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Differences between Big Data Hadoop and Spark you should Know
What you observe when you listen to any conversation regarding big data? Of course, you listen to a lot of about Hadoop or Apache Spark.
In case you want to know more these two technologies, keep reading this blog……..
They might sound the same but they do different things
Remember, they do different things, although they might sound the same. Yeah, both are the big data frameworks, but do not really serve the same purpose. Hadoop is a distributed data infrastructure or framework that distributes massive data sets across multiple nodes in a cluster of commodity server that reduces the chances of buying and maintaining expensive custom hardware. Also, there are indexes that keep track of the data, which enables the big data processing as well as analytics in a far more efficient way than what was possible earlier. Whereas, Spark is a data processing tool that runs on those distributed data sets. It doesn’t carry out distributed storage functionality.
Both can be used separately
In Hadoop there is not only a storage component called Hadoop Distributed File System but there is also a processing component called MapReduce. It means you don’t need Spark to get your processing done. On the other hand, you can use Spark separately without a need of Hadoop. Spark doesn’t have its own file management system; however, it has to be integrated with one, in case there is no HDFS then another cloud-based data platform. Spark was introduced for Hadoop, but many experts believe that they work better together.
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 experts agree they're better together.
Spark is faster than Hadoop
Spark is typically a lot faster than MapReduce. How come? Due to its way of processing data. While MapReduce runs in steps, Spark runs on the whole data set all at once.
The MapReduce workflow appears like this-
Spark on the other hand, performs the full data analytics tasks in-memory and in close real-time.
It shows that Spark can be as much as ten times faster than MapReduce for tasks like batch processing and up to hundred times faster in memory analytics.
You may not need the speed of Spark
The processing style of MapReduce can be just okay in case your data operations and reporting necessities are of static nature and you can wait for batch-mode processing. However, if you want to execute analytics on streaming data, like from a sensor fitted on the factory floor, or have apps that call for multiple operations, you definitely want to choose Spark. Most of the machine-learning algorithms need multiple operations. Common apps for Spark involve real-time marketing campaigns, cybersecurity analytics, online product recommendations and machine log monitoring.
Recovery from failure
Although it’s different, but it’s good. Hadoop by nature is flexible to system errors or failures since data are written to the disk after the execution of every operation, however, Spark has same built-in flexibility by good quality of the fact that its data objects are saved in something known as resilient distributed datasets distributed across the data cluster.
You can store these data objects in memory or on disks. The RDD offers full recovery from errors or failures.
Now, you saw that Hadoop and Spark are better together, so if you want any experience in that you can choose Hadoop training institutes in Delhi to find reliable Hadoop training courses.
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