What are the benefits of Apache Spark over Hadoop?

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Apache Spark and Hadoop stand out as the biggest players in the universe of Big Data and open-source software. Big Data consists of an extensive amount of data which tends to expand at an ever-increasing rate, therefore to process this gigantic diverse information, Spark and Hadoop come into the picture. Although Apache Spark and Hadoop are both processing frameworks, their functionality is quite different, moreover, there is an upper hand of Spark over Hadoop.

What is Apache Spark?

In 2009 Apache Spark began as a research project at the UC Berkeley AMP Lab and open-sourced in 2010 and since then it has earned a prestigious position as a strong clustering system that handles Big Data. Apache Spark is a great fit PySpark well as it is quick and adaptable. Spark specifically handles Big Data Analytics, Machine learning and AI, Graph work, and, Data streams.

Spark can work through data 10-100 times quicker than any other options for example Hadoop. Therefore, many companies prefer Spark over Hadoop as the latter is quick and efficient. Spark pulls this off by spreading out processing work across large groups of computers to run things side by side. Spark can also conveniently work with popular coding languages, such as Python, Java, Scala, etc. As a result, Spark has become the first choice of big companies and organizations.

What is Hadoop?

The Apache Hadoop software library is a strong framework that helps process huge data sets across computer clusters. It does this by using the MapReduce programming model, which makes Hadoop handy and powerful. The framework is built to grow, so it can work on one server or thousands of machines. Each machine adds its computing power and storage, which makes sure data is processed and managed well.

A key strength of Hadoop lies in its capacity to keep running even when some machines in the cluster break down. The system can spot and manage these issues within its application layer, which means the whole setup keeps working without a hitch. This method allows Hadoop to offer a service that goes down even in places where individual computers might stop working from time to time.

Advantages of Spark over Hadoop

Apache Spark and Hadoop fundamentally work on the same principle but differ in various arenas. Therefore, the debates on the advantages of Spark over Hadoop will always persist. Some of the benefits are mentioned below.

1. Processing Speed

Spark retains data in its RAM for a longer period. By storing intermediate data in memory, Spark bypasses the expensive disk read/write operations that Hadoop depends on. Benchmarks often show that Spark performs certain tasks up to 100 times faster than Hadoop MapReduce.

2. User Friendly

Spark supports multiple programming languages:  Java, Scala, Python, and R. It is a lot simpler to use as compared to Hadoop because it does not have low-level MapReduce API like all the other Big Data frameworks. Spark provides over 80 operators for interactive querying- a comprehensive library including support streaming, SQL, and complex analytics. This feature provides merit for Spark over Hadoop and is easy to access.

3. Flexibility in Data Processing

As everyone probably knows, Spark is a unified platform for big data processing that has modules like Spark batch, spark streaming, and so on. Hadoop for the most part focuses on bunch handling through MapReduce and would require external systems like Apache Storm or Apache Flink, that are intended to support real-time processing. Hadoop MapReduce is quite cumbersome to implement those iterative machine learning algorithms while Spark provides elegant APIs and libraries for advanced analytics such as SQL, streaming, or complex data processing workflows.

 4. Dynamic Processing

Spark is well recognized for providing dynamic processing since Flink processes data in micro-batches, insights and response can be near-real-time compared to other systems like Hadoop MapReduce which is designed for batch processing which could result in latency as well. There is always a dominance of Spark over Hadoop since Spark can adapt itself according to the changing needs and can handle the data in real-time.

5. Seamless Integration

Apache Spark can be run on Hadoop and henceforward to its data storage layer, the most common one of which is HDFS and this ease of integration with existing Apache-Hadoop installations has made it very important. Spark is agnostic concerning backend storage systems like HDFS, Apache HBase, Data warehouse, and other big data sources making it a highly flexible framework for batch processing or real-time streaming of data.

6. System Optimization

Dynamic query execution plans, based on runtime data statistics can be optimized and henceforth, performance is aided by this fact. It is also supported by Spark that intermediate results can be cached in memory, which comes in handy in iterative algorithms and repetitive queries. The superiority of Spark over Hadoop can be noted as it provides dynamic resource allocation based on workload and hence efficiently manages resources, scaling them as per the workload requirement.

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Conclusion

To sum up, we can say that the majority of the time there is an advantage of Spark over Hadoop as Spark is an advanced tool for many current data processing tasks. In addition, it works efficiently in solving the problems of big data. This is because Spark can process data in almost real-time due to its unique feature of in-memory processing. In short, we can say that Spark can carry out a variety of convenient tasks as compared to Hadoop.

Recommended Reads

1. Scope of Big Data Analytics Courses in 2024
2. What is Big Data Analytics? Why is it Important?
3. Benefits of Big Data Analytics – With Examples
4. What is AWS Big Data?

FAQ’s

Q1: What is the advantage of Spark over Hadoop?

Ans: Apache Spark is faster than Hadoop because it performs data in memory. It is fast as compared to Hadoop which relies on disk-based processing.

Q2: How does Spark process big data at a speed faster than Hadoop?

Ans: Spark can do In-Memory Computing which stores intermediate data in memory, thereby reducing the need for expensive disk I/O operations which the Hadoop framework relies on.

Q3: Is Spark easier to use than Hadoop?

Ans: Yes, Spark is user-friendly because it provides APIs (Application Programming Interfaces) in many different languages like Java, Scala, Python, etc. Hadoop MapReduce uses Java as its primary language so learning can be tough.

Q4: Can Spark perform real-time processing

Ans: Yes, Spark can perform real-time processing on large data as compared to Hadoop whose MapReduce does not support real-time processing.

Q5: How is Spark fault tolerance is better than Hadoop?

Ans: Spark makes use of Resilient Distributed Datasets (RDDs), through which Spark can recover lost data without depending on replication, as done by Hadoop.

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