Hadoop and Spark:
Hadoop is an Apache-based framework and application library which enables the distributed processing across computing clusters for huge data set. Hadoop holds the capability to scale one to thousands of systems which offer local storage and compute power.
The Hadoop framework consists of various modules that work together. The main modules are:
● Hadoop Yarn (Yet Another Resource Negotiator)
● Hadoop Distributed File System (HDFS)
● Hadoop Common
These four are the core Hadoop module. There are various other modules available for Hadoop to simplify the work and boost processing power in big data analytics such as Avro, Pig, Hive, Sqoop, Flume etc. It was initially designed to crawl and search millions of web pages to collect the data in the database, and the result was HDFS and MapReduce. MapReduce is a text processing engine.
The Apache Spark is considered as the fastest engine for processing large scale data. On comparing it with Hadoop, the Spark is 100 times faster than the Hadoop MapReduce. The best part is it can also perform batch processing. Spark is very good at dealing with interactive queries, machine learning and streaming workloads. Spark is broadly accepted in industries due to its real-time data processing feature which in MapReduce disk bound is a batch processing engine.
Spark contains its own page for execution. It can be run through the YARN on Hadoop clusters. Few data scientists think that one day Spark will replace Hadoop with its faster accessibility and processing powers. Spark is also a cluster computing-based framework. It doesn't have its own distributed file system, but it can use the Hadoop distributed file system. To understand the complete processing of Spark framework in big data processing, you can mark your presence in Spark Training offered online. The course provides a self-place and classroom learning and corporate training solutions with blended delivery models.
It's evident that Spark is faster than MapReduce because it processes on memory. Spark uses both memory and disk for processing, while MapReduce strictly relies on disk-based processing. The in-memory processing feature of Spark is beneficial in delivering the real-time analytics of data collected from machine learning, IoT gadgets, monitoring, security, marketing campaigns and social media platforms, while MapReduce uses batch processing and continuously collect data from the sites.
Spark and MapReduce, both are compatible with each other. Spark uses JDBC and ODBC drivers to share the MapReduce compatible file formats, BI tools or data sources.
Both the frameworks are scalable with the HDFS. The only question arises that how large these Hadoop clusters can grow? With the increase in requirement and data sizes, the size of the cluster is also increased maintaining the flow of work.
The Hadoop distributed file system have access control lists (ACLs), service level authorization and permission model which controls users job submission and right permissions. Hadoop also supports Kerberos authentication. To efficiently manage the authentication, various vendors provide Active Directory Kerberos, data encryption and LDAP.
The Spark security supports the shared secret or password authentication mechanism for providing effective authentication. The best part about Spark security, it can leverage the HDFS ACL and file level permissions.
Both the products are open source and free to download from the Apache website. The only price you will pay for executing it on the platform or utilizing other hardware resources. Both the platform falls under the category of white box server systems due to low cost and execution over commodity hardware. MapReduce requires a standard amount of memory as it is based on the disk-based processing. Sometimes a user will have to buy faster and large amount of disks for executing MapReduce. While, Spark need a large amount of memory, but it can also deal with the standard amount of disk which executes on standard processing speed.
On comparing it with MapReduce, Spark is more expensive because it requires a large amount of RAM for processing. But at the same time, it reduces the number of needed resources or system.
Member since: 30th January 2018
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