Big data has proven to be a significant part of companies’ operations and decision making. For this reason, businesses must find the best way to present the data to make sound, timely decisions. The companies need to acquire the best services to present their data for proper communication, which is why the services of the top data visualization companies are significant.
MapReduce is a programming model and a related implementation for processing and producing big data sets with a parallel, disseminated algorithm on a cluster. It has been the primary technique for handling big data. However, some weaknesses of MapReduce have started to emerge when it comes to managing real-time data. The following discussion looks at several of Google’s Big Data papers that have been significant in providing alternatives.
Google File System
Google File System dates back to 2003 and was the first of the Big Data papers. It is a distributed file system that splits the files into portions, then stores them redundantly on a cluster of commodity machines.
Big data came to be identified with MapReduce, which originated in 2004. It has been supposed that Google utilized MapReduce to work out its search indices. That is, Google usually had all the crawled web pages sitting on its cluster and every so often would run MapReduce to recalculate everything.
Created in 2006, the Bigtable paper has become the force behind the numerous NoSQL databases, including Cassandra, HBase, and several others. Cassandra’s architecture borrows up to about half of Bigtable, such as the data model, SSTables, and write-ahead logs.
Percolator, which dates back to 2010, contains an in-depth look at how Google keeps its search index up to date. The making of Percolator is based on technologies that are already in effect, such as Bigtable. However, the makers add transactions and locks on rows and tables, together with notifications for amendments in the tables. The notifications are subsequently utilized to trigger the various steps in a computation. By doing so, each update gets to “percolate” through the database.
This method is similar to stream processing frameworks (SPFs), such as Twitter’s Storm or Yahoo’s S4, although this has a significant database. SPF works by using message sharing without shared data. The mechanism eases the reasoning behind the process but lacks a way to access the outcome of the computation—unless you have it stored somewhere in the end.
Google developed and published Pregel in 2010 to make it possible to mine graph data, such as the social graph in an online social network. Pregel’s basic computational model is far complicated than that of MapReduce. This paper exhibits how to implement several algorithms, such as Google’s PageRank, bipartite matching, and shortest path. It is a large possibility that Pregel calls for more rethinking on the part of the implementor than MapReduce or SPF do.
Also created in 2010, Dremel is an interactive database with a SQL-like language to be used with structured data. Dremel stores data internally in a unique format that eases sweeps through the data. Queries are sent down to servers and then summed up as they come back. Some smart data format is additionally put to use for optimal performance.
Overcoming problems associated with MapReduce in handling big data
Google sought to overcome the weaknesses of MapReduce by developing applications to take care of the situations for which MapReduce was not suitable. Google’s actions offer an invaluable example of the whole Big Data industry, as MapReduce may be impossible to scale or simply unfit for some scenarios.
Projects are picking more recent ideas and papers by Google; for instance, Apache Drill went for Dremel Framework, and projects like Apache Giraph and Stanford’s GP have employed Pregel. Other approaches such as stream mining also exist.