Understanding Hadoop v/s Spark v/s Storm
May 2, 2016 | Big Data, Technology
Apache Spark and Storm has become quite popular in recent times as the open-source choices for the organizations to support streaming analysis in the Hadoop Stack. What exactly are Hadoop, Spark and Storm frameworks? We will also learn about the similarities and differences among these frameworks.
Hadoop is an open-source distributed processing framework. It is used for storing huge volumes of data and to run distributed analytics processes on various clusters. For companies who have budget and time limitations opt for Hadoop for storing huge data sets quickly. The reason why Hadoop is so efficient is that it doesn’t require big data applications to transmit large volumes of data across the network. In case of Hadoop, another advantage is that the big data applications keep running even if the clusters or individual servers fail. Hadoop MapReduce has a limitation of batch processing one job at a time. This is the reason why Hadoop is mainly being used in data warehousing rather than data analytics.
Spark is a data parallel open-source processing framework. Though Spark workflows are designed in MapReduce but are more efficient than Hadoop MapR. What’s best about Apache Spark is that it doesn’t use YARN for functioning; instead, it has its own streaming API. This allows independent processes for continuous batch processing at short time intervals. In certain scenarios, Spark runs 100 times faster than Hadoop but unlike Hadoop, it doesn’t have its own distributed storage system. Nowadays, you will find most big data projects installing Apache Spark on Hadoop – this allows advanced big data applications to run on Spark using data stored in HDFS.
The storm is a task parallel, open-source processing framework. The storm has its independent workflows in Directed Acyclic Graphs. The topologies in Storm work until there is some flaw or the system shuts down. Apache Storm does not run on Hadoop clusters but uses Zookeeper. It is capable of reading and writing files to Hadoop Distributed Filing System.
Similarities among Hadoop, Spark and Storm
- All three are open-source processing frameworks
- All these frameworks can be used for Business Intelligence and Big Data Analytics
- Each of these frameworks provides fault tolerance and scalability.
- These frameworks are preferred choices for Big Data Developers due to their simple installation methods.
- Hadoop, Spark and Storm have implemented in JVM based programming languages – Java, Scala and Clojure respectively.
How are Hadoop, Spark and Storm different from each other?
- Data Processing Models – Hadoop MapR is best suited only for batch processing. When the requirement rises for real-time options, companies steer towards other platforms like Impala or Storm. Talking about Apache Spark, it does not limit itself to data processing but can process graphs by using existing machine learning libraries. Thus, Spark can be used for batch processing as well as real-time processing.
Micro-batching is a technique that allows a process or task to treat a stream as a sequence of small batches or chunks of data. The storm is a complete stream processing engine which supports micro-batching.
- Performance – Spark processes in-memory data. Hadoop MapR, on the other hand, limits back to disk after a map or a reduce action. Thus, Spark leads ahead of Hadoop MapR in this aspect. Spark requires large memory similar to any other database because it loads the process in the memory and stores it for caching. Whereas, with Hadoop MapR the process is exterminated as soon as the job is done. This makes it possible to steer along with other resource demanding services in a Hadoop MapR scenario.
Talking about Spark and Storm, both provide fault tolerance and scalability but have different processing models. It streams events in small batches in small windows of time before processing while Apache Storm processes one event at a time.
- Development Ease
Developing for Hadoop
Hadoop MapR is written in Java. Hadoop Development is made easier by the use of Apache Pig. Before this, one must learn and understand the syntax of Apache Pig. For lending SQL compatibility to Hadoop, professionals can use Hive on top of Hadoop. Hadoop MapR lacks when it comes to interactive mode but tools like Impala make it a complete package.
Developing for Spark
It uses Scala tuples and they can only be made stronger by housing the generic types because Scala tuples are difficult to be implemented in Java. This, however, does not mean that you have to compromise on time type safety checks.
Developing for Storm
Storm uses Directed Acyclic Graphs which are natural to the processing model. Every node in the DAG can transform the data in some way and continue the process. The data transmission between the nodes in DAG has a natural interface and this happens through Storm tuples. However, this can be achieved by compromising at the expense of compile-time type safety checks.
Big Data Analytics has become one of the most sought-after professions in our times. The numbers of opportunities which arise from this field are overwhelming. There is a continuous demand for professionals who are skilled in technologies like Hadoop, Spark and Storm. A career in Big Data, not only gives you amazing growth opportunities but is also very rewarding financially.
Cognixia has various training programs on these frameworks which help you learn and understand the nuances of Hadoop, Spark and Storm. Our trainers are industry veterans and subject matter experts who train you on these concepts in a comprehensive manner. If you wish to make a career in Big Data Analytics, then you can enrol in our Hadoop Training or the Apache Spark & Storm Training and take your career on an upward trajectory.