Which of the following statement/s is/are true?
(i) Facebook has the world’s largest Hadoop cluster.
(ii) Hadoop 2.0 allows live stream processing of real time data

Question

Which of the following statement/s is/are true?(i) Facebook has the world&rsquo;s largest Hadoop cluster.(ii) Hadoop 2.0 allows live stream processing of real time data

Prepp Team · Accepted Answer

Analyzing Hadoop Statements

The question asks us to evaluate the truthfulness of two statements concerning Hadoop and its capabilities, specifically focusing on large-scale deployments like Facebook's and features introduced in Hadoop 2.0 related to real-time data processing.

Statement (i) Analysis: Facebook's Hadoop Cluster

Statement (i) claims that Facebook has the world’s largest Hadoop cluster. Facebook is a major user of Hadoop and has been instrumental in its development and adoption. They process petabytes of data daily using Hadoop infrastructure. While the exact size of large clusters maintained by various tech giants (like Google, Amazon, etc.) can vary and specific numbers are often proprietary and change over time, Facebook is widely acknowledged to have one of the largest, if not the largest, Hadoop deployments globally for storing and analyzing user data. This statement is generally considered true in the context of big data processing at scale.

Statement (ii) Analysis: Hadoop 2.0 and Stream Processing

Statement (ii) states that Hadoop 2.0 allows live stream processing of real time data. Hadoop itself, traditionally through HDFS and MapReduce, was designed primarily for batch processing of large datasets. However, Hadoop 2.0 introduced Yet Another Resource Negotiator (YARN), which significantly changed Hadoop's architecture. YARN separates the resource management layer from the processing layer, allowing various processing frameworks other than MapReduce to run on the Hadoop cluster. While Hadoop 2.0 (YARN) is not a stream processing engine itself, it provides the foundational platform upon which stream processing frameworks like Apache Spark Streaming, Apache Flink, Apache Storm, etc., can efficiently run and manage resources for real-time data processing. In this sense, Hadoop 2.0's architecture enables and facilitates live stream processing within the Hadoop ecosystem. Therefore, interpreting this statement as Hadoop 2.0 providing the capability to run stream processing applications makes the statement true.

Conclusion: Evaluating Hadoop Statements

Based on our analysis:

Statement (i): Facebook indeed operates one of the largest Hadoop clusters globally, supporting massive data processing needs. This statement is true.
Statement (ii): Hadoop 2.0, through YARN, provides the infrastructure for running various processing frameworks, including those designed for live stream processing. This statement is also true, in the context of the Hadoop ecosystem enabled by Hadoop 2.0.

Since both statements are considered true, the option indicating that both (i) and (ii) are true is the correct choice.

Revision Table: Hadoop Key Concepts

Concept	Description	Relevance to Question
Hadoop	Open-source software framework for storing data and running applications on clusters of commodity hardware.	Core technology discussed in the statements.
Hadoop Cluster	A collection of interconnected computers (nodes) that work together to store and process data using Hadoop.	Statement (i) discusses Facebook's large cluster.
Hadoop 2.0 (YARN)	Major revision of Hadoop introducing YARN, which separates resource management from data processing.	Statement (ii) refers to capabilities introduced in this version.
Batch Processing	Processing large volumes of data collected over a period of time. Traditional Hadoop strength.	Contrasts with stream processing.
Stream Processing	Processing data as it arrives in real time. Enabled on Hadoop 2.0 clusters via frameworks like Spark Streaming, Flink, etc.	Statement (ii) discusses this capability.

Additional Information on Hadoop and Big Data

Hadoop is a foundational technology in the world of big data. It was designed to handle the challenges of storing and processing extremely large datasets that cannot be managed by traditional database systems.

HDFS (Hadoop Distributed File System): This is Hadoop's distributed storage system. It stores data across multiple machines, providing high throughput access to application data. HDFS is fault-tolerant and designed to be deployed on low-cost hardware.
MapReduce: The original processing engine for Hadoop. It's a programming model for processing large datasets with a parallel, distributed algorithm on a cluster. It works in two phases: Map (processes data in parallel) and Reduce (aggregates results).
YARN (Yet Another Resource Negotiator): Introduced in Hadoop 2.0, YARN is the resource management layer. It is responsible for allocating system resources (CPU, memory) to applications running on the cluster and scheduling tasks. This separation allows various engines (MapReduce, Spark, Flink, Tez, etc.) to run on the same Hadoop cluster and share resources efficiently.
Hadoop Ecosystem: Beyond the core components, the Hadoop ecosystem includes numerous related projects like Hive (data warehousing), Pig (scripting for MapReduce), HBase (NoSQL database), Spark (fast processing engine), Flink (stream processing), Kafka (distributed streaming platform), and many others that integrate with Hadoop for different big data tasks.

While statement (ii) might be interpreted strictly as Hadoop's *own* processing capability, in the context of how Hadoop 2.0 is used in real-world big data environments, it absolutely enables and supports live stream processing through its ecosystem projects running on YARN.