What is a Data Lakehouse? How is it different from a Data Warehouse or Data Lake?

What is a Data Lakehouse?

A data lakehouse is a new architecture that combines the benefits of a data warehouse (DWH) and a data lake. It enables efficient management and analysis of massive amounts of data, accelerating business decision-making. This article covers the basics of a data lakehouse and explains its key benefits in detail.

▼I want to know more about data warehouses (DWH)
⇒ DWH｜Glossary
▼I want to know more about data lakes
⇒ Data Lake | Glossary

Definition and Background

A data lakehouse is a data management methodology that combines the features of a data warehouse and a data lake, maximizing the benefits of both. This approach enables the integrated management of both structured and unstructured data, making it possible to meet a variety of data needs.

For example, companies using a data lakehouse can seamlessly leverage data from different structures. This eliminates the barriers often associated with traditional data management systems, enabling faster decision-making. Organizing and accessing data on a single platform also improves operational efficiency and reduces costs.

As a result, data lakehouses are emerging as the new standard for solving the complex data management challenges faced by modern businesses and delivering more reliable analytical results.

How it differs from a data warehouse or data lake

Data lakehouses emerged to solve the challenges posed by both data warehouses and data lakes. Data warehouses provide fast and reliable analysis, but because they only handle structured data, they are limited in their ability to process unstructured data (such as images and logs). On the other hand, data lakes can flexibly store large amounts of diverse data, but lack analytically optimized query performance and data quality control. Data lakehouses combine the high-performance analytical capabilities of data warehouses with the scalability and flexibility of data lakes, allowing for integrated processing of structured and unstructured data. As such, data lakehouses occupy an important position in the ever-advancing world of data management.

Data Lakehouse and Machine Learning

Data lakehouses offer advantages over data warehouses and data lakes for machine learning and deep learning. While data warehouses are strong in structured data, they cannot handle unstructured data (images, text, video, etc.). Data lakes allow for flexible storage of unstructured data, but have challenges with query performance and data quality management. Data lakehouses combine the advantages of both, allowing for integrated processing of structured and unstructured data. Integrating this diverse data is important for machine learning. Furthermore, they support ACID transactions and schema management, enabling fast analysis and model training while maintaining data quality and integrity. This flexibility and efficiency make data lakehouses the ideal choice for machine learning and deep learning using large-scale, diverse data.

▼I want to know more about machine learning
⇒ Machine Learning | Glossary

BI (Business Intelligence) and Data Lakehouse

Data warehouses are suitable for visualizing structured data quickly, but they cannot handle semi-structured or unstructured data such as sensor data or log data. Data lakes can handle a variety of data types, but their analytical performance tends to be poor and they are not suitable for visualization. Data lakehouses offer the high-performance query capabilities of data warehouses and the flexibility of data lakes. This allows them to integrate both structured and unstructured data and provide real-time data visualization and dashboard updates. This allows for centralized visualization of complex business data and supports decision-making.

Data Lakehouse Architecture and Structure

We will explain the architecture of a data lakehouse and its underlying structure. The data lakehouse architecture consists of several distinct layers that support the availability and flexibility of the system.

Layered structure of a data lakehouse

A data lakehouse architecture uses a layered structure to enable simple and efficient data management. Each layer has a clearly defined role, improving data availability, consistency, and flexibility. A data lakehouse provides high performance and scalability across the data management, storage, processing, and analysis layers. Its key components are described below.

Storage Tier

The storage layer of a data lake uses distributed storage with the characteristics of a data lake. This layer often uses object storage services such as Amazon S3, Azure Data Lake Storage, and Google Cloud Storage. This allows for the storage of large amounts of structured, unstructured, and semi-structured data (for example, JSON or Parquet formats). The storage is scalableand can be expanded at low cost.

Metadata Management Layer

A data lakehouse has a metadata management layer, which is responsible for managing data schema, data quality, and data governance. The metadata catalog keeps track of what data is stored where, how it is structured, and how it should be used. This allows data to be efficiently searched and queries optimized.

Analysis and query layer

A data lakehouse uses SQL queries to analyze data. It supports ACID transactions to ensure data consistency and reliability. This ensures data integrity is maintained even when multiple users or processes update the data simultaneously. This functionality was previously lacking in data lakes, and it brings the benefits of a data warehouse to data lakes.

