What Is a Data Lakehouse?

A data lakehouse is a modern data management architecture that blends the flexibility and scalability of data lakes with the structured querying and data governance capabilities of data warehouses. This design allows for the storage and analysis of both structured and unstructured data on a single platform, making it possible to undertake a wide range of analytics tasks—from historical data analysis to real-time analytics and machine learning (ML).

Central to its approach is the use of low-cost storage options, combined with additional layers that enforce strict data governance, data quality, and reliability, ensuring efficient and fast query execution across vast datasets.

In this article, we’ll examine data lakehouses, including their features, functions, challenges, and more.

Data warehouses vs. data lakes vs. data lakehouses

Data warehouses are centralized repositories designed for structured data. They support efficient querying and analysis through highly organized, schema-on-write data. Data warehouses are widely used for business intelligence, reporting, and analytics, where consistency, governance, and performance are critical.

Data lakes store large volumes of raw data in its native format, including structured, semi-structured, and unstructured data. They are designed for flexibility and scalability, using a schema-on-read approach that allows data to be interpreted at the time of analysis. This makes them well suited for data exploration and advanced analytics, though they require careful management to maintain data quality and governance.

Data lakehouses combine elements of both architectures. They are designed to support multiple data types on a single platform while introducing more structure, governance, and performance optimization than traditional data lakes. This allows organizations to run analytics and machine learning workloads in a more unified and scalable way.

To make these differences clearer, the table below compares data warehouses, data lakes, and lakehouses across key decision-making dimensions.

Dimension	Data Warehouse	Data Lake	Data Lakehouse
Primary Focus	Curated analytics and BI on trusted datasets	Low-cost storage for raw, diverse data	Unified analytics and ML across all data types
Data Types	Structured	Structured, semi-structured, unstructured	Structured + semi/unstructured
Schema Approach	Schema-on-write	Schema-on-read	Hybrid (flexibility with added structure and controls)
Governance & Data Quality	Strong governance and consistency	Variable; requires active management	Improved governance with flexibility
Performance	Optimized for SQL and BI workloads	Variable depending on tooling	Designed for improved analytics performance on large-scale data
Cost Profile	Higher at scale	Lower storage cost	Balanced: lower storage with scalable compute

 

In practice, many organizations use a combination of these approaches depending on workload and requirements. The data lakehouse model has gained traction because it brings together flexibility, performance, and governance in a more unified architecture.

Features of a data lakehouse

A data lakehouse is characterized by several innovative features that combine the strengths of data lakes and data warehouses, creating a robust architecture for modern data management and analytics. These features address the challenges of managing vast amounts of data, ensuring data quality, and providing flexible, efficient access for analysis.

Key features include:

• Unified data storage. Data lakehouses utilize a single platform to store both structured and unstructured data. This unified approach simplifies data management, reducing the complexity and cost associated with maintaining separate systems for different data types.
• Scalable storage and compute. The architecture separates storage and compute layers, allowing each to scale independently. This scalability is critical for managing fluctuating workloads and growing data volumes, ensuring that performance remains consistent and cost-effective.
• ACID transactions: Data lakehouses support atomicity, consistency, isolation, and durability (ACID) transactions, a necessity for ensuring data integrity in multi-user environments. This feature allows multiple processes to read and write data concurrently without compromising its accuracy.
• Schema management and evolution. They provide tools for managing data schemas, allowing for the evolution of data structures over time without losing historical data compatibility. This flexibility supports the agile development of data models in response to changing business needs.
• Efficient data governance and quality control. Advanced metadata management and governance tools are integral to data lakehouse architectures. These tools enable organizations to enforce data quality, data security, and compliance standards across all stored data, ensuring that data is reliable and fit for purpose.
• Optimized query performance. By incorporating techniques like indexing, caching, and data clustering, data lakehouses improve query performance. These optimizations ensure that users can quickly access and analyze data, supporting both batch processing and real-time analytics workloads.
• Machine learning and advanced analytics support. The architecture is designed to support advanced analytics and machine learning directly on the stored data. This capability allows organizations to build and deploy predictive models and analytics applications on the same platform where their data resides, streamlining operations and enabling deeper insights.
• Cross-platform and multi-language support: Data lakehouses are built to work across different cloud platforms and support multiple programming languages and data analysis tools. This interoperability facilitates the integration of diverse data sources and tools, making it easier for teams to work with the data in their preferred environments.
• Cost-effectiveness. By leveraging low-cost cloud storage and only scaling compute resources as needed, data lakehouses offer a more cost-effective solution for data storage and analysis compared to traditional data warehouses.

