Big Data Technologies Frameworks for Enterprise
Big Data
Three Quintilian bytes of data are created every day. According to Wikipedia, this is called ‘Big Data.’ Big data is the term for a collection of information sets so large and complex that they are difficult to process using on-hand database management tools or traditional data processing applications.
The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization.
The trend to more massive data sets is due to the additional information derivable from analysis of a single extensive collection of related data as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to “spot business trends, determine the quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions."
The following characteristics describe big data:
Volume: The quantity of data generated and stored
Variety: Type and nature of data
Velocity: Speed at which data is generated and processed
Variability: Consistency of the data set
Veracity: Quality and accuracy of the data
Main components of big data
Techniques for analyzing data, like statistical testing, natural language processing, and machine learning
Big data technologies, such as cloud computing, business intelligence, and databases
Visualization, using graphs, charts, trees, and other display of data
Big data service or big data as a service is nothing but the delivery of statistical analysis tools or information by an outside provider to the organization. There are lots of companies providing big data services to their clients, such as consulting (i.e., data advisory, technology selection, and architecture advisory), data integration and management (extraction of big data from various sources and processing of the same), discovery services (visualization services) and so on.
Frameworks
There are many big data technologies frameworks used by different enterprises. Some of them are listed below:
Hadoop: Big data solution provided by Google was developed by Doug Cutting and his team and used an open source project called Hadoop. Hadoop is a framework that allows us to store big data in a distributed environment and process large data sets in a parallel and distributed fashion. Hadoop has two components - one is the HDFS (storage) which allows dumping any data across the cluster, and the second one is MapReduce (processing) which enables parallel processing of the data stored in HDFS.
Apache Spark: Apache Spark is a fast and general purpose cluster computing system for large scale data processing. It has high-level APIs in Java, Scala, Python, and R. It is both suitable for doing both batches based processing and real-time processing. It is intended for extensive – scale data processing. Apache Spark has become one of the largest open source communities in big data for the big data analytics solution.
Apache Spark and Hadoop are the best big data solution for enterprises.
R: Another open source project it is a programming language especially designed for working for statistics. This is darling of data scientists as it supports all the statistical computing and graphics. R is convenient for analysis due to the vast number of packages, readily available tests and the advantage of using formulas, but it can also be used for analysis without the installation of any, and only the big data sets require packages. Many organizations which rank the popularity of languages have said R as one of the most important words of the world. R is the best technology for plot visualization, which is essential for the big data analytics solution.
NoSQL Databases: NoSQL arose to get rid of some of the limitations that were present in the relational databases and mainly focusing on two things - high operational speed and flexibility in storing the data. In NoSQL databases, the data is structured and stored in a free-form format. Accessible NoSQL databases like MongoDB, Redis, Couchbase, and many others. As big data has grown. NoSQL database has become increasingly popular.
The Data Lake: Data lake is a data repository for large quantity and variety of data, both structured and unstructured in the native format. The architecture of a data lake is straightforward: a Hadoop File System (HDFS) with lots of d for easy consumption – the data lake is a large body of water in a more natural state.”Data lakes are designed for big data analytics solution as it solves various data challenges in the big data.
Many service providers are providing big data solution using these technologies frameworks. The market for big data technologies is diverse and continuously changing. Few enterprises have invested in these big data technologies, and many will continue to invest in the future.
