You may have heard the word “big data” being thrown around quite a lot. It means what you’d expect it to mean literally. Businesses have to handle large amounts of data as they scale up and that’s also when it becomes progressively harder to extract value out of the data.

This is where big data analytics comes in. It specifically deals with large volumes of both structured and unstructured data that's hard to almost impossible to process with conventional methods. The insights gleaned from the analysis of this data can help with smart decision making.

There are several frameworks that are widely used to work with big data. Frameworks themselves are simply the toolsets that provide the solutions to the problems posed by big data processing.

These tools also provide insights, incorporate metadata and help in decision making. Hadoop and Spark are two of the most widely used big data frameworks.

What is Hadoop?

Hadoop is one of the most popular big data frameworks in use today. Its popularity has actually made it synonymous with big data. Hadoop was one of the first frameworks of its kind which is one of the reasons why its adoption is so widespread.

Hadoop was created in 2006 by Yahoo. It was based on the Google File System and MapReduce. The company started using Hadoop on a 1000 node cluster the following year. Hadoop would then go on to be released as an open source project to Apache Software Foundation in 2008.

With Hadoop, it's possible to store big data in a distributed environment. This allows for the data to be processed parallely. Hadoop has two core components that include the HDFS or Hadoop distributed File System which stores data of various formats across a cluster.

YARN is the second component and it's tasked with resource management. It's the component that handles all of the processing activities by allocating resources and scheduling tasks. It allows parallel processing over the data.


  1. Scalability

    The ease at which Hadoop can be scaled up is one of its biggest advantages. The framework is based on the principle of horizontal scalability. It allows storage and distribution of big data across many different servers that operate in parallel.

    It's easy to add nodes to a Hadoop cluster on the fly which speeds up the scale at which the cluster size can grow.
  2. Open Source

    Hadoop is open source. What that means is that the source code for Hadoop is available for free. Anyone can take the code and modify it to suit their specific requirement without any issues. This is one of the reasons why Hadoop remains such a widely used big data framework.
  3. Performance

    Hadoop is capable of processing very large amounts of data at incredible speeds, made possible by its distributed processing and storage architecture. The input data files are divided into blocks that are then stored over several nodes.

    The tasks that are submitted by the user are also divided into sub-tasks that are assigned to worker nodes and are run in parallel.


  1. Perturbed by small data

    Hadoop is widely regarded as strictly a big data framework even though there are some other frameworks that work just as well with small data. Hadoop doesn't run into issues even when handling a small number of very large files but it can't deal with a large number of small files.

    Any file that's smaller than Hadoop's block size, which can either be 128MB or 256B, can overload the Namenode and disrupt the framework's function.
  2. Security concerns

    Hadoop has remained one of the most widely used big data frameworks despite the fact that there exist some security concerns.

    Those concerns largely stem from the fact that Hadoop is written in Java which happens to be a very common programming language. It's relatively easier for cyber criminals to exploit Java vulnerabilities.
  3. Higher processing overhead

    Hadoop is a batch processing engine at its core. All of the data is read via the disk and written to it as well. This can make the read and write operations quite expensive, particularly when the framework deals with petabytes of data.

    This processing overhead happens because Hadoop is unable to perform in-memory calculations.

What is Spark?

Spark is also a popular big data framework that was engineered from the ground up for speed. It utilizes in-memory processing and other optimizations to be significantly faster than Hadoop. It's faster because Spark runs on RAM, making data processing much faster than it is on disk drives.

Spark got its start as a research project in 2009. It was then open sourced in early 2020 and grew into a broad developer community in the years that followed. Spark moved to the Apache Software Foundation in 2013.

This general-purpose distributed processing framework can also distribute data across the cluster and then process is parallely, much like Hadoop.

A dedicated storage layer wasn't planned for Spark initially as the idea was to target the user base that already had their data in HDFS.


  1. Speed

    The sheer speed at which Spark is able to process large amounts of data is one of its primary advantages. Data scientists prefer Spark because of its speed and the fact that it's 100x faster than Hadoop for large scale data processing. It can easily work with multiple petabytes of clustered data of over 8000 nodes at the same time.
  2. Multilingual

    Unlike Hadoop which is only written in Java, Spark happens to be multilingual. It works with many languages for code writing, including but not limited to Java, Scala and Python.

    This also allows for enhanced security since code writing isn’t just limited to one language. It presents a bigger challenge to cyber criminals who look to exploit vulnerabilities in the programming languages to gain access to the framework.
  3. Powerful capabilities

    Spark has powerful capabilities that expand beyond what Hadoop can achieve. Its low-latency in-memory processing capability is paired with graph analytics algorithms. It also supports advanced analytics tools like streaming data, SQL queries, machine learning, and more.

    This definitely allows an organization to get much more out of their implementation, with enhanced insights into the data allowing them to make informed business decisions rapidly.


  1. No file management system

    Unlike Hadoop, which has its own file management system, Spark doesn't have one. This was a conscious choice made for the framework when it was first being engineered.

    Spark is thus dependent on other platforms like Hadoop or cloud-based storage platforms for its file management system.
  2. Processing isn’t cost-efficient

    Cost-efficient big data processing is an ideal that organizations chase but it can prove to be challenging with Spark. That's because of its in-memory processing. Spark requires high memory consumption.

    A lot of RAM is required for it to work as it was intended, and that can result in a higher processing cost.
  3. Manual back pressure handling

    Spark requires manual handling of back pressure, the point when data builds up at an input-output and the buffer is full and not able to accept any incoming data. Therefore, no data is transferred until the buffer is empty. Spark can’t do that on its own and requires manual input for the flow of incoming data to be accepted once again.

Which should you use?

Hadoop and Spark are not mutually exclusive. There's a case to be made for a hybrid arrangement that involves the use of both. The decision to pick one over the other has to be based on a variety of factors, including but not limited to running costs, hardware and maintenance purchases.

Hadoop requires more memory on disk whereas Spark needs more RAM. Consequently, Spark clusters end up being more expensive. A balance can be struck, though, when optimizing Spark for compute time since similar tasks can be processed much faster on a Spark cluster.

Many are of the view that Spark could end up replacing Hadoop altogether because of its raw speed. There are cases, though, where both of these frameworks can be used together. Such a hybrid arrangement can be reached in which both frameworks complement each other instead of competing.

This will be particularly beneficial for organizations that require batch and stream analysis for different services. Hadoop can be kept for heavier operations and it can deal with them at a lower price whereas Spark can exclusively be used to process many smaller tasks that require an immediate turnaround.

When deployed in isolation, Hadoop is best suited for disk-heavy operations whereas Spark happens to be more flexible but it comes at a higher cost due to its in-memory processing. An organization that only needs a big data framework for disk-heavy operations and isn’t particularly interested in turnaround time will fare better with Hadoop.

Those who prefer outright speed and don’t mind the higher processing costs will definitely be more satisfied with Spark. As far as the speed is concerned, there’s absolutely no comparison between the two, as Spark is proven to be 100x faster than Hadoop.