What are the characteristics of data processing in Hado…

Characteristics of data processing in Hadoop include:

1. Distributed processing: Hadoop employs a distributed computing model, breaking data into multiple blocks and processing them in parallel across multiple computing nodes to increase efficiency.
2. High reliability: Hadoop ensures data reliability by using data redundancy. It replicates data across multiple nodes in the cluster, so that in case one node fails, backup data can be retrieved from other nodes.
3. High scalability: Hadoop can easily expand to thousands of machines in a cluster, handling large-scale datasets by adding more computing nodes.
4. Fault Tolerant: Hadoop has a high level of fault tolerance, where in the event of a computing node failure, tasks will automatically be reassigned to other nodes to ensure completion.
5. Handle various types of data: Hadoop is able to process a wide range of data types, such as structured data, semi-structured data, and unstructured data.
6. Easy to use: Hadoop offers simple and user-friendly APIs and tools, allowing developers to easily handle and analyze large-scale data.
7. Cost-effectiveness: Hadoop, an open-source software, operates on inexpensive hardware and utilizes affordable storage devices, reducing the cost of data processing.
8. Supporting parallel processing: Hadoop utilizes the MapReduce model for parallel processing, breaking down large tasks into smaller ones and executing them simultaneously on multiple computing nodes, thus improving processing speed.