Hadoop and Spark are both frameworks in the Big Data environment. Apache Hadoop is a framework to store large amounts of data through distributed computing on different servers. Apache Spark is a framework for processing Big Data in real-time much faster than Hadoop’s MapReduce. Lets find out how to Choose the Right Framework i.e. Hadoop V/s Spark.
Before diving into the topic, take a quick peek at the topics covered:
- What is Apache Hadoop? What are its components?
- What is Apache Spark? What are its component?
- Hadoop v/s Spark
- Which is better, Hadoop or Spark? And in which use-case?
What is Apache Hadoop? What are its components?
Apache Hadoop is an open-source Big Data framework used for storing large datasets in a distributed fashion to enable much faster processing parallelly.
Hadoop uses a network of computers to store these data redundantly to maintain high availability. How Hadoop does this? It breakdown Big Data into smaller chunks of workload across nodes in a cluster. Learning Hadoop will open doors into the Big Data domain, then check out the Hadoop Certification course.
The primary components of Apache Hadoop are Hadoop Distributed File System or HDFS and Yet Another Resource Negotiator or YARN.
HDFS is the file system that Hadoop uses to store large-scale distributed data cheaply and also replicates it to avoid unavailability during failure. YARN, on the other hand, is the computation engine of Hadoop. It processes the Big Data by hosting open-source computing frameworks like MapReduce, Spark, etc.
But MapReduce processes these data by writing it onto disks and then performing analytics operation by retrieving it every time. That makes it relatively slower than Apache Spark.
What is Apache Spark? What are its component?
Apache Spark, as we discussed, earlier is a framework for processing Big Data at a much quicker rate than Apache Hadoop’s MapReduce could ever perform because it performs in-memory computation. Apache Spark can also be on top of Hadoop’s YARN to facilitate Big Data processing instead of MapReduce. If you wish to master Apache Spark, have a look into Spark Training.
The essential components of Apache Spark are:
Resilient Distributed Dataset (RDD) and Spark Core Engine. It is the fundamental data structure of Apache Spark and an immutable distributed collection of objects containing Scala, Java, or Python objects.
Spark Core Engine is the fundamental engine used for processing Big Data parallelly in a distributed fashion. Every other library sits on top of this engine that allows diverse workloads for SQL, Machine Learning, and streaming. This engine is also responsible for fault recovery, memory management, scheduling, distributing, and monitoring jobs on a cluster and interacts with storage systems.
Hadoop v/s Spark
- Performance: Apache Spark processes Big Data much faster than Hadoop’s MapReduce, 100x faster to be accurate. Because Spark uses RAM to keep data rather than storing it in disks, like MapReduce, thus avoids the time it takes to store and retrieve data.
- Expenses: Both Hadoop and Spark are open-source software frameworks. Using them is free, but setting up infrastructure to process Big Data will cost you. Both frameworks can run on commodity hardware. Hadoop uses disks to store and process data, whereas Spark uses RAM to process and keep the data. Relatively, Apache Spark will incur more as RAM costs more than disks. Maybe, in the long run, the chances are that Apache Spark will cost you less as Hadoop requires more hardware to operate efficiently.
- Processing Data: Processing Big Data is more comfortable in both of them. But Apache Spark, due to its in-memory computation, processes Big Data at lightning speeds.
- Security: Both frameworks have regular security measures. Hadoop offers support for Kerberos for authentication. And also provides 3rd party vendor support like Lightweight Directory Access Protocol (LDAP), Access Control Lists (ACLs), offers encryption, etc. Spark offers you shared secret authentication. Spark is a processing engine that can also be hosted on top of YARN and can access ACLs and Kerberos.
Which is better, Hadoop or Spark? And in which use-case?
Hadoop is cheaper than Spark. Hadoop’s MapReduce is efficient in performing Big Data processing. But Apache Spark processes these large-scale data at a very rapid pace, almost 100x faster. So, if you have the necessary capital and need for stream processing real-time data, then go with Apache Spark. And if you are low on capital and batch processing (processing after a regular interval) or moderate and slow processing will get the job done, then go for the Hadoop framework.
You can also use both Apache Hadoop and Spark on top of it, to get the best of both worlds.