The Lakehouse Architecture
This article covers the fundamentals of lakehouse architecture along with its needs, evolution, core components, functions, technologies, challenges and the roadmap.
Overview
When it comes to data analytics, the current ecosystem is divided in two segments — one which is based on traditional data-warehouse systems (like Teradata) and the other which is based on datalakes using technologies like Amazon S3, GCS, etc. Both these models have their pros and cons and typically co-exist in an organization.
Lakehouse architecture combines the benefits of both these worlds — data lakes and data warehouses.
As per the paper presented in CIDR 2021 (Conference on Innovative Data Systems Research), Lakehouse Architecture can be defined as a data management system based on low-cost and directly-accessible storage that also provides traditional analytical DBMS management and performance features such as ACID transactions, data versioning, auditing, indexing, caching, and query optimization.
To understand the topic in detail, we will focus on key questions —
- Why do we need Lakehouse Architecture?
- How does the Lakehouse Architecture work?
