The rise of big data has led to the emergence of new data architectures that can handle the volume, variety, and velocity of data generated by modern organizations. Two of the most popular data architectures are traditional data warehouses and lake house architectures. In this post, we will compare these two architectures, their use cases, and their performance.

Traditional Data Warehouses

A traditional data warehouse is a centralized repository of structured data that has been preprocessed and transformed for analysis. It typically uses a relational database management system (RDBMS) to store data in tables with a predefined schema. Data is loaded into the data warehouse through a process called ETL (extract, transform, load), which involves extracting data from source systems, transforming it into a predefined schema, and loading it into the data warehouse.

A traditional data warehouse is optimized for fast and efficient querying of structured data. It typically uses indexing and pre-aggregated tables to speed up select and aggregate queries. However, a traditional data warehouse is less flexible and scalable than other data architectures. It can be challenging to handle unstructured data, real-time data processing, or large volumes of data.

Lake House Architectures

A lake house architecture is a more flexible and scalable data architecture that combines the benefits of a data warehouse with the flexibility of a data lake. It stores data in a raw format and uses distributed processing engines such as Apache Spark to preprocess and transform the data for analysis. A lake house architecture can handle both structured and unstructured data, real-time data processing, and large volumes of data.

A lake house architecture is designed to handle the volume, variety, and velocity of modern data. It can scale horizontally across a cluster of nodes and can handle large volumes of data. A lake house architecture is also cost-effective, as it uses open-source technologies and can be deployed on commodity hardware.

Use Cases for Lake House Architectures

Here are some real-life use cases for a lake house architecture:

1. Customer Analytics: A lake house architecture can be used to analyze customer behavior across different channels and touchpoints, such as websites, mobile apps, and social media. By combining data from different sources, a lake house architecture can provide a 360-degree view of the customer and enable personalized marketing and customer service.
2. Fraud Detection: A lake house architecture can be used to detect fraud in real-time by analyzing transaction data from multiple sources, such as credit card transactions, log files, and social media data. By combining data from different sources, a lake house architecture can identify patterns and anomalies that indicate fraud.
3. Internet of Things (IoT) Analytics: A lake house architecture can be used to analyze sensor data from IoT devices in real time. By preprocessing and transforming the data in real-time, a lake house architecture can enable real-time monitoring and decision-making.
4. Supply Chain Analytics: A lake house architecture can be used to analyze data from different sources in the supply chain, such as inventory data, shipping data, and weather data. By combining data from different sources, a lake house architecture can enable more efficient and effective supply chain management.
5. Healthcare Analytics: A lake house architecture can be used to analyze patient data from different sources, such as electronic health records, medical images, and wearable devices. By combining data from different sources, a lake house architecture can enable personalized medicine and improved patient outcomes.

Performance Comparison

Here is a performance comparison of select and aggregate functions in a traditional data warehouse and a lake house architecture:

1. Select Functions: In a traditional data warehouse, select functions are typically fast and efficient due to the predefined schema and indexing of data. In a lake house architecture, select functions may be slower due to the need for preprocessing and transformation of the raw data.
2. Aggregate Functions: In a traditional data warehouse, aggregate functions are typically fast and efficient due to the use of pre-aggregated tables. In a lake house architecture, aggregate functions may be slower due to the need for preprocessing and transformation of the raw data. However, a lake house architecture can handle more complex aggregation queries that a traditional data warehouse may struggle with.

When to Consider a Lake House Architecture

Even if a traditional data warehouse is providing good performance, there are still several reasons why an organization may want to consider a lake house architecture:

1. Handling Unstructured Data: A traditional data warehouse is optimized for structured data, while a lake house architecture can handle both structured and unstructured data.
2. Scalability: A lake house architecture is designed to scale horizontally across a cluster of nodes, while a traditional data warehouse may struggle with large volumes of data.
3. Real-Time Data Processing: A lake house architecture can handle real-time data processing, while a traditional data warehouse may struggle with real-time data.
4. Cost-Effective: A lake house architecture is often more cost-effective than a traditional data warehouse, as it uses open-source technologies and can be deployed on commodity hardware.

Companies use Lakehouse

Uber:

Uber uses a lake house architecture to manage and analyze data from its global ride-sharing platform. The architecture is used to capture and store data from a wide variety of sources, including rider and driver data, trip data, and location data. The data is processed and analyzed in real-time to optimize driver and rider experiences, forecast demand, and manage supply chains.

Airbnb:

Airbnb uses a lake house architecture to manage and analyze data from its global vacation rental platform. The architecture is used to capture and store data from a wide variety of sources, including host and guest data, booking data, and search data. The data is processed and analyzed to personalize user experiences, optimize pricing strategies, and improve operational efficiencies.

Capital One:

Capital One uses a lake house architecture to manage and analyze data from its banking and financial services business. The architecture is used to capture and store data from a wide variety of sources, including customer data, transaction data, and market data. The data is processed and analyzed to personalize customer experiences, develop new financial products, and manage risk and compliance.

Netflix:

Netflix uses a lake house architecture to manage and analyze data from its global streaming platform. The architecture is used to capture and store data from a wide variety of sources, including user data, viewing data, and content data. The data is processed and analyzed to personalize user experiences, develop new content, and optimize content delivery networks.

Walmart:

Walmart uses lake house architecture to manage and analyze data from its global retail business. The architecture is used to capture and store data from a wide variety of sources, including transactional data, supply chain data, and customer data. The data is processed and analyzed to optimize inventory levels, forecast demand, and personalize marketing campaigns

Guide on building Lakehouse

Delta Lake

How to build Lakehouse using Delta Lake? Please find the below blog which explains from the start how to create a Lake house using Delta Lake.

Delta Lake: An Introduction to a High-Performance Data Management System

Apache Hudi

How to build Lakehouse using Apache hudi? The article is on the way.

Apache Iceberg

How to build Lakehouse using Apache Iceberg? The article is on the way.

Conclusion

In conclusion, a traditional data warehouse and a lake house architecture have different strengths and weaknesses. A traditional data warehouse is optimized for fast and efficient querying of structured data, while a lake house architecture is more flexible and scalable and can handle both structured and unstructured data. When considering which data architecture to use, organizations should consider their use cases, performance needs, and scalability requirements.