<ul>
<li>Purpose: Apache Hadoop: An open-source framework designed for processing large-scale data across distributed computing environments. It focuses on batch processing, and managing big data through the Hadoop Distributed File System (HDFS).SAP HANA: An in-memory database and analytics platform. It optimizes real-time data processing, enabling high-speed transactions and advanced analytics in a single system. 
</li>
<li>Architecture: Hadoop: Utilizes a distributed architecture, splitting data across multiple nodes, facilitating scalability and fault tolerance. It supports a range of tools like MapReduce for processing.HANA: Centralized, leveraging in-memory computing to store and process data directly in RAM, ensuring faster data access and query performance.
</li>
</ul>
<img src="https://miro.medium.com/v2/resize:fit:753/0*57V50VaqrLP0p0cX" alt />
<ul>
<li>Use Cases: Hadoop: Ideal for processing large, unstructured data sets in finance, healthcare, and social media analytics.HANA: It is suited for real-time analytics, transactional processing, and hybrid workloads, often used in enterprise applications like ERP systems.</li>
</ul>
Both serve distinct purposes: Hadoop excels in big data processing, while HANA is built for speed and real-time insights.

* **Purpose: Apache Hadoop:** An open-source framework designed for processing large-scale data across distributed computing environments. It focuses on batch processing, and managing big data through the Hadoop Distributed File System (HDFS).**SAP HANA:** An in-memory database and analytics platform. It optimizes real-time data processing, enabling high-speed transactions and advanced analytics in a single system.  
    
* **Architecture: Hadoop:** Utilizes a distributed architecture, splitting data across multiple nodes, facilitating scalability and fault tolerance. It supports a range of tools like MapReduce for processing.**HANA:** Centralized, leveraging in-memory computing to store and process data directly in RAM, ensuring faster data access and query performance.
    

![](https://miro.medium.com/v2/resize:fit:753/0*57V50VaqrLP0p0cX align="left")

* **Use Cases: Hadoop:** Ideal for processing large, unstructured data sets in finance, healthcare, and social media analytics.**HANA:** It is suited for real-time analytics, transactional processing, and hybrid workloads, often used in enterprise applications like ERP systems.
    

Both serve distinct purposes: Hadoop excels in big data processing, while HANA is built for speed and real-time insights.

Difference between Apache Hadoop and SAP HANA?

Apache Hadoop vs. SAP HANA: Key Differences

What is the difference between Apache Hadoop and SAP HANA?

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