What is Data Lakehouse?

Data Lakehouse is a modern data management approach that combines the advantages of data warehouse and data lake architectures. This structure offers the ability to process both structured and unstructured data on a single platform, making data analytics and big data processing processes more efficient.

Traditional data warehouses are analytics-oriented and handle structured data, while data lakes offer the flexibility to store large volumes and a variety of data types. Data Lakehouse combines the best features of these two structures and offers advantages such as cost, performance, scalability.

Key Features of Data Lakehouse

Data Lakehouse is distinguished from traditional data platforms by the following features:

1. Hybrid Storage: Structured (tables) and unstructured (video, audio, text) data can be stored on the same platform.
2. Low Cost: It combines the analytical capabilities of data warehouses with cost-effective storage solutions of data lakes.
3. High Performance: It offers fast data query and analysis thanks to advanced processing engines and indexing technologies.
4. Open Data Formats: Using open data formats such as Parquet, ORC, facilitates platform-independent work.
5. Real-Time Analytics: New data added to the data lake can be processed and analyzed instantly.
6. Comprehensive Data Management: It includes integrated tools that ensure data quality, security and access controls.

How Does Data Lakehouse Work?

Data Lakehouse combines the operating principles of data warehouse and data lake architectures. Here are the basic processing steps:

Data Storage

Data Lakehouse stores a wide range of data, from structured data to unstructured data. Data is often held in low-cost and highly scalable cloud storage environments.

Data Processing

The stored data is processed for analysis and reporting. Process engines are used, supported by modern technologies such as machine learning and big data analytics.

Data Access and Analysis

Users can easily access data using familiar languages or APIs, such as SQL. This enables data scientists, analysts and business units to gain insights quickly and effectively.

Integration and Sharing

Data Lakehouse can take data from various data sources and work integrated with different analysis tools. In addition, it allows data to be easily shared and used by different teams.

Advantages of Data Lakehouse

Working on a Single Platform

Data Lakehouse allows both structured and unstructured data to be stored in the same environment. This eliminates data silos, making data access and analysis easier.

Cost Efficiency

It combines the analytical power of data warehouses with the advantage of low-cost storage of data lakes. Businesses can reduce their reliance on high-cost data warehouses.

Performance and Flexibility

Data queries are made faster thanks to advanced processing engines. In addition, the Data Lakehouse adapts easily to changing business needs thanks to its flexible structure.

Real-Time Processing

Data Lakehouse enables instant processing and analysis of new data. This is an important advantage, especially in business processes that require quick decision-making.

Use of Open Formats

The use of open data formats provides platform independence and allows enterprises to easily integrate different tools and technologies.

Uses of Data Lakehouse

Data Lakehouse has a wide range of uses in various industries and business processes. Here are some of the prominent uses of this structure:

1. Finance and Banking

• Analyze customer behavior and provide personalized financial services.
• Conducting risk analysis and fraud detection.
• Rapidly processing high volumes of financial data.

2. Health Sector

• Storing and analyzing patient data.
• Develop big data analytics and machine learning applications in clinical research.
• Perform real-time analysis to optimize health care.

3. E-commerce and Retail

• To provide personalized product recommendations by analyzing customer shopping habits.
• Optimizing inventory management and supply chain.
• Making data-driven decisions to improve sales performance.

4. Media and Entertainment

• Storing large multimedia files at low cost.
• Develop personalized content recommendations by analyzing user behavior.
• Generate real-time monitoring and performance reports.

5. Production and Industry

• To process sensor data from manufacturing processes.
• Improving the efficiency of the supply chain.
• Optimizing quality control processes.

Challenges with Data Lakehouse

Data Lakehouse can also face some challenges, although it offers many advantages:

• Data Management Complexity: Managing different types of data on a single platform can be difficult.
• Investment Cost: Initial implementation and infrastructure development costs can be high.
• Technical Competence: Expertise is required to effectively manage Data Lakehouse solutions.
• Compatibility and Security: Storing large amounts of data securely and complying with legal regulations can be challenging.

Data Lakehouse combines the best features of data warehouse and data lake technologies, providing a powerful solution for modern data management. With advantages such as cost-effectiveness, flexibility and performance, businesses can manage data processes more effectively and accelerate data-driven decision-making.