What is a Data Lakehouse? How is it Different from a Data Warehouse and a Data Lake?

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In today's digital world, organizations need a more flexible and cost-effective way to store as well as analyze data, and so they are continuously seeking a more ideal data management solution. Data Lakehouse aimed to be just that: a modern solution that resolves the challenges of traditional offerings like Data Lakes and Data Warehouses. It provides a single platform for data storage and management, ideal for businesses aiming to avoid isolated systems for processing different type of workloads. Lakehouse also makes it easier for businesses to work with large amounts of data and scale for advanced analytics, such as Machine Learning and AI applications.?

Understanding Data Lake and Data Warehouse?

Before learning about Data Lakehouse, it is important to understand Data Warehouses and Data Lakes.?

Data Warehouse have a long history in BI application and data analytics. It works as a central repository to stores, manages, and analyzes structured data from various sources. And so, Data Warehouse is optimized for analytical processing (OLAP) rather than transactional processing (OLTP), and Data Warehouse also utilize the ETL process to extract data from sources, transform into consistent format, and then loaded into the warehouse. This makes it easier to analyze big data, create dashboard, and generate enterprise reporting. However, Data Warehouses can be expensive and does not handle semi-structured and unstructured data well.?

A Data Lake is a storage system that can hold all types of data, including structured, semi-structured, and unstructured data in its native file format. They are known for their low cost and storage flexibility as they do not require a predefined schema like that of traditional data warehouse. This allows data in Data Lake to be stored in its original form, meaning it is not processed or organized in a specific way. This also means Data Lake can store different kinds of data, such as audio, video, and text. This makes Data Lakes flexible and suitable for data science and machine learning task, but this also means it lacked some critical features that are common in Data Warehouse: transaction support, data quality enforcement, and consistency in data structure.??

What is a Data Lakehouse??

The Data Lakehouse later emerged, combining the best features of both Data Lakes and Data Warehouses, forming a better data management system. It allows businesses to store large amounts of raw data at a low cost, like a Data Lake, while also organizing and managing the data like a Data Warehouse.??

In the past, companies used Data Warehouses for structured data (used in Business Intelligence (BI)) and Data Lakes for unstructured or semi-structured data (used in Machine Learning and Big Data). But moving data between these two systems created complexity, high costs, and consistency issues. Data Lakehouse solves these problems by leveraging warehouse-like features and pairs it with low-cost storage and flexibility of data lakes, enabling businesses to access and store big data more efficiently, while mitigating potential data issues. This makes data management simpler, reduces duplicate data, and allows for real-time data analysis.?

Key Features of a Data Lakehouse?

Data Lakehouse is a powerful solution that integrates data storage, management, and processing. It acts as a single storage system for structured, semi-structured, and unstructured data, reducing the need for multiple platforms.?

Strong data management ensures high-quality, secure, and well-governed data, while ACID transactions maintain accuracy and consistency, even with multiple users. Standardized data formats like Parquet and Iceberg improve compatibility with various tools, preventing vendor lock-in.?

With real-time data processing, businesses can capture and analyze data instantly, enabling faster decision-making. Its scalable architecture separates storage and computing, optimizing performance and cost efficiency without disrupting operations.?

Key Technology enabling the Data Lakehouse?

The development of data lakehouse is driven by several key technological advancements, including metadata layers, high-performance query engines, and optimized access for data science and machine learning workloads. These innovations enable lakehouses to combine the scalability of data lakes with the performeance and reliability of traditional data warehouses.?

Metadata layers, such as Delta Lake, Apache Iceberg, and Hudi, play a crucial role in managing data within a lakehouse. These layers sit on top of open file formats like Parquet and ORC, providing essential features such as ACID - compliant transactions, schema enforcement and evolution, and time travel functionality. By supporting streaming I/O, metadata layers eliminate the need for external messaging systems like Kafka, allowing real-time data ingestion. Additionally, they enable efficient data validation and indexing, ensuring that both structured and semi-structured data can be managed with consistency and integrity.?

