When you see the title of this post, you might wonder how many buzzwords can fit into one sentence. However, it’s important to explore how a customer data stack benefits from the data lakehouse. But first, let’s clarify what we mean by customer data stack and data lakehouse.
What is a Customer Data Stack?
The modern data stack is a popular topic, but it’s important to make a distinction, especially when it comes to customer data. Customer data provides unique value to an organization and comes with its own set of technical challenges. It provides behavioral information about customers, which is crucial for any business, especially modern online businesses with limited direct customer interactions.
Customer data comes with challenges, such as large quantities, noise, constant change, and a variety of structured and unstructured formats. Working with customer data requires unique choices when building a data stack, leading to the term “Customer Data Stack.”
In short, a Customer Data Stack is a complete data stack that allows for the capture, storage, and processing of customer data at scale.
What is a Data Lakehouse?
There has been a lot of buzz around the terms data lake and data lakehouse. The data lakehouse is a newer concept that builds upon the traditional data lake architecture.
Data Lake
The concept of a data lake involves the separation of storage and compute. This includes scalable object storage systems, such as S3 on the cloud, and processing frameworks like Trino and Spark. However, traditional data lakes lack some important functionalities found in data warehouses and transactional databases, leading to the emergence of the data lakehouse.
Data Lakehouse
A data lakehouse enhances the data lake concept by adding critical data management features found in database systems. It incorporates functionalities like transactions and table formats, creating an architecture that combines the benefits of a data warehouse with the scalability of a data lake.
Data Lakehouse as the Foundation of a Customer Data Stack
When it comes to building a data stack for customer data, the storage and processing layer is crucial. Using a lakehouse for customer data has several benefits, including cheap, scalable storage, support for every data format, and the ability to handle hybrid workloads, including operational analytics and advanced ML techniques.