The availability of multiple as well as similar big data frameworks in the market is leading to increased difficulty in choosing the appropriate one. I, in this article, have picked up two such frameworks, Hadoop MapReduce and Apache Spark. This is to provide a fair comparison of both the frameworks. These frameworks are mainly for data processing. In fact, the prime difference amid the two is the method of processing:
- Spark does it in-memory
- Hadoop MapReduce read from and write to a disk
Their distinctive data processing approach affects the performance speed hugely. Spark functions up to 100* times faster than MapReduce. However, the quantity of data MapReduce could process is humongous in comparison with Spark. Companies choose frameworks based on their requirements.
Let me first define both Hadoop and Spark, to set the right context for comparing both the frameworks. Once that is done, we can proceed with the comparison of different parameters.
What is Hadoop?
Apache hadoop is an open-source big data framework built mainly to store data and run applications on a group of product hardware. It offers huge data storage space for all types of data, with efficient processing ability and practically handles numerous corresponding tasks.
Hadoop framework is the pioneer amongst the emerging big data technologies. It assists advanced analytical wits, like, data mining, predictive analytics, and machine learning applications. Hadoop also deals with popular types of big data (structured and unstructured). This provides the users the liberty in terms of collection, analysis, and processing of data way better than its peers.
What is Apache Spark?
Apache Spark is an open-source big data framework, explicitly for managing, processing and analyzing large-scale data sets. It facilitates the accessibility to a variety of data sources like Hadoop Distributed File System (HDFS), OpenStack Swift, Amazon S3, and Cassandra.
Spark provisions in-memory data processing to enhance the performance of big data analytics applications. It also performs predictable disk-built processing mostly to accommodate large data sets in the available system memory.
Parameters Of Hadoop And Spark For Comparison
The security in Spark is primitive, with mere authentication assistance through password verification. However, organizations mostly run Spark on HDFS to gain the benefit of HDFS ACLs and file-level authorizations.
Hadoop MapReduce, in contrast, has better security features. It supports Kerberos substantiation, a complex yet good security feature to manage. Hadoop MapReduce integrates with Hadoop security projects, like Knox Gateway and Sentry. Hadoop’s Distributed File System is also well-suited for access control lists (ACLs) and traditional file permission models.
Spark is fast because it has in-memory processing. Its in-memory processing provides almost real-time analytics. Thus, Spark framework is ideal for the credit card processing system, security analytics, machine learning, and IoT sensors.
Hadoop design is to unceasingly collect data from various sources irrespective of the data type and its storage across the distributed environment. Hadoop MapReduce uses batch processing.
Both Hadoop and Spark are compatible with each other. Spark integrates with all the data sources and file formats that Hadoop supports. Thus, it can be said that Spark has similarities with Hadoop like having a good rapport with data sources and types, file formats, and business intelligence tools via JDBC (Java Database Connectivity) and ODBC (Oracle Database Connectivity).
Ease of Use
Spark possess comprehensible APIs for Scala, Python, Java and Spark SQL. Also, Spark SQL is like SQL, so it is easier for SQL developers to learn it. Spark offers the interactive space for developers and users alike query and conducts multiple actions and get immediate feedback.
MapReduce, in contrast, does not offer any interactive platform, however, the add-ons like Hive and Pig thus working becomes easier with MapReduce for adopters.
As Hadoop MapReduce and Apache Spark are open-source projects, the software is for free of cost. Cost is only for the infrastructure. Apache Spark does in-memory processing, it requires more RAM space, however, it can operate at standard speed and quantity of disk. Spark is expensive as RAM is a costly investment.
Hadoop, on the other hand, is disk-bound and hard disks are cheaper comparatively. However, not to ignore the fact that, Hadoop consumes more systems for distribution of disk I/O over numerous systems and Spark does not.
Thus, as far as money matters, organizations must prioritize their necessities. If the need is to process large chunks of big data, Hadoop is a wise choice as it is economical.
Which Framework To Choose Between Hadoop And Spark
Both Hadoop and Spark are worthy investments. Hadoop is efficient in linear processing of large datasets, whereas Spark adds on fast performance, real-time analytics, iterative and graph processing, machine learning and so on. There are instances when Spark may outpace Hadoop MapReduce as it is a recent, evolved and flexible framework compared to MapReduce. The good news is that Spark completely syncs and works effortlessly with Hadoop eco-system.
So, to conclude it could be said that It’s your business needs that should determine the choice of a framework– Hadoop MapReduce or Apache Spark and there exists no generalized conclusion of which framework to choose.
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