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Hadoop vs Spark: Which is Better?

Hadoop vs Spark: Which is Better?

We witness a lot of distributed systems each year due to the massive influx of data. Of course, this data needs to be assembled and managed to help in the decision-making processes of organizations. But with so many systems present, which system should you choose to effectively analyze your data? We have broken down such systems and are left with the two most proficient distributed systems which provide the most mindshare. These are Hadoop and Spark.

Even if we narrowed it down to these two systems, a lot of other questions and confusion arises about the two systems. Which distributed system secures the first position? Which system is more capable of performing a set of functions as compared to the other? Another thing that muddles up our thinking is that, in some instances, Hadoop and Spark work together with the processing data of the Spark that resides in the HDFS. However, both of these systems are considered to be separate entities, and there are marked differences between Hadoop and Spark. This is what this article will disclose to help you pick a side between acquiring Hadoop Certification or Spark Courses.

Understanding Hadoop and Spark

What is Hadoop?

Hadoop is an open-source project of Apache that came to the frontlines in 2006 as a Yahoo project and grew to become one of the top-level projects. The distributed processing present in Hadoop is a general-purpose one, and this system has a large number of important components. All the files which are coded in the format of Hadoop-native are stored in the Hadoop Distributed File System (HDFS).

Another component, YARN, is used to compile the runtimes of various applications and store them. The most important function is MapReduce, which is used to process the data. Primarily, Hadoop is the system that is built-in Java, but it can be accessed by the help of a variety of programming languages.

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What is Spark?

Spark can be considered as a newer project as compared to Hadoop, because it came into existence in 2012 and since then it has been utilized to work on big data. The main difference in both of these systems is that Spark uses memory to process and analyze the data while Hadoop uses HDFS to read and write various files. Spark uses RAM to process the data by utilizing a certain concept called Resilient Distributed Dataset (RDD) and Spark can run alone when the data source is the cluster of Hadoop or by combining it with Mesos.

Comparison of Hadoop with Spark

After understanding what these two entities mean, it is now time to compare and let you figure out which system will better suit your organization.

  1. In Terms of Performance

Spark beats Hadoop in terms of performance, as it works 10 times faster on disk and about 100 times faster in-memory. The main purpose of any organization is to assemble the data, and Spark helps you achieve that because it sorts out 100 terabytes of data approximately three times faster compared to Hadoop. But there are also some instances when Hadoop works faster than Spark, and this is when Spark is connected to various other devices while simultaneously running on YARN. In such cases, Hadoop comes at the top of the list and becomes much more efficient than Spark.

  1. In Terms of Security and Fault Tolerance

When we talk about security and fault tolerance, Hadoop leads the argument because this distributed system is much more fault-tolerant compared to Spark. This is because Hadoop uses various nodes and all the replicated data gets stored in each one of these nodes. Therefore, even if the data gets lost or a machine breaks down, you will have all the data stored somewhere else, which can be recreated in the same format.

The fault tolerance of Spark is achieved through the operations of RDD. But first the data gets stored on HDFS, which becomes fault-tolerant by the courtesy of Hadoop architecture. Once Spark builds an RDD, it remembers how a dataset is created in the first place, and thus it can create another one from scratch. Both of these entities provide security, but the security controls provided by Hadoop are much more finely-grained compared to Spark.

  1. In Terms of Costs

Hadoop and Spark are free open-source projects of Apache, and therefore the installation costs of both of these systems are zero. However, the maintenance costs can be more or less depending upon the system you are using. In general, it is known that Spark is much more expensive compared to Hadoop. Hadoop needs more memory on the disks whereas Spark needs more RAM on the disks to store information. As already mentioned, Spark is newer compared to Hadoop. There are less Spark experts present in the world, which makes it much more costly.

  1. In Terms of Machine Learning

Hadoop has a much more effective system of machine learning, and it possesses various components that can help you write your own algorithms as well. On the other hand, Spark has a library of machine learning which is available in several programming languages. So, if you want to enhance the machine learning part of your systems and make it much more efficient, you should consider Hadoop over Spark.

Final Words

Now, let us decide: Hadoop or Spark? Both of these systems are the hottest topic in the IT world nowadays, and it is highly recommended to incorporate either one of them. For heavy operations, Hadoop can be used. If you want to learn all about Hadoop, enroll in our Hadoop certifications. On the contrary, Spark is considered to be much more flexible, but it can be costly. The implementation of such systems can be made much easier if one knows their features.

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