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JA2. Apache Spark vs. Apache Hadoop

Statement

Reflect on the learning from this week around Apache Spark and discuss the following:

  • Distinguish the differences between the Apache Spark and Apache Hadoop frameworks. You need to write at least 4 points each.
  • Which framework do you feel provides faster processing and analysis features of big data? Justify your response with supporting references.

Answer

Introduction

Hadoop is an open-source framework written in Java for storing and processing data in a distributed manner using horizontal scaling through clustering of commodity hardware. It was started in 2002 by Doug Cutting and Mike Cafarella, and the V1.0 was released in 2011 (Aggawral, 2019).

Spark is an open-source framework written in Scala for in-memory processing of big data leveraging Hadoop components such as YARN and HDFS. It was started in 2009 by Matei Zaharia at UC Berkeley’s AMPLab and it supports various programming languages such as Java, Scala, Python, and R (Introduction to Apache Spark, n.d.).

Differences between Apache Spark and Apache Hadoop

Components are the first difference. Hadoop has four main components: Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce, and Hadoop Common Utilities. Spark has three main components: Spark Core, Spark SQL, and Spark Streaming along with additional components to support machine learning and graph processing (Hadoop vs Spark - Difference Between Apache Frameworks - AWS, 2023).

Architecture is the second difference. Hadoop processes data stored on disk leveraging HDFS and MapReduce, where batches of data are read from disk, processed, and results are written back to disk. Spark processes data in memory, but its architecture is more flexible to use its own file system or HDFS, or event connecting to external storage systems like Redshift or Cassandra (Hadoop vs Spark - Difference Between Apache Frameworks - AWS, 2023).

Performance is the third difference. Spark is faster as it processes data in memory. Hadoop is slower as reading and writing data back to disk is time-consuming. Spark is up to 100 times faster than Hadoop, but that number in reality is much less. Hadoop, however, has an advantage in long-running workloads (Lawton, 2022).

Scalability is the fourth difference. Hadoop is more scalable as it is easy to add more nodes to the cluster to increase resources. Spark scalability is a bit more challenging as it usually requires more memory and simply adding notes may not fit properly and it is usually more expensive (IBM Cloud Education, 2021).

Which framework do you feel provides faster processing and analysis features of big data

The answer to which is faster depends on the use case, the context of the problem and the characteristics of the data being analyzed. Hadoop vs Spark - Difference Between Apache Frameworks - AWS. (2023) lists a few things to consider when choosing between Hadoop and Spark:

  • Cost-effective scaling: If budget is a concern, Hadoop is more suitable as it is cheaper to add more average nodes to the cluster than to add pure memory nodes for Spark.
  • Batch vs Real-time processing: Hadoop processes data in batches which makes it more suitable for long-running background jobs that are time-insensitive. Spark processes data in real-time which means that it processes each record as it comes in and the results are available immediately.
  • Machine learning capability: Spark excels in this area as it has a built-in machine learning library; and while it is possible to add such capabilities to Hadoop, it is not as straightforward as in Spark.
  • Security: Hadoop has better security features than Spark, as it has been around longer and has more mature security features.

So, although Spark is faster in processing data; speed is not everything and it is hard to tell which one is faster at processing without doing some analysis of the data and the use case.

Conclusion

The text discussed the differences between Apache Spark and Apache Hadoop frameworks, focusing on components, architecture, performance, and scalability. While Spark is faster in processing data, Hadoop has its own advantages in terms of cost-effective scaling and scalability, while Spark has advantages in real-time processing and machine learning capability. The choice between the two frameworks depends on the use case, the context of the problem, and the characteristics of the data being analyzed.

References

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