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.
Data Lakehouse is distinguished from traditional data platforms by the following features:
Data Lakehouse combines the operating principles of data warehouse and data lake architectures. Here are the basic processing steps:
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.
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.
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.
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.
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.
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.
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.
Data Lakehouse enables instant processing and analysis of new data. This is an important advantage, especially in business processes that require quick decision-making.
The use of open data formats provides platform independence and allows enterprises to easily integrate different tools and technologies.
Data Lakehouse has a wide range of uses in various industries and business processes. Here are some of the prominent uses of this structure:
Data Lakehouse can also face some challenges, although it offers many advantages:
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.
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You can find all the details that need to be known about the computing cycle in the continuation of the article, and you can get healthy data by processing your company data according to these stages.
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