Considered competitors or enemies in Big Data space by many, Apache Hadoop and Apache Spark are the most looked-for technologies and platforms for big data analytics. More interestingly, in the present time, companies that have been managing and performing big data analytics using Hadoop have also started implementing Spark in their everyday organizational and business processes. So, the two niche are used together; like one on top of the other.
Check out the video on PySpark Course to learn more about its concepts:
Hadoop or Spark: Task they performed
Serving the similar purpose of handling large volumes of data, Hadoop and Spark are mutually exclusive in the tasks they perform and their ways of data management.
| · While the former is composed of a distributed file system (HDFS) that stores varieties of data coming from any type and number of dissimilar data sources.
· The idea and basic architecture involves the node-cluster system, where the massive data gets distributed across multiple nodes in single Hadoop cluster. Thus, there isn’t any requirement of any outside custom hardware and thus, no additional costs involved for the maintenance.
| · Conversely, Spark is not a distributed storage framework; it rather supports and encourages reusing the data on distributed collections in an application array.
· In comparison to Hadoop storing data on the disk, Spark is more of in-memory data storage. The primary concept of Apache Spark is Resilient Distributed Datasets (RDDs), which are referred to provide fault-tolerant and efficient mechanisms for disaster and recovery management across multiple clusters.
Many technologists call it as, “Hadoop is putting a Spark into enterprise Big Data.” The major analyst firms’ outlooks for big data reveal that much of the attention is on Hadoop until now.
“In the last quarter of 2015, IBM announced its plans to ingrain Spark into its industry-leading Analytics and Commerce platforms, and to offer Apache Spark as a service on IBM Cloud.” “The Experts also mentioned that IBM will proceed to put more than 3,500 IBM researchers to work on the Spark-related projects.”
“The Hadoop Market is forecast to grow at a compound annual growth rate (CAGR) 58% surpassing $16Billion by 2020.”
Returning to answering the important question of choosing the right Big data tool for better business and organizational processes between Apache Hadoop and Apache Spark, here is a rundown to few key technological differences between these two platforms. Although a difference here won’t tell you what is better, it will guide you to select the right framework according to the requirements and results you expect at that time.
Hadoop vs Spark: They are good together but can be used separately
Like HDFs, Hadoop also consists of an important component called MapReduce (known as the heart of Hadoop). MapReduce is responsible for carrying out all necessary computations across the Hadoop cluster. The data processing is in the hands of MapReduce, which relieves enterprises to introduce Apache Spark framework for data computations.
Correspondingly, Spark too can be implemented without HDFS and MapReduce. Despite no built-in data management system, Spark manages to work without Hadoop. If required, it makes use of other cloud-computing platforms.
However, many a times, both Spark and Hadoop frameworks are said to work together and Spark operates on top of HDFs in many real-time projects now.
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Hadoop vs Spark: Race of Speed
10-100X faster Data Management using Apache Spark
Spark’s capabilities for handling data processing tasks including real-time data streaming and machine learning is way too speedier than MapReduce. It’s in-memory data operations, along with the fast speed, is certainly the reason for the upsurge. Here, the real-time data processing refer to the mechanism that data is fueled into an analytical application the time it is captured and the valuable information is then provided to the user via dashboard for further actions. Most retailers use recommendation engines based on this processing style in several big data applications.
Explaining to non-technical business groups, Park performs all data analytics at once. The sequence of operations is:
- reading data from the cluster,
- Performing analytics operations, and
- Writing the output to the cluster.
On the other hand, Hadoop MapReduce writes all data back to the physical storage disk after each data operation, which makes the process relatively lengthier and time-consuming. The processes involved are-reading data from the cluster, performing cluster operations, writing results to the cluster, again reading updated data from the cluster, performing the analytics, writing back the results and so on.
Spark’s 100X speed isn’t necessary
Even though the data is processed 10 or/and 100 times faster, if the system for which you are doing the big data analytics and processing can wait for batch-mode conversions, this speed of Spark is trivial. MapReduce is the best platform to select since it performs most cost-effective and productive big data processes if the data and information requirements are static. Instead, if your data and business requirements are dynamic, Spark is preferable.
Further, a considerable benefit of Hadoop over Spark’s speed is that if the data size is larger than memory, Spark is not capable of extracting its cache such that it is possible that the Spark’s processing becomes slower than batch processing.
In an interview with Barclays Head of Information, he mentioned, “It was taking about six weeks to process data across its small business customers; with Hadoop that has been reduced to about 21 minutes.”
To conclude this comparison between two popularly used big data tools, even though Spark provides faster batch processing and stream processing for big data, when run on HDFS, it offers reliability and advanced processing power in the same data processing system.
For more in-depth information on Apache Hadoop – read our interactive Apache Hadoop Tutorial.
The opinions expressed in this article are the author’s own and do not reflect the view of the organization.