Collectively, these features enable organizations to manage their data more efficiently, perform a broader range of analyses, and make data-driven decisions with confidence.

The advantages of data lakehouses

Data lakehouses offer several notable advantages to modern data management and analytics needs:

• They provide a unified platform for all data types, both structured and unstructured, ultimately simplifying data management and accessibility. This consolidation enables organizations to conduct a broader range of analyses and derive insights from their entire data estate without the need for multiple disparate systems.
• They ensure high data quality and governance. By incorporating structured querying capabilities and data management features of traditional data warehouses, they enforce data governance, quality, and reliability more effectively than data lakes alone.
• They offer scalability and performance benefits. Data lakehouses are designed to efficiently handle vast amounts of data, scaling storage and computing resources independently to meet demand. This scalability, paired with optimized query execution, ensures that organizations can manage large datasets and complex analytics workloads without compromising on performance.
• They’re cost-effective. Data lakehouses leverage low-cost cloud storage options while providing high-level analytics capabilities, offering a more economical solution for data storage and analysis compared to traditional data warehouses.
• They support ACID transactions and schema evolution, ensuring data integrity and flexibility and accommodating changes in data structure without significant overhead.

Data lakehouse architecture

The architecture of a data lakehouse is designed to bridge the gap between data lakes and data warehouses, incorporating elements of both to create a versatile data management platform. At its core, a data lakehouse uses a layered architecture that includes a storage layer, a metadata layer, and a processing layer:

• The storage layer relies on inexpensive, scalable cloud storage to house raw data in various formats. This foundation supports the immense scalability of a data lakehouse, allowing it to store petabytes of data across structured, semi-structured, and unstructured formats.
• The metadata layer works to manage data across the lakehouse. It provides a unified catalog of data assets, enforces schema definitions, and tracks changes to data structure over time. This layer enables the ACID transactions and schema evolution capabilities that are central to data integrity and flexibility.
• The processing layer is where data is analyzed and queried. It can scale compute resources independently of storage, allowing for efficient query execution and support for a wide range of analytics workloads, from batch processing to real-time analysis and machine learning.

Data lakehouse challenges

Despite their advantages, data lakehouses can pose some challenges. One significant challenge is the complexity of managing a hybrid data management platform that combines features of both data lakes and warehouses. Organizations must have the expertise to implement and maintain the advanced features of a data lakehouse, such as transactional support and schema management.

Another challenge is ensuring data governance and security in a platform that aims to be both flexible and open. Balancing accessibility with control requires robust data governance policies and tools to prevent data from becoming disorganized or exposed to unauthorized access.

Performance optimization can also be a challenge, particularly as data volumes grow and queries become more complex. Organizations need to carefully manage compute and storage resources to maintain high performance, which may involve tuning and optimizing data formats, query execution plans, and resource allocation.

Lastly, migrating from existing data platforms to a data lakehouse architecture can be complex and resource intensive. Organizations must plan and execute the migration of data, schemas, and analytics workloads carefully to minimize disruptions and ensure compatibility.

Data lakehouse FAQs

What’s the difference between a data hub and a data lakehouse?

A data hub acts as a central point for data integration, focusing on the ingestion, sharing, and distribution of data from multiple sources. It facilitates data movement rather than analysis. Conversely, a data lakehouse is designed for the storage and comprehensive analysis of vast data volumes, integrating the management and analytical capabilities of data lakes and warehouses.

Who uses data lakehouses?

Data lakehouses are utilized by organizations that need to manage large volumes of diverse data efficiently while enabling advanced analytics and machine learning. This includes industries like financial services, healthcare, retail, and technology fields, where deep analytical capabilities are essential for driving strategic decisions.

Can a data lakehouse replace a data warehouse?

The decision to transition from a data warehouse to a data lakehouse depends on specific organizational needs, the current data infrastructure, and the desired analytical capabilities. Both data warehouses and data lakehouses have their benefits, so business should select the one that will optimize the value and realization of the desired use cases.

With Teradata VantageCloud organizations have the option to use both data warehouses and data lakehouses within a single platform, deriving benefits from the flexibility and value of each. To learn more about the benefits of VantageCloud, get in touch with us today.