Business Intelligence Using Big Data

BUSINESS INTELLIGENCE USING BIG DAT
By Name
Course
Instructor
Institution
Location
Date

Contents
Introduction................................................................................................................ 3
Explaining the Vs of big data...................................................................................3
Big Data Technologies................................................................................................ 4
Hadoop Ecosystem.................................................................................................. 4
Spark....................................................................................................................... 4
R.............................................................................................................................. 5
Data Lakes.............................................................................................................. 5
NoSQL Databases.................................................................................................... 6
Predictive Analytics................................................................................................. 6
Artificial Intelligence................................................................................................ 6
Streaming Analytics................................................................................................ 7
Edge computing...................................................................................................... 7
Blockchain............................................................................................................... 8
Big Data Modeling...................................................................................................... 8
Techniques for Data Modeling.................................................................................9
Data Modeling in Social Networks........................................................................9
Modeling of Data in Cloud Environment.............................................................10
Data Model based on Ontology..........................................................................10
Big Data for United Airlines...................................................................................... 11
Architecture of Big Data used by United Airlines...................................................11
How United Airlines may use big data to customize customer needs...................12
Providing highly personalized offers...................................................................12
Calls that is more insightful................................................................................12
Provision of smarter and safer flights.................................................................13
Provision of real time baggage status................................................................13
References............................................................................................................... 15

Introduction
Big data is a term that is constantly changing which is a description of a large volume of
structured, semi-structured as well as unstructured data which has the ability for being mined for
information as well as being used in projects of machine learning as well as other applications of
advanced analytics. Big data is normally featured by the 3Vs: the extreme data volume, the broad
variety of data kinds as well as the velocity where the data has to be processed. Those features
were initially identified by Gartner analyst Doug Laney in a finding published in the year 2001
(Abbasi, Sarker and Chiang, 2016). More recently, numerous other Vs have been included to
descriptions of big data among them veracity, variability as well as value. In as much as big data
is not equivalent to a certain volume of data, the term is normally used in the description of
terabytes, exabytes as well as petabytes of data taken with time.
Explaining the Vs of big data
These voluminous data may come from an avalanche of sources for instance customer
databases, business transaction systems, medical records, mobile applications, real-time data
sensors, data generated from machine, internet clickstream logs alongside the gathered results of
scientific experiments (Ahmed et al., 2017). The data may be in the raw form or even pre-
processes with the use of various data mining tools otherwise data preparation software prior to
analysis.
Big data as well entail an avalanche of types of data, inclusive of structured data in the SQL
databases as well as data warehouses, unstructured data including document as well as text files
as held in Hadoop clusters or even NoSQL systems alongside semi-structured for instance web
server logs or even streaming data from the various sources. Still, big data is inclusive multiple,
simultaneous sources of data that may not be integrated. For instance, a project data analytics

may try to measure the success of a product as well as future sales by associating data sales,
online buyer review data as well as return data for the specific.
Big Data Technologies
Hadoop Ecosystem
In as much as Apache Hoop might not be as dominant as it used to be then, it is almost not
possible to discuss big data without talking about this open source framework that is used for
distributed processing of large sets of data (Al-Ali et al., 2017). It was predicted by Forester that
nearly 100% of the larger enterprise would enact Hadoop alongside the related technologies
including Spark for the purposes of big data analytics within a period of the preceding two years.
Hadoop has managed to develop over the years to incorporate a whole ecosystem of interlinked
software and numerous big data solutions are dependent on Hadoop. The market for products as
well as services based on Hadoop has been forecast by Zion Market research to grow continually
at 50 per cent CAGR all the way to 2022
Spark
Apache Spark is a component of the Hadoop ecosystem even though the use has gained a lot of
widespread that is qualifies to have its own category. It is a processing system for use in the
processing of big data inside Hadoop and has been established to be to the tune of 100 times
factor in comparison with the standard Hadoop engine, MapReduce
Going by the AtScale 2016 Big Data Maturity Survey, about 25 per cent of the intervieews
confirmed they had adopted Spark in their production systems while another 33 per cent had
projects of Spark in development. This is a clear indication that there is sizeable and growing