Spark vs Hadoop: 10 Key Differences You Should Be Knowing

Spark vs Hadoop: 10 Key Differences You Should Be Knowing
Spark vs Hadoop: 10 Key Differences You Should Be Knowing


Often Spark is mistaken for Hadoop by data analysts and data scientists and vice-versa, the two terms are distinct and have an extensively broad meaning. Although, the field of Spark is analogous to that of Hadoop, yet there is a wide chain of differences between the two.

First, let’s understand the meaning of the two terms and their implications individually, then we shall discuss their differences on various bases to get more clarity on Spark vs Hadoop.

What is Spark?

Apache Spark is an open-source tool. This framework runs in standalone mode  on a cloud or cluster manager. For example : Apache Mesos. It is designed for swift performance and avails RAM for caching and processing data.

Spark performs various sorts of big data workloads. This comes with MapReduce-like batch processing, and real-time stream processing, machine learning, graph computation, and interactive queries. With the help of these easy-to-use high-level APIs, Spark can work with various libraries, such as PyTorch and TensorFlow.

The Spark engine was designed to enhance the efficiency of MapReduce. Spark does not come with a file system, it can access data on various storage solutions. The data structure that Spark is known as the Resilient Distributed Dataset, or RDD.

There are five primary components of Apache Spark:

1. Apache Spark Core: Spark Core is mainly responsible for essential functions including scheduling, task dispatching, input and output operations, fault recovery, and so on. Various other functionalities are built on top of this core.

2. Spark Streaming: It enables the processing of live data streams. Data might initiate from various sources, such as Kafka, Kinesis, Flume, etc.

3. Spark SQL: This component is used for gathering information related to structured data and how it is processed.

4. Machine Learning Library (MLlib): This library is made up of numerous machine learning algorithms. MLlib’s prime objective is scalability and increasing the accessibility of machine learning.

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5. GraphX: A bundle of APIs primarily used for enhancing graph analytics tasks.

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Use-cases of Spark

Spark outperforms Hadoop by providing these additional use cases:

  • The analysis of real-time stream data.
  • In extremely critical cases, Spark provides swift results with in-memory computations.
  • Handling sets of parallel operations using several iterative algorithms.
  • Graph-parallel processing for modelling the data.
  • A wide range of machine learning applications.

What is Hadoop?

Apache Hadoop is a data handling platform that manages large datasets in a distributed manner. This framework avails the MapReduce technique for dividing the data into blocks and allotting these bits to nodes across a cluster. MapReduce computes the data parallely on each node to generate a distinct output.

Each machine in the cluster can both store and process data. Hadoop stores the data into disks using HDFS. This software comes with limitless scalability options. You can even initiate the system with a single machine and then expand to thousands, by including various sorts of enterprise or commodity hardware.

The Hadoop ecosystem is highly fault-tolerant. Hadoop doesn’t rely on hardware for achieving high availability. In the core, Hadoop is built for detecting bugs at the application layer. By duplicating data across a cluster, when a hardware component malfunctions, the framework can reconstruct the missing parts from other locations.

The Apache Hadoop Project consists of four primary modules:

1. HDFS – Hadoop Distributed File System: Thefile system that manages the storage of large sets of data across a Hadoop cluster. HDFS can manage both structured and unstructured data. The storage hardware may vary from any consumer-grade HDDs to enterprise drives.

2. MapReduce: It is the processing component of the Hadoop ecosystem. It allows the data fragments from HDFS to distinct map tasks in the cluster. MapReduce processes the bits parallelly to combine the individual blocks for getting the desired result.

3. YARN – Yet Another Resource Negotiator: Its main task is to manage computing resources and job scheduling.

4. Hadoop Common. A bunch of common libraries and utilities that back up numerous other modules. This module is also known as  Hadoop core, as it enhances the working of all other Hadoop components.

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Use-cases of Hadoop

The popular use cases of Hadoop include:

  • Pre-processing large datasets in ecosystems where data size exceeds the unused memory.
  • Creating data analysis infrastructure with a minimal budget.
  • Completing jobs where immediate results are not required, and time is not a limiting factor.
  • Batch processing with tasks exploiting disk read and write operations.
  • Historical and archive data analysis.

Spark vs Hadoop: A head-to-head comparison

Being a data scientist, you must distinctly understand the difference between the two widely used technical terms: “Spark” and “Hadoop”. After reading the above-mentioned introduction, you must now go through the head-to-head comparison between the two through the difference table given below.

