What is a data lakehouse?

In today's world of data management, organizations need systems that can manage large amounts of data while making analysis and processing easy. A data lakehouse is a modern solution that combines the best features of data lakes and data warehouses. Before we explore what a data lakehouse is, let’s first understand the basics of data lakes and data warehouses.

Data lake

A data lake is a storage location that holds large amounts of data. This data is stored in a raw format, i.e., it can either be structured, unstructured, or semi-structured data. This allows a cost-effective solution to store information from data-heavy platforms like the cloud, social media, and conventional databases. However, these data lakes are not optimized for queries and governance, making analyzing data tedious. Moreover, data lakes can become data swamps if their data is duplicated, incomplete, or inaccurate. This becomes one of the reasons why we need to create a data lakehouse.

Data warehouse

A data warehouse is an enterprise data warehouse (EDW) where data is loaded from either data lakes or operational applications. It is optimized and organized to run specific analytical tasks, which can power business intelligence (BI), such as building dashboards, data representatives, and machine learning (ML) tools. Moreover, data warehouses are trusted for integrity as their data is clean, organized, and sorted. However, they come at high costs and are slow as they must sort and clean the data. Moreover, they do not support unstructured and semi-structured data sources. This creates a bad experience for real-time support, although querying data is optimized.

Data lakehouse

A data lakehouse integrates the flexibility of data lakes with the performance and management capabilities of data warehouses. It enables organizations to store raw data and process structured data simultaneously, creating a unified platform for analytics and machine learning.

Now, you can see the difference between data lake, data warehouse, and data lakehouse.

How does a data lakehouse work?

A data lakehouse employs a layered architecture combining storage, metadata management, and compute layers. It allows raw data storage in a central repository while supporting structured querying and transaction-level operations.

1. Raw data storage: Data is ingested in its original format, just like a data lake.

2. Metadata management: Metadata layers provide organization, governance, and schema enforcement.

3. Compute layer: This layer handles query optimization, enabling high-performance analytics and processing.

Key features of the data lakehouse

The following are the key features of the data lakehouse:

• Unified architecture: Merges data lake flexibility and warehouse performance.

• ACID transactions: Ensures data consistency and reliability.

• Support for diverse workloads: Handles BI, data science, and ML workloads seamlessly.

• Decoupled storage and compute: Allows independent scaling of resources.

• Schema evolution: Supports schema changes without disrupting existing data.

Benefits of using a data lakehouse

The following are the benefits of using a data lakehouse:

1. Simplified data management: Combines data lake and warehouse capabilities in one platform.

2. Cost efficiency: Reduces infrastructure costs by eliminating multiple systems.

3. Flexibility: Supports both structured and unstructured data types.

4. Real-time analytics: Processes streaming data efficiently.

5. Improved governance: Ensures compliance through centralized control.

Challenges

The following are the challenges of using a data lakehouse:

1. Complexity: Requires advanced tools and expertise for implementation.

2. Resource management: Balancing storage and computing can be challenging.

3. Migration issues: Transitioning from traditional systems may be time-consuming.

4. Data governance: Maintaining high standards for large datasets can be resource-intensive.

Architecture

The following are the layers of data lakehouse architecture:

1. Data ingestion layer: Collects raw data from diverse sources.

2. Storage layer: Centralized repository for raw and processed data.

3. Metadata layer: Manages schema, governance, and cataloging.

4. Processing layer: Executes ETL (extract, transform, load) and analytics.

5. Consumption layer: Provides tools for BI, ML, and data visualization.

Examples

The following are the examples of data lakehouses:

• Delta Lake by Databricks: Combines ACID transactions with data lake storage.

• Google BigLake: Enables unified analytics across cloud storage and data warehouses.

• Snowflake: Supports hybrid workloads for data science and analytics.

Quiz

A quick quiz to test your understanding of data Lakehouse.

Conclusion

A data lakehouse is revolutionizing data management by bridging the gap between the flexibility of data lakes and the structured performance of data warehouses. It provides a unified, cost-efficient platform for organizations to handle diverse workloads, from real-time analytics to machine learning. As data grows in volume and complexity, the lakehouse model ensures scalability, governance, and actionable insights, making it a critical tool for modern enterprises.