HADOOP

 

                                         HADOOP

 

What is Hadoop?

Hadoop is an open-source framework that plays a crucial role in storing, processing, and analyzing large-scale data. It was created in the early 2000s and has since become a go-to platform for big data projects, especially those involving distributed computing¹. Here are some key points about Hadoop:



 1.Distributed Storage Platform: Hadoop provides an efficient way to store massive amounts of data from various sources such as ERP systems, log files, and web platforms. It's particularly suitable for distributed computing environments, making it ideal for large-scale storage purposes or tasks that require parallelization¹.

 

2.Data Processing Engine: Hadoop allows users to write programs for processing and managing large volumes of data in parallel. Compared to traditional data warehouse systems, it offers fault tolerance, scalability, and cost savings¹.

 

3.Importance for Data Scientists:

Data Exploration: As a data scientist, you often encounter mountains of data to sift through. Hadoop simplifies this process by allowing you to store large amounts of data while still enabling easy analysis. You can explore your data in different ways and identify patterns more quickly, leading to better decision-making¹.

 Scalability: Hadoop's distributed architecture ensures scalability, which is crucial when dealing with massive datasets. It allows you to handle data growth without compromising performance¹.

Cost-Effective: Hadoop's open-source nature makes it cost-effective compared to proprietary solutions. However, building a custom code base on top of Hadoop requires understanding its workings and how it fits into your project¹.

 

4.Considerations:

 Resource Requirements: Hadoop demands more RAM than other platforms. If you plan to run multiple applications simultaneously, ensure your server has sufficient memory (more than 8 gigabytes) before installing Hadoop.

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