Hadoop and Spark, both developed by the Apache Software Foundation, are widely used open-source frameworks for big data architectures. 

We are really at the heart of the Big Data phenomenon right now, and companies can no longer ignore the impact of data on their decision-making.

As a reminder, the data considered Big Data meet three criteria: velocity, speed, and variety. However, you can’t process Big Data with traditional systems and technologies.

It is with a view to overcoming this problem that Apache Software Foundation has proposed the most used solutions, namely Hadoop and Spark.

However, people who are new to big data processing have a hard time understanding these two technologies. To remove all the doubts, in this article, learn the key differences between Hadoop and Spark and when you should choose one or another, or use them together.


Hadoop is a software utility composed of several modules forming an ecosystem for processing Big Data. The principle used by Hadoop for this processing is the distributed distribution of data to process them in parallel.

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The distributed storage system setup of Hadoop is composed of several ordinary computers, thus forming a cluster of several nodes. Adopting this system allows Hadoop to efficiently process the huge amount of available data by performing multiple tasks simultaneously, quickly, and efficiently.

Data processed with Hadoop can take many forms. They can be structured like Excel tables or tables in a conventional DBMS. This data can also be presented in a semi-structured way, such as JSON or XML files. Hadoop also supports unstructured data such as images, videos, or audio files.

Main Components

source: Wikimedia

The main components of Hadoop are:

  • HDFS or Hadoop Distributed File System is the system used by Hadoop to perform distributed data storage. It is composed of a master node containing the cluster metadata and several slave nodes in which the data itself is stored;
  • MapReduce is the algorithmic model used to process this distributed data. This design pattern can be implemented using several programming languages ​​, such as Java, R, Scala, Go, JavaScript or Python. It runs within each node in parallel;
  • Hadoop Common, in which several utilities and libraries support other Hadoop components;
  • YARN is an orchestration tool to manage the resource on the Hadoop cluster and the workload performed by each node. It also supports the implementation of MapReduce since version 2.0 of this framework.

Apache Spark


Apache Spark is an open-source framework initially created by computer scientist Matei Zaharia as part of his doctorate in 2009. He then joined the Apache Software Foundation in 2010.

Spark is a calculation and data processing engine distributed in a distributed manner over several nodes. The main specificity of Spark is that it performs in-memory processing, i.e. it uses RAM to cache and process large data distributed in the cluster. It gives it higher performance and much higher processing speed.

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Spark supports several tasks, including batch processing, real-stream processing, machine learning, and graph computation. We can also process data from several systems, such as HDFS, RDBMS or even NoSQL databases. Spark’s implementation can be done with several languages ​​like Scala or Python.

Main components

The main components of Apache Spark are:

source: AWS
  • Spark Core is the general engine of the whole platform. It is responsible for planning and distributing tasks, coordinating input/output operations or recovering from any breakdowns;
  • Spark SQL is the component providing the RDD schema that supports structured and semi-structured data. In particular, it makes it possible to optimize the collection and processing of structured type data by executing SQL or by providing access to the SQL engine;
  • Spark Streaming which allows streaming data analysis. Spark Streaming supports data from different sources such as Flume, Kinesis, or Kafka;
  • MLib, Apache Spark’s built-in library for machine learning. It provides several machine-learning algorithms as well as several tools to create machine-learning pipelines;
  • GraphX combines a set of APIs for performing modeling, calculations, and graph analyses within a distributed architecture.

Hadoop vs Spark: Differences


Spark is a Big Data calculation and data processing engine. So, in theory, it is a bit like Hadoop MapReduce, which is much faster since it runs in-Memory. Then what makes Hadoop and Spark different? Let’s have a look:

  • Spark is much more efficient, in particular thanks to in-memory processing, while Hadoop proceeds in batches;
  • Spark is much more expensive in terms of cost since it requires a significant amount of RAM to maintain its performance. Hadoop, on the other hand, relies only on an ordinary machine for data processing;
  • Hadoop is more suitable for batch processing, while Spark is most suitable when dealing with streaming data or unstructured data streams;
  • Hadoop is more fault tolerant as it continuously replicates data whereas Spark uses resilient distributed dataset (RDD) which itself relies on HDFS.
  • Hadoop is more scalable, as you only need to add another machine if the existing ones are no longer sufficient. Spark relies on the system of other frameworks, such as HDFS, to extend.
Processing Batch ProcessingIn-memory Processing
File ManagementHDFSUses Hadoop’s HDFS
SpeedFast10 to 1000 times faster
Language SupportJava, Python, Scala, R, Go, and JavaScriptJava, Python, Scala, and R
Fault ToleranceMore TolerantLess Tolerant
CostLess Expensive More Expensive
ScalabilityMore Scalable Less Scalable

Hadoop is Good for

Hadoop is a good solution if processing speed is not critical. For example, if data processing can be done overnight, it makes sense to consider using Hadoop’s MapReduce.

Hadoop allows you to offload large datasets from data warehouses where it is comparatively difficult to process, as Hadoop’s HDFS gives organizations a better way to store and process data.

Spark is Good for:

Spark’s resilient Distributed Datasets (RDDs) allow multiple in-memory map operations, while Hadoop MapReduce has to write interim results to disk which makes Spark a preferred option if you want to do real-time interactive data analysis.

Spark’s in-memory processing and support for distributed databases such as Cassandra or MongoDB is an excellent solution for data migration and insertion – when data is retrieved from a source database and sent to another target system. 

Using Hadoop and Spark Together


Often you have to choose between Hadoop and Spark; however, in most cases, choosing may be unnecessary since these two frameworks can very well coexist and work together. Indeed, the main reason behind developing Spark was to enhance Hadoop rather than replace it.

As we have seen in previous sections, Spark can be integrated with Hadoop using its HDFS storage system. Indeed, they both perform faster data processing within a distributed environment. Similarly, you can allocate data on Hadoop and process it using Spark or run jobs inside Hadoop MapReduce.


Hadoop or Spark? Before choosing the framework, you must consider your architecture, and the technologies composing it must be consistent with the objective you wish to achieve. Moreover, Spark is fully compatible with the Hadoop ecosystem and works seamlessly with Hadoop Distributed File System and Apache Hive.

You may also explore some big data tools.