In the today’s era of data explosion, the comprehension of the massive amount of unprecedented datasets have emerged by both the big and small companies to survive competition. Not only data analysis but data visualization has also become essential for companies to bring out meaningful results from unlimited data. Big data has emerged as a game changer and has replaced the traditional data processing ways that goes beyond Excel charts, graphs, and pivot tables – the data visualization tools.
Big data allows you to make real time personalization by making it possible for you to track the behavior of individual customers from the Internet clicks they make. Based on their websites surfing behavior, businesses canoffer their consumers products and services they need.
The two most common technologies that come to mind when you think about Big Data is Apache Hadoop and Apache Spark. Although, both technologies are used for distributed storage and to process big data sets, none of them is the replacement of the other.
Hadoop has emerged as a mature and cost effective way to manage large data sets with its MapReduce programming model. MapReduce is best suited for fast data processing of static data. It does batch processing of static data in most effective way. However, it is not suitable for iterative work. Hadoop was initially used for logs processing and analyzing. Hadoop uses HDFS and works with datasets loaded from disks, which makes its performance slow because of continuous disk IO operations, data serialization and replication of data in HDFS.
Apache Spark is capable of working with large datasets in memory itself and can process terabytes of real time streaming data, across number of machines. Spark is easier to learn and use as compared to Hadoop. It is 100 times faster than Hadoop and does not use MapReduce execution engine but uses its own distributed runtime for generalized computation. It uses iterative shell and its API supports multiple languages.
Spark is best suited for applications that runs on iterative model and performs continuous read and write operations. It is best to be used in situations where data is few hundred gigs and can fit in memory.
However, the major disadvantage of using Spark is that it does not have a storage layer and uses Hadoop HDFS. In addition it is less fault tolerant as compared to Hadoop. So if you need high fault tolerance than you should use Hadoop instead of Spark.
Hadoop and Spark can be used in collaboration in scenarios where you have to perform many data transformations from huge data sets. You need intermediate data storage for data processing and also need to store data on disk. In such a scenario, you can use Spark for intermediate processing of data in memory and Hadoop to store data on disk. Using any one technology in such a scenario can give you a tough time because Sparks alone is not fit to store production workloads and Hadoop alone cannot provide fast execution time. Using both technologies together can give you smooth data flow and faster data processing.
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