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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