With the ever increasing volumes of data to deal with, and work on, the difference between Hadoop and Spark has become an aspect worth learning for both the people working with data, as well as the firms looking for effective ways of working with large volumes of data. These frameworks are both very useful in the analytics of big data as they have revolutionized the concepts that were used before, especially by allowing easier and faster means of working on very big datasets.
What is Hadoop?
Apache Hadoop aggregately answers the need for the management of storage devices and the execution of processing on all the available storage devices. The difference between hadoop and spark will become more evident when the elements comprising it are examined.
Components of Hadoop
– HDFS (hadoop distributed file system): This ensures that data is stored in multiple nodes and can be accessed easily and in full from any node
– MapReduce: It is used to support batch processing
– YARN (yet Another Resource Negotiator): It is used for efficient Resource Management in the cluster
What is Spark?
Apache Spark, on the other hand, better depicts current trends in big data processing. One area where the difference between hadoop and spark is quite vivid is the design of spark.
– RDDs (Resilient Distributed Datasets): This is what enables easier memory based computation
– It covers libraries for streaming, machine learning and graphs
– It supports several programming languages
Main Differences Between Hadoop and Spark
Models of Data Processing
One of the most significant differences between Hadoop and Spark is the processing technique:
– Hadoop powerfully supports only the batch processing of data which is stored on disks through MapReduce
– In contrast, Spark powers the in-memory processing of batches and live streams
Differences in Performance
– Hadoop: Slower as it runs on a disk based processing system
– Spark: Much speedier due to availability of in-memory computation
Usage Convenience
In addition, another major difference between Hadoop and Spark which I feel is worth mentioning is the relatively low level of programming which is associated with them:
– Hadoop uses the Java paradigm of writing programs in a MapReduce fashion
– Similarly, Spark is friendly to use as it offers developer friendly APIs in Scala, Java, or Python.
Eco-System and Ease of Integration
– Single most important function of Hadoop is HDFS.
– Other than that, its eco system is focused more towards Spark where other storage systems can be implemented when need arises.
Fault Handling
– The need for Hadoop is to allow the user to reproduce the data in the format of HDFS for every failure.
– In case of Spark, RDD lineage is utilized to recover from the loss of data.
Resource Consumption
– Hadoop is more suitable for batch processing of a large number of records in certain cases where cost is very critical.
– Spark, however, takes up more resources due to its reliance on in-memory processing.
Appropriate Cases to use Hadoop and Spark
The Cases for Using Hadoop
– Performs well with huge volumes of data when processed in batches over a period of time
– Where usage of Memory is restricted
– Efficient batch processing at a lower cost
The Situations Where Spark may be Appropriate
– Handles data processing and analysis in real-time
– For the training problems, AI problems, component based software development problems, that require solving the problem with the fast computation
– Needs for processing and providing data very fast with low-latency
How to Leverage Both: Incorporating Spark and Hadoop
Interestingly, the difference between hadoop and spark is often not indicative of them existing in their own worlds. Many organizations have mastered the hybrid approach:
– Storage is handled by Hadoop HDFS while
– Processing and analyzing of the data is done by making use of Spark.
Conclusion
Learning about the difference between Hadoop and Spark helps us to choose an optimal Big Data framework. Whereas Hadoop is mainly pivotal for less expensive data processing at work, Spark benefits in improving this speed and dynamic. Depending on the exact purpose of your project, the type of computation required, and the degree of the performance the Answer is.
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