In this post, we have covered key differences between Apache Spark Vs Hadoop MapReduce.
As we felt that people getting Confused between Apache Spark & Hadoop MapReduce, so we thought of writing this blog and if you go through post completely, you will find all your doubts have been cleared.
If you are just starting out in BigData & Hadoop then I highly recommend you to go through these post first:
- Big Data Hadoop Keypoints & Things you must know to Start learning Big Data & Hadoop, check here
- Big Data & Hadoop Overview, Concepts, Architecture, including Hadoop Distributed File System (HDFS), Check here
Key Differences Between Hadoop & Spark
Hadoop and Apache Spark are both big-data frameworks, but they don’t really serve the same purposes. Hadoop is essentially a distributed data infrastructure: It distributes massive data collections across multiple nodes within a cluster of commodity servers, which means you don’t need to buy and maintain expensive custom hardware. Spark, on the other hand, is a data-processing tool that operates on those distributed data collections; it doesn’t do distributed storage
- Apache Spark: Apache Spark is a general-purpose & lightning fast cluster computing system. It provides high-level API. For example, Java, Scala, Python and R. Apache Spark is a tool for Running Spark Applications. Spark is 100 times faster than Bigdata Hadoop and 10 times faster than accessing data from disk.
- Hadoop: Hadoop is an open source, Scalable, and Fault-tolerant framework. It efficiently processes large volumes of data on a cluster of commodity hardware. Hadoop is not only a storage system but is a platform for large data storage as well as processing.
Wise Comparison Between Apache Spark & Hadoop:
- Apache Spark – Spark is lightning fast cluster computing tool. Apache Spark runs applications up to 100x faster in memory and 10x faster on disk than Hadoop. Because of reducing the number of read/write cycle to disk and storing intermediate data in-memory Spark makes it possible.
- Hadoop MapReduce – MapReduce reads and writes from disk, as a result, it slows down the processing speed.
- Apache Spark – Spark is easy to program as it has tons of high-level operators with RDD – Resilient Distributed Dataset.
- Hadoop MapReduce – In MapReduce, developers need to hand code each and every operation which makes it very difficult to work.
3. Easy to Manage:
- Apache Spark – Spark is capable of performing batch, interactive and Machine Learning and Streaming all in the same cluster. As a result, makes it a complete data analytics engine. Thus, no need to manage different component for each need. Installing Spark on a cluster will be enough to handle all the requirements.
- Hadoop MapReduce – As MapReduce only provides the batch engine. Hence, we are dependent on different engines. For example- Storm, Giraph, Impala, etc. for other requirements. So, it is very difficult to manage many components.
4. Real-time analysis:
- Apache Spark – It can process real-time data i.e. data coming from the real-time event streams at the rate of millions of events per second, e.g. Twitter data for instance or Facebook sharing/posting. Spark’s strength is the ability to process live streams efficiently.
- Hadoop MapReduce – MapReduce fails when it comes to real-time data processing as it was designed to perform batch processing on voluminous amounts of data.
5. Fault tolerance:
- Apache Spark – Spark is fault-tolerant. As a result, there is no need to restart the application from scratch in case of any failure.
- Hadoop MapReduce – Like Apache Spark, MapReduce is also fault-tolerant, so there is no need to restart the application from scratch in case of any failure.
- Apache Spark – Spark is little less secure in comparison to MapReduce because it supports the only authentication through shared secret password authentication.
- Hadoop MapReduce – Apache Hadoop MapReduce is more secure because of Kerberos and it also supports Access Control Lists (ACLs) which are a traditional file permission model.
You can use one without the other: Hadoop includes not just a storage component, known as the Hadoop Distributed File System, but also a processing component called MapReduce, so you don’t need Spark to get your processing done. Conversely, you can also use Spark without Hadoop. Spark does not come with its own file management system, though, so it needs to be integrated with one — if not HDFS, then another cloud-based data platform. Spark was designed for Hadoop, however, so many agree they’re better together.
Conclusion: Spark is an extension to Hadoop. Though you can run Spark in standalone mode, if Spark is integrated on the top of Hadoop, its processing capabilities speed up with the number commodity hardware running in Hadoop cluster
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