apache spark
As an open-source parallel processing application, Apache Spark is best suitable for executing massive applications of data analytics across a clustered computer.
It is capable of handling real-time analytics, batch analytics, and data-processing workloads in an efficient manner. Spark provides efficient solutions in terms of lighting fast processing, ease of use, support for sophisticated analytics, and real-time stream processing.
Moreover, Spark can easily run on the existing Hadoop cluster and effortlessly access the Hadoop Distributed File System (HDFS).
Traditionally, Hadoop is utilized to run MapReduce work tasks. These tasks run for a longer duration and take from minutes to hours to complete. However, as Spark is well designed to suit the Hadoop.
It acts as an alternative model to the conventional batch MapReduce. It also can be utilized for processing of real-time streaming data and interactive queries that finishes within a few fractions of minutes.
As Hadoop is best suitable for both, Spark and conventional MapReduce. We should consider Hadoop as a universal-purpose system that is apt for numerous models. Whereas Spark as a substitute for Hadoop MapReduce instead of as an alternative to Hadoop.
As Spark utilizes more RAM in place of disk I/O and network in comparison to Hadoop, it requires a highly efficient physical system for the production of the efficient results. The Spark is comparatively faster as against Hadoop and heavily depends on constantly changing variables.
Spark stores all the valuable data within in-built memory whereas Hadoop saves useful data on the disk. Hadoop achieves minimal fault tolerance with the help of replication, while Spark attains this by using data storage models and Resilient Distributed Datasets (RDD) which also lessens the network I/O.
As mentioned in the Academic Paper of Spark: “RDDs achieve fault tolerance through a notion of lineage. If a partition of an RDD is lost, then it has sufficient data to restore/build that partition.” This abolishes the requirement for replication to attain the minimum fault tolerance.
Most people think that to gain expertise in Spark one needs to learn Hadoop. However, that is not true as Spark is an independent field. Following Hadoop 2.0 and YARN, Spark observed noticeable popularity, owing to the feature that enables it to run along with HDFS as well as other components of Hadoop.
Today, Spark has been turned out to be one of the prominent data processing mechanisms in the ecosystem of Hadoop and is a best suitable option for the entire business community as it offers additional capabilities over Hadoop.
According to several software developers, there is no much difference lies between Hadoop and Spark. For them, Hadoop is a well-defined application which is used to write MapReduce tasks with the help of Java – general purpose programming language.
Whereas Spark is a resourceful library which facilitates parallel computation through function cells. Furthermore, for the system operators, Spark just features additional general skills. Including code deployment and monitoring configuration.
Speed: Apache Spark runs faster since it supports in-memory processing. Though it is capable of using disk for that data which doesn’t fit all into in-memory.
This advanced feature of Spark delivers approximate analytics for real-time streaming data. Which makes it well suitable for machine learning, credit/debit card processing systems, security analytics, as well as IoT’s sensors.
Originally meant for collecting data from a variety of sources on a regular basis irrespective of data types. Hadoop stores the data within the distributed environment. It’s key core, MapReduce solely supports batch processing, while YARN is developed to provide parallel processing over the distributed dataset.
Moreover, Hadoop and Apache Spark cannot be compared in terms of speed as they carry out processing tasks differently.
Ease of Use: Considerably similar to the SQL, Spark’s SQL is much easier to learn and provides easy-to-use APIs for the programming languages – Scala, Python, Java, and Spark SQL. In addition to this, Spark offers an interactive shell to perform necessary actions related to queries and get instant feedback.
With Hadoop, the ingestion of data is much simpler and can be done by either utilizing shell or incorporating it with numerous tools such as Flume, Sqoop, etc.
On the contrary, Hadoop’s component YARN is simply processing application which can be unified with numerous tools – Pig and Hive. By utilizing a SQL kind of interface, Hive performs some of the critical tasks such as writing, reading, as well as managing on the massive data sets within a distributed environment.
Hadoop requires higher versions of Java Runtime Environment (JRE) to run, whereas Spark is capable of running on Mesos, Hadoop, and Cloud-platform. Apart from this, it can even run as a stand-alone application. Moreover, it can easily access diversified data sources, namely, HDFS, HBase, Cassandra, and S3.
Final Remarks
Although Apache Spark Services offers several advanced features over Hadoop, still it requires more time to be a replacement for the Hadoop. At present, to be on the safe side, we can just claim that Spark might be effectively integrated into Hadoop in place of labeling it as an alternative for it.
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