Apache Spark vs Hadoop

When it comes to choosing the best big data framework, it’s not an easy task. Today we have two popular frameworks namely Apache Spark and Hadoop. Apache spark is considered to be the most demanding big data framework due to its multiple solutions. In this Apache Spark VS Hadoop blog, we are going to explain major differences between these two big data tools; this can be done based on many criteria. As we already have some ideas about Apache Spark and Hadoop tools, Apache spark is an open-source distributed cluster framework whereas Hadoop is an open-source framework written using Java language. Are you excited to learn about your favorite big data framework? Then let’s get started;

What is the Apache Spark Framework?

Apache Spark is one of the popular tools used in big data and it’s an open-source cluster computing framework mainly used in real-time processing. This Apache Spark consists of thriving open-source features and actively used technology in most big data projects. This popular big data tool provides an interface for entire programming clusters with implicit fault-tolerance.

Apache spark processes the data in the form of batches which can be used for real-time processes. This framework runs almost 100 times faster than the competitive framework. It helps to store the data in the RAM memory and also supports caching in-memory data storage capabilities.Because of all these reasons, the Apache framework is emerging as one of the top frameworks used.

  • There are 3 major components of the Apache Spark ecosystem:

Language support: this component helps to integrate with different languages to perform the analytical tasks.

  • The languages are JAVA, Python, R, and Scala.

Core Components: There are 5 main core components available:

1. Spark core

2. Spark SQL

3. Spark streaming

4. Spark MLIib

5. GraphX.

Important features of Apache Spark

The below are the major features of Apache spark:

  • Apache Spark allows users to integrate with Hadoop data.
  • This tool consists of many interactive languages like Shell, Scala.
  • Apache Spark offers multiple analytic tools and they are used for Interactive query analysis, graph processing, and graph processing.
  • The Apache spark consists of RDDs; this can be cached across the computing nodes in multiple data clusters.

Limitations of Apache Spark

Below are the few drawbacks of Apache spark:

  • No real-time data processing and no file management system.
  • Handling black pressure and manual optimizations.
  • Lesser number of algorithms and iterative processing.
  • Expensive and latency.
  • Small file issues and window criteria.

What is the Hadoop framework in big data?

Hadoop is an Apache product and this is nothing but a collection of open-source software framework utilities that provides a network of many computer devices to solve complex problems which involve a massive amount of data and perform computations.This offers a storage framework to process the big data modules with the help of MapReduce programs. This Hadoop also offers massive data storage for any kind of data, enormous data processing power, and allows users to handle enormous concurrent tasks or jobs. This Hadoop framework is written using programming languages like Java, but never makes use of OLAP or online analytical processing.This is used for batch or offline data processing.  Hadoop is used by major companies like Facebook, Yahoo, Google, Linked In, and many more to store a large volume of data.

Features of Hadoop

Below are the few major features of Hadoop:

  • One of the important advantages of Using Hadoop is its cost-effective nature when compared to other traditional database technologies in storing and performing data computations.
  • Hadoop comfortably accesses different kinds of business solution data and also proved its benefits in decision making.
  • This Hadoop also acts as an enabler for social media, log processing, data warehousing, emailing, and error detection.
  • By mapping the data sets wherever it is placed, Hadoop technology minimizes the time taken to unfolding any data sets. It can also work on a large number of petabytes of data on an hourly basis and makes it super-fast.

Limitations of Hadoop

Below are the few drawbacks of the Hadoop framework; they are

  • Hadoop framework is a small file application.
  • Slow in-memory data processing when compared to Apache spark.
  • No real-time data processing.
  • No iterative data processing.
  • You will face a lot of data security problems while working with Hadoop big data framework.

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Major differences between Apache Spark and Hadoop:

In this section, we are going to differentiate between Apache Spark and Hadoop based on a few criteria.

Framework:

 a. Hadoop is an Apache open-source framework that allows distributed processing and analysis of large data sets across multiple clusters of computers using various data processing models.

b. Apache spark is also an open-source distributed general-purpose cluster computing framework.

In-memory data processing:

a. As Hadoop makes use of the Map-reduce algorithm, this is a distributed and parallel processing algorithm mainly used to process large data volumes. This algorithm also needs to perform three types of tasks, they are, Mapping, reducing, and batch processing.

b. Apache spark offers faster in-memory data processing when compared to the Hadoop framework and there is no need to spend time moving or processing data.

Data pipelining:

a. Hadoop framework doesn’t support data pipelining. For example a sequence of stages involved where the previous stage’s output ID will be the next stage’s input Id.

b. Whereas Apache spark supports data pipelining, as it involves providing continuous data input and output. This pipelining process is also known as “Real-time processing”.

Latency:

a. As we know that Hadoop uses a Map-reduce algorithm, which makes the framework slow to perform various activities. This is due to, Map-reduce algorithm supporting different data formats, processing large data volumes, and structures. So Hadoop provides higher latency when compared to Apache.

b. Apache spark offers less latency as it works faster than Hadoop. This is due to using RDD, RDD helps caches most of the data input in its memory. RDD is nothing but Resilient Distribution Datasets which is a fault-tolerated collection of operational datasets that run in parallel environments. There are 2 partitioned datasets available in an RDD as Immutable and distributed. They are; 1. Parallelized collections and 2. Hadoop data sets.

Evaluation:

1. Hadoop always performs slowly when it compares to Apache Spark. This is due to using Map reduce algorithm and also takes time to process the large volume of data sets.

2. Apache Spark offers lazy evaluation this is due to we make use of this framework when we needed it absolutely. So this increases operation speed in the project development environment.

Codes:

1. Hadoop framework is written using JAVA programming codes. So it ejects a lot of codes and developers need to write a large number of code statements.

2. Apache spark framework is written in both Scala and Java languages. The final framework will be implemented using Scala codes, so the numbers of code lines are lesser in Apache Spark when compared to the Hadoop framework.

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Fault tolerance:

a. Hadoop uses replication of data in multiple copies to achieve fault tolerance.

b. Whereas Apache Spark uses resilient distributed data sets (RDD) to achieve fault tolerance.

Uses:

a. This big data framework is mainly used to boost the Hadoop computational process.

b. Whereas Apache Spark is used to manage data storing and processing of big data applications running in clustered systems.

Job scheduler:

a. Hadoop requires an external job scheduler.

b. No such external job scheduler is required in Apache spark as it offers a high level of in-memory processing.

Cost-effective:

a. Hadoop is a cost-effective tool because it offers various options to store large data volumes for deep data analysis purposes.

b. Apache Spark is less cost-effective than the Hadoop framework because it requires a lot of RAM devices to run in-memory data processing.

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Final Words

So as per my knowledge, Apache spark is a widely used framework, this is due to the above reasons. Many top companies use the Apache Spark tool to improve their business and services insights. We use both Hadoop and Apache spark for our day-to-day activities. Even many industry applications implement both these frameworks, for example, e-commerce companies (eBay and Alibaba), health care sectors, Media and entertainment, and retail industries. In some cases, we need Hadoop and Apache Spark together to achieve the highest level of data insight. So in this blog, we have tried our best to explain the major differences between Apache Spark and Hadoop based on various categories such as data processing,cost-effective, uses, latency, evaluation, job scheduler, and fault tolerance. Apache spark VS Hadoop blog helps you to choose the right framework as per project requirements.

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Gayathri
Gayathri
Research Analyst
As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.