Evolution of Big Data Infrastructure: From Data Warehouse to Lakehouse

When companies first began seriously dealing with large volumes of data (around the 1980s–90s), the main technology they used was the traditional Data Warehouse (DWH). These systems stored data strictly in structured formats, following predefined schemas. The standard workflow was the ETL process (Extract, Transform, Load), where data was transformed and structured before being stored in a centralized repository.

Over time, however, as data volumes grew exponentially, this traditional approach became too costly, inflexible, and insufficient for handling diverse data types—especially semi-structured and unstructured data generated by web services, social media, and IoT devices.

Emergence of Big Data and Data Lakes

Around the mid-2000s, the explosion of internet services and connected devices dramatically increased the volume and complexity of data. This shift led to the concept of Data Lakes.

The core idea behind a Data Lake was to store data in its raw, native format without pre-processing. This approach provided inexpensive storage solutions and immense flexibility in handling various types of data, enabling later processing according to specific analytical needs.

Key technologies supporting this approach were:

• Hadoop: Introduced in the mid-2000s, Hadoop allowed companies to store and process large datasets cheaply at scale using distributed storage (HDFS) and MapReduce for computation.

• Apache Spark: Arriving around 2014, Spark significantly improved upon Hadoop by performing computations in-memory, speeding up data processing dramatically.

• Specialized storage formats like Apache Parquet, Avro, and ORC, optimized specifically for analytics workloads.

But after some time, significant drawbacks became apparent—particularly issues with data governance, consistency, metadata management, and difficulties in supporting updates and transactional workloads.

Transition to the Lakehouse Paradigm

In response to these challenges, around 2017-2019, the industry began shifting toward the Lakehouse architecture. This approach combines the flexibility, scalability, and affordability of Data Lakes with the transactional reliability and structured data governance typical of Data Warehouses.

Lakehouse architectures offer the best of both worlds:

• Flexible and inexpensive storage (from Data Lakes).

• Structured data governance, reliability, and strong consistency features (previously available only in data warehouses).

• ACID transactions and schema management capabilities.

Prominent technologies that drive the Lakehouse approach today include:

• Delta Lake (by Databricks): Adds ACID transactions, data versioning, and schema evolution capabilities directly atop Data Lakes.

• Apache Iceberg (used by Netflix and Apple): An open, scalable table format simplifying management of massive datasets, providing reliable schema evolution and efficient query performance.

• Apache Hudi: Enables Change Data Capture (CDC), incremental updates, and efficient streaming updates, allowing faster and easier management of data lakes.

Comparing Lakehouse with Previous Approaches

Feature	Data Warehouse	Data Lake	Lakehouse
Data types	Structured only	All types (structured, semi-structured, unstructured)	All types
Cost	High	Low	Low
Flexibility	Low	High	High
Transactions (ACID)	Supported	Limited or None	Fully supported (ACID)
Metadata management	Excellent	Weak	Excellent
Use cases	Analytics	Analytics & ML	Unified Analytics & ML

Why the Lakehouse Matters Today

Today, Lakehouse architecture significantly impacts how organizations approach data analytics. Businesses can now leverage a single, unified platform for both analytical workloads and machine learning tasks, eliminating redundancy and complexity.

The Lakehouse has also driven the adoption of real-time analytics and streaming solutions (e.g., Apache Kafka and Apache Flink), allowing near-real-time insights and faster data-driven decision-making.

In short, the introduction of the Lakehouse represents a crucial step toward simpler, more efficient, and flexible data management solutions. Companies increasingly adopt this architecture, recognizing its potential to simplify infrastructure, reduce costs, and accelerate innovation with data.