What is Data Lakehouse?

Data Lakehouse is an architecture that combines the advantages of Data Lake and Data Warehouse. It was first proposed by Databricks in 2020.

💡 Core Concept

Lakehouse is an integrated data platform that simultaneously provides the flexibility of data lakes and the performance of data warehouses.

📖 Table of Contents

  1. What is Lakehouse?
  2. Limitations of Traditional Architecture
  3. Benefits of Lakehouse
  4. Core Technologies
  5. Conclusion

🏗️ What is Lakehouse?

Data Lakehouse is an architecture that combines the advantages of Data Lake and Data Warehouse. It was first proposed by Databricks in 2020.

💡 Core Concept

Lakehouse is an integrated data platform that simultaneously provides the flexibility of data lakes and the performance of data warehouses.

⚠️ Limitations of Traditional Architecture

Problems with Data Lakes

  • Absence of ACID Transactions: Difficulty in ensuring data consistency
  • Lack of Schema Management: Data quality and validation issues
  • Query Performance Degradation: Slow response during large-scale data analysis
  • Absence of Data Governance: Lack of access control and audit functions

Limitations of Data Warehouses

  • Cost Increase: High costs for data storage and processing
  • Scalability Limitations: Difficulty in processing large-scale unstructured data
  • Data Movement: Data duplication and delays during ETL processes
  • Lack of Flexibility: Complex migration when changing schemas

🚀 Benefits of Lakehouse

🔒 ACID Transactions

Provides reliable data analysis environment by ensuring data consistency and integrity

📊 Schema Management

Improves data quality through schema evolution and version management

⚡ High-Performance Queries

Fast data search and analysis through indexing and partitioning

💰 Cost Efficiency

Cost optimization through separation of low-cost storage and high-performance computing

🔄 Real-time Processing

Integration of streaming data and batch data processing

🛡️ Data Governance

Provides access control, auditing, and data lineage management functions

🔧 Core Technologies

1. Table Format

The core of Lakehouse is table format. This supports ACID transactions and schema management.

Major Table Formats:

  • Delta Lake: ACID transaction support developed by Databricks
  • Apache Iceberg: Schema evolution specialization started by Netflix
  • Apache Hudi: Real-time processing specialization developed by Uber

2. Metadata Management

Manages metadata at the file level to overcome limitations of centralized metastores.

Metadata Layers:

  • File Level: Schema and statistical information of each data file
  • Table Level: Schema and partition information of entire tables
  • Catalog Level: Catalogs managing multiple tables

3. Query Engine

Supports various query engines to meet diverse analysis requirements.

Supported Engines:

  • Spark SQL: Large-scale data processing
  • Presto/Trino: Interactive queries
  • Flink: Streaming data processing

🎯 Use Cases

1. Data Engineering

  • ETL/ELT Pipelines: Data transformation and loading
  • Data Quality Management: Schema validation and data verification
  • Real-time Data Processing: Streaming data integration

2. Data Analysis

  • Business Intelligence: Dashboards and reports
  • Data Science: Machine learning model development
  • Ad-hoc Analysis: Temporary data analysis

3. Data Governance

  • Access Control: Role-based access control
  • Data Lineage: Tracking data sources and transformation history
  • Auditing: Recording data access and usage history

🔮 Future Prospects

1. Technology Development

  • AI/ML Integration: Support for machine learning workflows
  • Automation: Automation of data quality and performance optimization
  • Multi-cloud: Support for various cloud environments

2. Ecosystem Expansion

  • Tool Integration: Integration with various BI tools and analysis tools
  • Standardization: Definition of industry standards and interfaces
  • Community: Expansion of open source ecosystem

🎉 Conclusion

Data Lakehouse is an innovative solution that overcomes limitations of existing data architectures and combines the flexibility of data lakes with the performance of data warehouses.

Key Benefits:

  • Integrated Platform: All data processing in one system
  • Cost Efficiency: Separation of low-cost storage and high-performance computing
  • Scalability: Support for large-scale data processing and real-time analysis
  • Data Quality: Improved data reliability through ACID transactions and schema management

Lakehouse has become an essential architecture in modern data engineering and will continue to develop to lead data-centric digital transformation in the future.

This article was written for engineers who want to understand Lakehouse architecture in the data engineering field. For more detailed content, please learn through official documentation and hands-on practice of each technology.