of a data lake, supporting a broader range of data types and analytics.

What is the difference between a data lakehouse and a data mesh?

A data lakehouse is a unified technology architecture that blends the flexibility of data lakes with the management features of data warehouses, centralizing storage and analytics. A data mesh, on the other hand, is an organizational approach that emphasizes decentralized data ownership and domain-driven design, often leveraging distributed architectures; lakehouse platforms can be one component within a broader data mesh strategy.

Can a data lakehouse replace a data warehouse?

A data lakehouse can often replace a traditional data warehouse by providing structured storage, ACID transactions, and analytics capabilities in addition to handling semistructured and unstructured data. However, migration requirements and compatibility with legacy systems should be carefully evaluated before any transition.

How does AI play a role in a data lakehouse?

AI enhances data lakehouses by automating metadata management, ensuring data quality, and optimizing resources through intelligent monitoring and recommendations. In addition, a data lakehouse is an enabling technology for AI systems because they allow for real-time access to many types of data files.