Why Lakehouse Architecture Is Redefining Modern Data Platforms

Data warehouses originated to help business leaders gain insights by collecting operational data into a centralized repository for decision support and business intelligence (BI). They use a Schema-on-Write approach, optimizing the data model for downstream BI, and are considered the first generation of analytics platforms.

These warehouses bind storage and compute resources to the same device, forcing enterprises to provision for peak loads, which becomes increasingly costly as data grows.

They were designed for structured data, but the rise of semi‑structured and unstructured data (videos, audio, documents) exceeds their processing capabilities.

To address these issues, the second‑generation analytics platform offloads data to a data lake.

Data lakes are low‑cost storage systems with file APIs (e.g., HDFS) that store data in open formats such as Apache Parquet and Apache ORC. They use a schema‑on‑read architecture, allowing cheap storage of any data, but push data‑quality and management work downstream. Typically, a small portion of high‑value data is ETL‑ed into a warehouse (e.g., Teradata) for critical BI, while the lake serves other workloads such as machine learning.

Since 2015, cloud object stores like Amazon S3, Azure Delta Lake Storage, and Google Cloud Storage have begun to replace HDFS, offering higher durability, cross‑region replication, and lower cost. The two‑layer lake‑plus‑warehouse architecture has become mainstream.

Although the two‑layer architecture separates compute and storage, it adds complexity: data must be ETL‑ed to the lake and then to the warehouse, increasing latency and failure risk. Existing warehouses and lakes also provide limited support for advanced analytics.

Four common problems of current analytics platforms are:

Reliability – keeping lake and warehouse data consistent requires extensive ETL work.

Data staleness – warehouse data is often days behind the lake.

Limited support for advanced analytics – machine‑learning systems cannot efficiently read from warehouses, while lakes lack ACID transactions, versioning, and indexing.

Total cost of ownership – duplicate storage in both lake and warehouse increases expenses.

The technical question is whether a data lake built on open formats can become a high‑performance system that offers warehouse‑level performance and management while supporting fast I/O for advanced analytics.

LakeHouse Architecture

A Lakehouse is a low‑cost, directly‑accessible storage system that also provides traditional analytical DBMS features such as ACID transactions, data versioning, auditing, indexing, caching, and query optimization.

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.

The Lakehouse combines the low‑cost, open‑format storage of a lake with the strong management and optimization of a warehouse.

Because Lakehouses expose raw data files, they sacrifice some data independence, a cornerstone of relational DBMS design.

The architecture fits cloud environments with separated compute and storage, allowing independent compute nodes (e.g., GPU clusters for machine learning) to access the same storage. It can also be implemented on non‑cloud systems like HDFS.

2.1 Overall Architecture

The first key decision is to store data as standard file formats (e.g., Apache Parquet) in low‑cost object storage (e.g., Amazon S3) and to add a transactional metadata layer that tracks which files belong to which table version. This enables ACID transactions and version control while keeping most data in cheap object storage. Projects such as Delta Lake and Apache Iceberg implement this approach.

Lakehouses must also deliver good SQL performance. Traditional warehouses use techniques like SSD hot data, statistics, indexes, and data layout optimizations. Although Lakehouses cannot change the file format, they can use caching, auxiliary data structures, and layout optimizations to accelerate queries.

Thanks to DataFrame APIs, Lakehouses can support advanced analytics workloads. Machine‑learning libraries such as TensorFlow and Spark MLlib can read Parquet files directly. By first consulting the metadata layer to discover relevant files, they can operate on lake data efficiently.

2.2 Metadata Layer

The metadata layer sits on top of the lake storage and provides ACID transactions and other management features. Systems like Hive ACID, Delta Lake, and Apache Iceberg store transaction logs (often in Parquet) that track file membership, enabling billions of objects per table.

These layers also support data quality checks, governance features such as access control and audit logging, and can be retrofitted onto existing lake data by adding a transaction log.

Open challenges remain, such as high latency of object‑store‑based transaction logs, limited concurrent transactions, and the need for faster metadata storage.

2.3 SQL Performance Optimizations

Lakehouses face the challenge of delivering top‑tier SQL performance without traditional data independence. Three optimization techniques used in Delta Lake are:

Data caching : cache lake files on faster media (SSD or RAM) and optionally in a more query‑friendly format.

Auxiliary data : store column‑level min‑max statistics, Bloom filters, and other structures alongside transaction logs to enable data skipping and indexing.

Data layout optimization : order records (e.g., Z‑order, Hilbert curves) or use different compression algorithms to improve I/O patterns.

These techniques reduce I/O and improve query latency, especially for hot data.

2.4 Support for Advanced Analytics

Advanced analytics systems use declarative DataFrame APIs that map to Spark SQL plans, allowing them to benefit from Lakehouse optimizations. Ongoing research explores factorized ML that pushes ML logic into SQL joins and designs standard interfaces for data scientists.

Open Issues

The article concludes with several research directions: alternative ways to achieve Lakehouse goals, cost‑effective metadata stores, handling of transaction‑log latency, multi‑transaction support, and integration with serverless compute for query processing.

Overall, the Lakehouse aims to eliminate the drawbacks of the two‑layer architecture and improve support for machine‑learning workloads, representing a significant trend in modern data management.