Choose the Right Framework – Spark and Hadoop
We shall discuss Apache Spark and Hadoop MapReduce and what the key differences are between them. The aim of this article is to help you identify which big data platform is suitable for you.
What is Spark?
Apache Spark is an open-source big data platform responsible for monitoring, preparing and breaking down vast scale data sets that can be either structured or unstructured. It also makes it easy to connect with a variety of data sources like OpenStack Swift, Hadoop Distributed File System (HDFS), Cassandra, Amazon S3, etc.
Sparks also has the ability to totally enhance the efficiency of a big data analytics system to provide meaningful and decisive reports. It additionally performs disk build processing generally to suit substantial data sets in the accessible framework memory.
What is Hadoop?
Hadoop platform is the oldest and widely popular amongst all big data technologies. Apache Hadoop is also an open-source big data system that is primarily responsible to store data sets and process the data in a distributed environment across different groups of systems. It offers enormous data storage for a wide range of information, with effective data processing capacity and, for all intents and purposes, handles various communication tasks across different systems.
Hadoop helps in developing advanced analytical skills to make sense of huge data chunks by using data mining, machine learning, and predictive analytics. Hadoop additionally manages mainstream sorts of big data that can be structured or unstructured. This gives the clients the freedom as far as data collection, data processing and ultimately analytics is concerned.
Key Parameters of Hadoop and Spark
As far as security is concerned, Hadoop will always have an upper hand over Spark. The security in Spark is crude as it features only password protection to protect your data. Nonetheless, several organizations prefer to run Spark on HDFS to pick up the advantage of HDFS ACLs and file level authorizations, which basically simplifies the entire process.
Hadoop MapReduce, on the other hand, is equipped with better security features. HDFS is highly appropriate for ACLs (access control lists) and file permission models. It bolsters a complex yet great security feature known as Kerberos substantiation to monitor the data security systems. Hadoop has the feature to easily integrate with several security systems such as Sentry, Knox Gateway, etc.
When it comes to speed, Apache Spark scores more than Hadoop. It is because of the fact that Hadoop is designed to constantly gather information from different sources regardless of the information type and its storage across the distributed environment by using batch processing.
On the contrary, Apache Spark is quick as it is equipped with in-memory data processing resulting in parallel working and consequently shorter cycle time. Because of in-memory data processing, it gives real-time analysis of the data, which is the ultimate requirement for security analytics, cash flow analysis, credit card processing, IoT sensors, and machine learning.
As far as Hadoop is concerned, it requires an external job scheduler, for example, Oozie, whereas Apache Spark comes with inbuilt in-memory computation, therefore, you don't need any external job scheduler for processing.
Apache Sparks and Hadoop MapReduce are total opposites when it comes to latency. Hadoop MapReduce is a high latency computing framework whereas Spark provides low latency computation.
Both Apache Spark and Hadoop MapReduce are highly scalable. The maximum number of nodes that you can add in Hadoop MapReduce cluster is 14000 whereas, in Spark, the maximum number of nodes that you can add is limited to 8000.
6). Ease of use
Hadoop’s MapReduce model is complex in comparison to Spark's model. Hadoop has to handle low-level APIs whereas, in Spark, data processing can be done using high-level operators.
Apache Spark is equipped with intelligent APIs for Python, Java, Scala, and Spark SQL. Likewise, Spark SQL resembles working of basic SQL, so any SQL developers can easily understand the fundamentals. Spark offers an interactive UI and platform for developers and clients for multi-tasking and gets immediate feedback. Whereas Hadoop MapReduce, interestingly, does not offer any interactive platform for multitasking, instead it provides additional features like Hive and Pig which makes it easier to work with Hadoop MapReduce.
Caching is an important parameter as it is responsible for enhancing the efficiency of the system. Apache Spark can store cache data in the memory for future working, therefore, it saves considerable time. Cache data storing feature is not available in the Hadoop MapReduce model, which is one of the main reasons why the processing speed of Hadoop is not as high compared to that of Apache Spark.
As far as cost is concerned, Hadoop MapReduce model is cheaper in comparison to Apache Hadoop model. The main reason behind the difference in the cost is that Apache Hadoop has the feature of in-memory data processing. Therefore, Apache Spark requires more RAM space so that it can work at standard speed. Heavier RAM installation is the root cause that makes Spark more expensive.
Hadoop is disk bound in nature and cost of hard disk is not much when compared to that of RAM. In any case, not to disregard the fact that Hadoop devours more frameworks for the dissemination of disk I/O over various frameworks, which is not the case with Spark.
It is vital to understand the business requirement before deciding whether to go for Hadoop or Apache. If your business requires you to store humongous data, then Hadoop will be the right choice. If the business requires to process the data at incredible speed so that it fetches you the real-time data analysis, then Spark will be the right choice for you.
Choosing the appropriate big data framework is complex and challenging. Hadoop MapReduce and Apache Spark are the leading big data platforms and both have their pros and cons. It is essential to understand which model will be suitable under what circumstance to get the maximum value for the money that you are investing.
About the Author:
Manchun Pandit loves pursuing excellence through writing and has a passion for technology. He currently writes for JanBaskTraining.com, a global training company that provides e-learning and professional certification training. His work has been published on various sites related to DevOps, Big Data Hadoop, Data Science, and more.
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