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    Apache Spark vs Hadoop MapReduce: Choosing the Right Big Data Framework

    Apache Spark vs Hadoop MapReduce: Choosing the Right Big Data Framework

    Ilya Dudkin


    9 min read

    spark vs hadoop

    Analyzing big data has become the key to success in all industries. Businesses are always trying to get more insights from everything that they amass has led to a demand for frameworks that can give us the most information. To solve these issues, a lot of people are turning to Apache Spark and Hadoop, however, it can be challenging to pick the one that is right for you. In order to assist you with making such a decision, we created a comparison of Hadoop vs Spark which will show you the benefits of each.    

    Defining the Two Big Data Frameworks 

    Before we get into answering more detailed questions such as how much faster is Apache Spark compared to Hadoop MapReduce, it would be useful to get a definition of each first. Apache Spark is a framework that will allow you to analyze data right away and mostly performs in-memory calculations. Its blazing speed alone is attractive to many experts. Furthermore, you will be able to use it as a tool all by itself or in combination with Hadoop YARN. These factors have led to many declaring the fall of Hadoop and hailing the rise of Sparks as its permanent replacement.   

    As far as Hadoop is concerned, with the added boost of MapReduce, it first stores all the information before processing it. Since it loses the velocity contest, it is still very much useful since it can be used to process large volumes of data with the fraction of the memory.   

    Spark vs MapReduce Performance   

    While it is tempting to look at the velocity of Spark and immediately declare it the winner, things are not so easy when we dig down further. As we mentioned above, Spark uses in-memory data processing. However, this means that it will use up a lot of memory since it will require a lot of data caching until processing is complete. Therefore, prior to choosing Spark, be sure that you have enough memory to use it.    

    If velocity is not at the top of your priorities list, go with MapReduce because it can handle avalanches of data without eating up too much memory. Hadoop first stores the data and then processes it. Its intended purpose was to process data that was collected from lots of different sources.  

    difference between hadoop and spark

    Apache Spark vs Hadoop MapReduce Language   

    In addition to different ways of handling data, the languages that these two use, are not the same either. Hadoop is written in Java, but you will also find situations where Python is used. Conversely, Spark is written in Scala but will also include APIs for Java and a whole bunch of other languages that are easier to program in. The choice of language gives developers much more flexibility in terms of language and can also make it easier to learn.    

    Apache Spark vs Hadoop MapReduce Cost 

    You might altogether avoid installation costs since both are open source. However, the price tag, as far as development is concerned might span the gamut. If you are on a tight budget, Hadoop would be a better option since it does not need as much RAM and can run on commodity software. However, even though Spark might need more expensive systems, it will require fewer computation units which may enable you to find some areas to save money.     

    If you are wondering whether or not you need Apache Spark software development, it would be best suited for the following use cases:   

    • Real-time analysis 
    • Instant results from analyzed   
    • Repetitive operations   
    • Machine Learning algorithms   

    Hadoop would be better used for:  

    • Analyzing archived data   
    • Commodity hardware operations   
    • No rush data analysis   
    • Linear data processing   
    big data frameworks


    While we provided some information on the difference between Hadoop and Spark and why is Spark faster than Hadoop, a lot will depend on your project requirements and the expertise you have on staff. It might appear at first glance that Spark is a newer better version than Hadoop, but this is not the case, and it is a good idea to conduct even more in-depth research into big data vs Hadoop to find out if it a good fit for you. As far as expertise is concerned, if your team is not comfortable using Hadoop or has no experience with it whatsoever, it could be very challenging to learn considering its heavy use of Java which is difficult to program with. If this is the case, then it would be better to use Spark since it is much easier code and comes with an interactive mode that allows you to receive instant feedback after running commands.    

    Also, it is worth noting that even though we compared and contrasted Hadoop and Spark, a good situation is when you do not have to choose and can use both. You might have noticed the symbiotic relationship these frameworks have. Spark is utterly compatible with Hadoop and works well with the distributed file system. Therefore, if you decided on one of them, consider using the other alongside to achieve comprehensive analysis. By using the two frameworks together, you will be able to get faster analytics, optimized costs and avoid duplication. A lot of big-name companies such as Yahoo, Amazon, and eBay are already using the two combinations and judging from the level of growth they have experienced it seems to be working well for them.

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