Open Table Format

Data lakehouses utilize open table formats to efficiently structure the massive amounts of data stored in data lakes and support advanced analytics such as business intelligence (BI) and machine learning. Major open table formats widely used include Delta Lake, Apache Iceberg, and Apache Hudi. While each has its own strengths, they all have features that streamline data manipulation. This combines the flexibility of a data lake with the performance of a data warehouse, providing an ideal environment for supporting corporate analysis and decision-making.

Benefits and challenges of implementing a data lakehouse

Learn about the benefits of implementing a data lakehouse and the challenges you may face.

We'll start by discussing the key benefits of a data lakehouse, then explore the challenges and solutions associated with its implementation.

Cost reduction and efficient resource utilization

The introduction of a data lakehouse can reduce data management costs and improve resource efficiency. While traditionally separate platforms such as a data warehouse and a data lake were required, the adoption of a data lakehouse eliminates the need for duplicate storage and analysis tools. Managing data through a single platform reduces operational costs.

A unified data lakehouse architecture also provides a strong foundation for eliminating operational waste by simplifying the processes involved in collecting, storing, and analyzing data, improving the efficiency and performance of data management.

Scalability and flexibility

The design of a data lakehouse allows it to handle large amounts of data, creating a highly flexible system. Its distributed design gives it high adaptability, allowing it to quickly respond to increases in data size and fluctuations in workload. For example, multinational companies use data lakehouses to consolidate the management of data distributed across regions, enabling scalable operations at a global level. This scalability also allows them to flexibly respond to sudden business demands and new data utilization needs.

Challenges faced during implementation

When implementing a data lakehouse, there are challenges to overcome to ensure the system is integrated and operates smoothly, most notably the significant cost and specialized technical knowledge required for initial setup.

A major challenge is ensuring compatibility with existing data storage and legacy systems. These systems often have different data formats and access protocols, which necessitate the implementation of new data conversion processes. For example, if you need to centralize data from an on-premise data warehouse to cloud-based storage, a corresponding technology solution is essential.

To address these challenges, having the right talent, including engineers and data experts, is key, and choosing a trusted technology partner can help reduce complexity and optimize the implementation process.

Data Quality Management and Security

Compared to traditional data warehouses, data lakehouses offer more flexibility in how data is stored, which can make data quality management more difficult. Even though data is managed centrally, governance and quality control are important to maintain data consistency and accuracy. If confidential or personal information is included, measures to maintain its security are also necessary. It is necessary to strictly enforce data governance regulations (for example, data management, audits, and compliance) and establish consistent rules within the organization.

Approaches to problem solving

As technology advances, the limitations and operational bottlenecks of traditional data platforms are being overcome. Addressing these challenges with a data lakehouse requires a multifaceted approach:

By utilizing technologies that support ACID characteristics, ensuring consistent data transactions and reliability is a top priority. This allows for efficient data processing while maintaining data integrity. The introduction of open table formats and medallion architectures increases the flexibility of data management and the speed of analysis.

Additionally, strengthening data access controls and governance capabilities, including security measures such as strict access logging and role-based permission management, is critical to ensure secure data operations and regulatory compliance.

These approaches are expected to optimize the entire data management system, with the data lakehouse at its core, and promote data-driven decision-making in all industries.

Summary of the future and outlook for data lakehouses

Data lakehouse technology is maturing as the next generation data management architecture, combining the strengths of existing data lakes and data warehouses to enable efficient data analysis, reduce operational costs, and increase scalability.

We encourage you to keep an eye on future developments in data lakes and consider incorporating them into your data management strategy, as they can help deliver competitive business insights faster.

To accelerate data lakehouse adoption, organizations need to continually learn about related technologies and best practices, and seek expert assistance for proper design and implementation.

The person who wrote the article

Affiliation: Marketing Department

Seiji Hosomi

After working in systems development for around 10 years at a system integrator in Tokyo, he joined Appresso (now Saison Technology) in 2016. After working as a development engineer and then project manager for the data integration software DataSpider, he is currently in charge of data utilization in the marketing department. Drawing on the IT system utilization experience he gained during his time as a systems engineer, he supports customers in "data utilization" and "digital transformation."
(Affiliations are as of the time of publication)

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