Data Warehouse vs. Data Lakehouse: What is the difference?

Before we talk about the difference between a data lakehouse and a data warehouse, let’s talk about their definitions and how large organizations use them.

We often hear the term “Data Warehouse”, which involves data storage management in large organizations. What does it mean? How important is it to the business activities in your organization?

Data Warehouse is a large-scale data management system that integrates data from multiple systems and sources, such as CRM systems (Customer Relationship Management), inventory, and sales transactions by storing time-variant data over extended periods of both current and historical data as a data center and leveraging them for in-depth analysis and sharing information across different departments within the organization.

Features of Data Warehouse

• Subject Oriented: Data is categorized and stored around specific subjects, enabling quick retrieval for data analysis.
• Integration: Data warehouses consist of data from different sources integrated and structured in the same format before storage.
• Time variant (time series): Historical data and the most recent data collected in the data warehouse are identified by a particular time period. This means that the data warehouse also records the timestamp of data.
•  Non-Volatile: The data stored in the data warehouse is for analysis only. This means that there can be no alterations or changes once the data is imported.

Consolidating data in one place makes it more convenient and easier to use. Data warehouses typically store structured data and install it on a server, which is both storage and compute. Once data is in your data warehouse, BI tools can analyze data sets and present analytical findings in dashboards.

As more people began to interact online, the amount of data generated increased dramatically, including streaming data, social media, and IoT. Thus, arises the notion of “Big data”. These data types are semi-structured (e.g., XML, HTML, JSON) and unstructured (e.g., text files, image files, audio files). As a result, the data warehouse cannot support them.

Therefore, another platform called “Data Lake” has been developed to support such data types and is utilized in conjunction with data warehouses to respond to more business needs.

The principle of a data lake is to store raw data collected from various sources without organization. When in-depth analysis is needed, they are converted into structured data and inserted into the data warehouse. In addition, data scientists or data analysts can create real-time data analysis models by reorganizing the data structure.

Although the data lake can support various data types, there are still some limitations, such as applying business intelligence and analytics. It is necessary to convert all data into a structured format and store it in the data warehouse without analyzing the data first, making querying difficult and causing redundancy between the data lake and the data warehouse. Additionally, it is difficult to do tasks such as inserting, updating, or deleting data in a certain period because the data is stored in the same file.

Due to the limitations of data lake, the “Data Lakehouse” platform has been developed to address these problems.

Data Lakehouse is a storage technology that combines the benefits of data lakes and data warehouses by implementing the data structures and data management features of the data warehouse directly on data lake storage. Data lakes are more flexible than data warehouses.

Features of Data Lakehouse

• Cloud object storage: Data lakehouse typically stores data as objects on a low-cost and easily scalable cloud, such as Microsoft’s Azure Blob Storage, Google Cloud Storage, etc.
• Transactional metadata layer: With the intake capability of a data lake, it is easy to manage data and implement data governance in data warehouses. The lakehouse architecture also supports ACID Transactions (atomic, consistent, isolated, durable) to ensure the accuracy of the transmitted data.
• Data optimization capabilities: Data lakehouse also includes the ability to optimize data for faster analytics through measures such as clustering, caching and indexing.
• Concurrent read & write transactions: Ability to handle multiple simultaneous read and write operations.
• Open storage formats and APIs: Data lakehouse provides data storage technology and APIs for direct data access.
• Various data types and workloads: Data lakehouse can handle various data types and available for BI tools and Machine Learning
• End-to-end streaming: This enables real-time reporting and analysis under the same storage.

Comparison chart of Data Warehouse vs. Data Lakehouse

Although each data analytics platform has different points, it does not mean that newly emerging platforms can always replace old platforms. It depends on the appropriate use of the business, including various elements of each organization.

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