To address the historical performance limitations of data lakes, modern data lakehouses leverage advanced query engines such as Apache Spark, Presto, and Trino. These engines incorporate optimizations like vectorized execution, which improves processing efficiency by handling multiple rows simultaneously through SIMD (Single Instruction, Multiple Data). Additionally, caching frequently accessed data in RAM or SSD minimizes disk I/O bottlenecks, significantly enhancing query performance. Adaptive query execution (AQE) dynamically adjusts execution plans based on runtime statistics, while columnar storage formats like Parquet and ORC reduce the amount of data read from storage. These advancements allow data lakehouses to perform complex analytical queries at speeds comparable to traditional MPP (Massively Parallel Processing) data warehouses.?

Data lakehouses also provide optimized access for data science and machine learning workloads, eliminating the need for complex ETL pipelines. By supporting open data formats, lakehouses allow direct access from machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn. Distributed processing frameworks like Spark DataFrames and Dask enable large-scale data transformations and feature engineering. Additionally, lakehouses leverage efficient indexing and partitioning to accelerate model training and inference. Feature stores, which provide centralized storage and versioning of ML features, further enhance reproducibility and consistency in machine learning workflows.?

Data lakehouse also provides optimized access for data science and machine learning workloads, eliminating the need for complex ETL pipelines. By supporting open data formats?

Overall, data lakehouses represent a significant evolution in data architecture, bridging the gap between data lakes and data warehouses. Their ability to handle structured and unstructured data, coupled with high-performance query execution and native machine learning integration, makes them a powerful solution for modern data-driven enterprises.?

Comparing Data Lakehouse, Data Warehouse, and Data Lake?

Benefits of Using a Data Lakehouse?

A Data Lakehouse offers many advantages for businesses.?

First, it simplifies data management by combining the functions of a Data Warehouse and a Data Lake into one system. This eliminates data silos and the need to transfer data between different platforms, simplifying data observability and making the process more efficient.?

Second, a Data Lakehouse improves data quality. It allows businesses to organize data properly and apply rules to keep data clean and reliable. It also ensures that data is always up-to-date and ready for analysis.?

Third, it reduces costs. Since Data Lakehouses stores raw data on low-cost cloud storage, businesses do not need to maintain multiple expensive data systems. It also eliminates duplicate data and reduces the need for additional data processing.?

Fourth, it increases data reliability. Because the data stays in one system, there is less chance of errors, inconsistencies, or missing data. This makes it easier for businesses to trust their data and make informed decisions.?

Finally, a Data Lakehouse supports various workloads, including Business Intelligence, Machine Learning, and Big Data analytics. It can handle real-time queries, allowing businesses to gain insights quickly and respond to changes in the market.?

Applications of Data Lakehouse?

Today, many businesses are adopting Data Lakehouse technology for data management and analytics.?

For example, Amazon Redshift Spectrum allows companies to analyze data stored in Amazon S3 without moving or copying it. This makes data processing faster and more efficient.?

In Vietnam, Data360X by Elcom is a great example of a platform using Data Lakehouse architecture. It helps businesses manage and analyze data effectively. By using a Data Lakehouse, Data360X can store, organize, and process data in a cost-efficient way.?

The most significant benefits of Data360X can be mentioned as:?

• Centralized Data Management: Combines data from multiple sources into one platform, ensuring consistency and accuracy.?

• Flexible Analysis: Supports BI, predictive analytics, Machine Learning, and Big Data processing.?

• Cost Optimization: Uses cloud-based storage with scalable resources, reducing operational costs.?

By adopting Data Lakehouse solutions, businesses can unlock the full potential of their data, gain better insights, and stay competitive in the digital age.?

Conclusion?

A Data Lakehouse is an advanced data architecture that combines the flexibility of a Data Lake with the structured organization of a Data Warehouse. It helps businesses store, manage, and analyze all types of data in a single system.?

With its scalability, cost efficiency, and strong data management features, a Data Lakehouse is an ideal solution for businesses that want to optimize their data-driven decision-making. As more companies embrace Big Data and AI, the adoption of Data Lakehouses is expected to grow, making them a key technology in the future of data management.?