Understanding Live Chat Conversation Data: Hadoop or Spark?
Live chat conversation data is just one category of unstructured data that enterprises need to harness for growth. The already vast amount of unstructured data continues to grow, and less than 1% of it is analyzed. Unlike structured data, which exists in organized columns and rows that make search and data analysis an easy task, unstructured data tends to be messy. It’s of no use until it’s processed, analyzed and evaluated.
Many large companies like Google, Facebook, Amazon, Netflix are leveraging unstructured data to facilitate human decision making, automate simple tasks, and to make the world a smarter place. The term big data is used to describe these unstructured datasets that are so large and complex that traditional database systems, such as MS SQL and MySQL, are incapable of handling them.
It’s not the amount of data that’s important— it’s what organizations do with data that matters most. Data analysis can generate insights that lead to better management practices and strategic business decisions. For example, large sets of live chat conversation data from e-commerce companies can be analyzed by Data Scientists to produce actionable insights that drive live chat sales growth.
Now that we’re familiar with the concept of big data, we need to address how unstructured data is processed. MapReduce was introduced to the world in 2004. It’s a programming model which allows you to process large amounts of data. In the map phase, a block of live chat conversation data is read and processed to produce key value pairs as intermediate outputs. This output is then fed to the Reducer. The Reducer then aggregates those intermediate data tuples (key-value pairs) into a smaller set of tuples which is the final output.
Apache Hadoop was born out of MapReduce in 2006, and it revolutionized the world of Big Data. Hadoop provides a platform to store data in the form of Hadoop Distributed File System. Hadoop uses Master-Slave architecture for analyzing large datasets using the MapReduce paradigm. The major components of Hadoop include HDFS, Hadoop MapReduce, and YARN (Yet Another Resource Negotiator). The computing power of one machine is not sufficient for processing massive data sets. Hadoop makes this possible by distributing the processing load throughout a cluster of nodes, or different computers that send messages to one another.
Apache Spark is a powerful successor to Hadoop. It’s more accessible, powerful, and capable of tackling various big data problems. Its architecture is based on two kinds of abstractions: Resilient Distributed Datasets (RDD) and Directed Acyclic Graphs (DAG). RDDs are a collection of data items that can be split and stored in memory on worker nodes of a spark cluster. The DAG abstraction of Spark helps eliminate the Hadoop MapReduce multistage execution model.
In the words of Rajiv Bhat, Senior Vice President of Data Sciences and Marketplace at InMobi, “Spark is beautiful. With Hadoop, it would take six to seven months to develop a machine learning model. Now, we can do about four models a day”. Thanks to its speed and efficiency, an increasing amount of companies are moving from Apache Hadoop to Spark.
When you look at how the respective compositions of the two frameworks, it’s easy to see that Spark is far more elegant than its predecessor. While both systems utilize a parallel distribution system, Apache Spark overcomes limitations of the Apache Hadoop program, MapReduce, which forced a linear data flow on an otherwise distributed network.
When utilizing the Apache Hadoop framework to process extensive datasets, as in the case of live chat conversation data, MapReduce is the metaphorical bottleneck of the system. It can take minutes, hours, or even days to process a dataset. The Apache Spark framework can be used for real-time data processing and delivers responses to interactive queries within seconds.
One of the major differences between Apache Spark and Hadoop MapReduce is that Spark stores data in memory, whereas MapReduce stores data on the hard disk. As a result, Spark is more efficient when it comes to speed and performance, but it requires machines with a lot of RAM. MapReduce uses replication to achieve fault-tolerance, whereas Spark uses RDDs to achieve this. Finally, it’s easier to program without abstractions in Spark than it is in Hadoop. Programmers have the liberty of performing streaming, batch processing, and machine learning, all in the same cluster. Spark processes jobs 1000 times faster than MapReduce as the network and disk access overhead is eliminated.
Authored by Tushar Pandit, Data Science Advisor to RapportBoost.AI.
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