Spark vs Hadoop: Difference Table

Sr. NoBasis Of DifferenceSparkHadoop
1PerformanceFast in-memory performance with minimal disk reading and writing operations. Slower performance,as it relies on disks for storage and adopts its read and write speed. 
2CostEven though it is an open-source platform, it relies on memory for computation, which significantly adds to the running costs.Being an open-source platform,it is less expensive to run. Cheaper consumer hardware. Abundant Hadoop professionals are available. 
3Data ProcessingOptimal for iterative and live-stream data analysis. Compatible with RDDs and DAGs to run operations.Recommended for batch processing. Avails MapReduce to divide a large dataset across a cluster for parallel computation. 
4Fault toleranceIt tracks the RDD block creation process, and then rebuilds a dataset if partition fails. It also uses a DAG to rebuild data across nodes. A highly reliable fault-tolerant system. Duplicates the data across the nodes and backs them up in case of a bug. 
5ScalabilityDifficult to scale as it relies on RAM for computation. Still supports thousands of nodes in a single cluster.Easily scalable by adding nodes and disks for storage. Supports more than 10,000 nodes without a known maxima. 
6Ease of UseHighly user friendly. Comes with an interactive shell mode.More challenging to use with less supported languages.
7Language SupportAPIs are usually written in Java, Scala, R, Python, Spark SQL. Java or Python for MapReduce apps.
8Machine LearningHighly swift in-memory processing. Comes with MLlib for computations. Slower than Spark. Data fragments can be huge and initiate bottlenecks. Mahout is the primary library. 
9Scheduling and Resource ManagementNumerous default tools for resource allocation, scheduling, and monitoring. Depends on external solvess. YARN is most popularly used resource management. Oozie is availed for workflow scheduling.
10SecurityNot that secure. The security is turned off, by default. Depends on integration with Hadoop for attaining security.Highly secure. Backed by LDAP, ACLs, Kerberos, SLAs, etc.

Frequently Asked Questions

Which is better to learn Spark vs Hadoop?

The first and the key difference between Spark vs Hadoop is the capacity of RAM and its usage. Spark consumes higher Random Access Memory than Hadoop, on the other hand, it “avails” a lesser amount of internet or disc memory. Hence, using Hadoop is a better way to build a high computation machine with larger internal storage.

Is Spark part of Hadoop?

Yes, Spark is a part of the Hadoop ecosystem. The Hadoop ecosystem comes with numerous well-known tools including HDFS, Hive, Pig, YARN, MapReduce, Spark, HBase, Oozie, Sqoop, Zookeeper, and so on.

Spark is a swift and general processing engine that can work on Hadoop data. It can be executed in Hadoop clusters with the help of YARN or Spark’s standalone mode. Along with HDFS, it can also process data in HBase, Cassandra, Hive, etc.

How are Hadoop and Spark related?

Hadoop is typically used for batch processing, while Spark is used for batch, graph, machine learning, and iterative processing. Spark is compact and efficient than the Hadoop big data framework.

Hadoop reads and writes files to HDFS, whereas Spark processes data in RAM with the help of a concept known as an RDD, Resilient Distributed Dataset.

Is Hadoop dead?

The adoption of Hadoop storage (HDFS) is declining owing to its complexity and high cost and even because compute fundamentally cannot scale elastically if it is linked to HDFS. Data in the HDFS is now being displaced to an optimal and cost-efficient system, it can be cloud storage or on-prem object storage.

Even, though it is being replaced, Hadoop is not going to be dead soon, as it is still employed for abundant data storage if not for analytics. In the coming years, hybrid methods for data storage and analytics will be introduced by leveraging both cloud-based and on-premise infrastructures.

Does Hadoop use SQL?

Yes, Hadoop uses SQL. SQL is a domain-specific programming language for manipulating data in relational databases. SQL-on-Hadoop is a class of analytical application tools that combine established SQL-style querying with the contemporary Hadoop data framework elements.

By backing up known SQL queries, SQL-on-Hadoop allows a larger group of developers and business analysts to work with Hadoop on commodity computing clusters.

Key Takeaways

Finally, after understanding both these terms we can conclude that both Spark and Hadoop go hand in hand. Spark and Hadoop are mostly used together, such as Spark processes the data which is present in HDFS, Hadoop’s file system. However, both of them are unique and separate entities, each comes with its own pros and cons and specific business use cases.

If you are thinking of building a career in Data Analysis or Data Science with Spark or Hadoop you can learn about a few software including R, Python, SQL, this will help you in dealing with data sets better and devising the algorithms efficiently.

Before getting enrolled in any course understand the technical terms distinctly, so that you get to learn exactly what you have been looking for. You can check out our courses on Data Science and Machine Learning if you wish to build a few projects on your own under the guidance of our Mentors.

By Vanshika Singolia