In this article I am going to list a bunch of information about data lakehouses and will also compare it to other data storage and transformation concepts. So let’s dive right in.
What is a Data Lakehouse?
A data lakehouse is an architectural paradigm that combines the best features of data lakes and data warehouses. It aims to provide the scalability and flexibility of data lakes with the reliability and performance of data warehouses. That means data is stored in a cheap and reliable way, which gets combined with compute resources. To add value to the data, it gets processed via a so called medallion architecture.
Medallion Architecture
The medallion architecture is a data design pattern used to organize data into different layers based on its quality and processing stage. This architecture typically consists of three layers:
Bronze Layer: This is the raw data layer where data is ingested in its original format. It includes all the data collected from various sources, in the same and unchanged format as on the origin system. The primary purpose of the bronze layer is to serve as a landing zone for incoming raw data and offering this to upstream layers.
Silver Layer: In this layer, data is cleaned, transformed, harmonized, and enriched. The silver layer contains data that has undergone initial processing to improve its quality and usability. This may include removing duplicates, handling missing values, and applying basic transformations. Often the data is stored in a domain or use-case specific data model. In addition to that you also want to get rid of sources system specific information such as primary keys, ID’s or specific designations - this is what we understand as harmonization. The silver layer serves as an intermediate stage between raw and fully processed data.
Gold Layer: The gold layer contains highly curated, business-ready data that is optimized for analytics and reporting. Data in this layer has undergone extensive processing and transformation to ensure high quality and reliability. The gold layer is used for generating insights, serving data to dashboards, and providing its data for machine learning purposes.
The medallion architecture helps in managing data quality and ensuring that data is processed in a structured and organized manner. It allows data engineers to incrementally improve data quality and make it more accessible for various use cases.
Comparison with Other Data Storage and Transformation Approaches
Next let us compare the data lakehouse concept to other data storage and transformation approaches.
Data Lakes
- Pros: Data lakes offer high scalability and flexibility, allowing storage of vast amounts of raw data in its native format. They are cost-effective for storing large datasets.
- Cons: Data lakes can become data swamps if not managed properly, leading to issues with data quality and governance. Query performance can be slower due to the lack of structure.
Data Warehouses
- Pros: Data warehouses provide high performance for complex queries and analytics. They ensure data quality and consistency through structured schemas and ETL processes.
- Cons: They can be expensive to scale and maintain. Data warehouses are less flexible in handling unstructured or semi-structured data compared to data lakes.
Relational Databases
- Pros: Relational databases are excellent for transactional processing and managing structured data with ACID (Atomicity, Consistency, Isolation, Durability) properties. They offer robust data integrity and support complex queries.
- Cons: They are not designed for handling large volumes of unstructured data and can be costly to scale horizontally. Performance can degrade with very large datasets. They may be an element of a big data architecture, but processing all data in a single database may not be the best idea.
Excel Sheets
- Pros: No. Please do not even consider this.
- Cons: They are not scalable for large datasets and lack robust data governance and security features. Excel sheets are prone to errors and are not suitable for collaborative, multi-user environments.
Data Lakehouse
- Pros: Combines the scalability and flexibility of data lakes with the performance and reliability of data warehouses. Supports both structured and unstructured data, enabling advanced analytics and machine learning. The medallion architecture ensures data quality and organized processing stages.
- Cons: Implementing a data lakehouse can be complex and requires careful planning and management.
Summary
In summary, a data lakehouse is a modern data architecture that combines the benefits of data lakes and data warehouses. It leverages the medallion architecture to manage data quality through different processing stages, making it suitable for various analytics and machine learning applications. While it offers scalability, flexibility, and performance, implementing a data lakehouse requires careful planning and management to fully realize its potential.