Lec-127: Introduction to Hadoop🐘| What is Hadoop🐘| Hadoop Framework🖥

Summary

The video provides an introduction to the evolution of big data, starting from the early 2000s. It explains how the rise of the internet brought a significant change in the types and volume of data, leading to the emergence of big data. The speaker discusses how tools like Hadoop were developed to store and process large amounts of data efficiently. Key components such as HDFS and MapReduce are introduced, along with the concept of distributed data storage and parallel processing. The video also highlights how data replication ensures reliability in case of system failures.

Takeaways

• 😀 Introduction to the story of Hadoop, which began around the early 2000s with the rise of the internet.
• 💻 The rise of the internet in 2001 led to an increase in data types, including text, images, and video, resulting in massive data generation.
• 📈 Before the internet boom, data was stored in structured formats, but with the rise of unstructured data, traditional systems were no longer efficient.
• 📊 Big Data became a challenge as traditional storage systems could not handle the growing volume and variety of data.
• ⚙️ Hadoop was developed to address the issue of storing and processing large amounts of unstructured data in a distributed manner.
• 🛠️ Doug Cutting and Mike Cafarella started working on the Hadoop project in 2002, and Yahoo open-sourced it in 2008, followed by Apache making it available to the public in 2012.
• 📂 Hadoop uses a distributed storage system, HDFS (Hadoop Distributed File System), which breaks files into partitions (default size: 128 MB) and stores them across multiple data nodes.
• 🗂️ Data replication is key to preventing data loss in case of node failure, with data replicated across different nodes.
• 🔧 Hadoop’s two main components are HDFS for storage and MapReduce for processing large datasets in a parallel manner.
• 🖥️ MapReduce processes large queries by dividing them into smaller tasks, which are solved in parallel, using key-value pairs.

Q & A

What is the significance of the 21st century in the context of the story?

The 21st century, specifically starting from 2001, marks the period when the internet began to gain popularity. This growth in internet users led to a significant increase in the amount of data being generated, shifting the focus from structured data to handling large volumes of unstructured data.

How did the internet impact data storage and processing?

With the rise of the internet, the types of data being generated changed from simple text to more complex forms like images and videos. This increased the volume of data, requiring new solutions for storage and processing, as traditional systems could no longer handle the massive amounts of data effectively.

What is 'big data' as described in the script?

Big data refers to the large and complex datasets generated by users as they interact with the internet, including text, images, and videos. The size and complexity of this data made traditional storage and processing methods insufficient, leading to the need for distributed systems like Hadoop.

Who are considered the 'fathers of big data' according to the script?

Doug Cutting and Mike Cafarella are credited as the 'fathers of big data.' In 2002, they began working on a project called Hadoop to handle large amounts of data more effectively.

What role did Yahoo and Apache play in the development of Hadoop?

Yahoo declared Hadoop as an open-source project in 2008, and in 2012, Apache made Hadoop publicly available as an open-source framework, making it freely accessible to the public for big data storage and processing.

What is Hadoop, and how does it differ from traditional databases?

Hadoop is an open-source framework, not just a simple software like traditional databases (e.g., RDBMS). It provides a system for storing and processing large datasets in a distributed manner, allowing data to be stored across different nodes and accessed in parallel.

What are the two key components of the Hadoop framework mentioned in the script?

The two key components of Hadoop are HDFS (Hadoop Distributed File System) and MapReduce. HDFS manages how data is stored across different nodes, while MapReduce processes the data by dividing complex queries into smaller tasks that can be executed in parallel.

How does HDFS store data, and what is the concept of partitioning?

HDFS stores data in a distributed manner by breaking large files into smaller chunks (partitions). By default, a file is divided into 128 MB blocks, which are stored across different data nodes. This ensures that even large datasets can be stored efficiently across multiple devices.

What happens if a data node fails in Hadoop, and how is data loss prevented?

Hadoop prevents data loss through replication. When a data block is stored, it creates at least two to three copies (replicas) of the block and stores them on different nodes. If one node fails, the system can access the replica from another node, ensuring data reliability.

What is the role of MapReduce in Hadoop?

MapReduce is Hadoop's processing element that allows users to query large datasets. It divides a large problem into smaller tasks (map) and then aggregates the results (reduce), enabling efficient parallel processing across distributed data.