Ipl Report
Ipl Report
Ipl Report
A report submitted
In partial fulfillment of Requirements
For the degree of
BACHELOR OF TECHNOLOGY
IN
COMPUTER SCIENCE & ENGINEERING
By
PRANJAL JAIN
ROLL NO.- 1401010088
SARANSH UPADHYAY
ROLL NO.- 1401010117
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CERTIFICATE
This is to certify that this project titled “IPL DATA ANALYSIS” submitted by Pranjal
Jain & Saransh Upadhyay in a partial fulfillment of the requirement for the award of degree B.Tech
in Computer Science & Engineering to U.C.E.R. , Allahabad , Dr A.P.J. Abdul Kalam Technical
University, Lucknow is a record of candidate’s own work carried by them under my supervision.
The matter embodied in this is original and has not been submitted for the award of any other
degree.
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DECLARATION
I declare that the work presented in this project titled “IPL DATA ANALYSIS” submitted to
Computer Science Department of United College Of Engineering & Research, Allahabad for a
award of Bachelor Of Technology Degree in Computer Science & Engineering is our original work.
I have not plagiarized or submitted the same work for the award of any other degree. In case this
declaration found incorrect, I accept that my degree is unconditionally withdrawn.
Place:
3
Abstract
Analysis of structured data has seen tremendous success in the past. However, analysis of large
scale unstructured data to perform predictions remains a challenging area. The Indian Premier
League (IPL) is a Twenty20 cricket league tournamnet held in India contested during April and May
of every year where top players from all over the world take part. The IPL is the most-attended
cricket league in the world and ranks sixth among all sports leagues.
The idea is to analyze the IPL data hosted by Kaggle to come up with something interesting and
useful which I would recommend to the Royal Challengers Banglore Team Management. I have
used various graphs and plots for doing this analysis using the Fascinating ggplot2 package.
The project utilizes the IPL datasets that allows analyst to incorporate functions that are used by IPL
application to fetch and view information.
The text file output generated from the console application is then loaded from HDFS
(Hadoop Distributed File System) file into HIVE database. Hive uses a SQL-like interface to query
data stored in various databases and file systems that integrate with Hadoop. HDFS (Hadoop
Distributed File System) is a primary Hadoop application and a user can directly interact with
HDFS using various shell-like commands supported by Hadoop. This project uses SQL like queries
that are later run on Big Data using HIVE to extract the meaningful output which can be used by the
management for analysis.
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ACKNOWLEDGEMENT
I would first like to thank Mr RAM JEE DIXIT for allowing me to work on such an interesting and
fun project. He was instrumental to my success in this project. He was very supportive and
understanding provided me an extra push and confidence to succeed in this venture. He took an
extra effort to review the report and provide his invaluable feedback.
In addition, I would like to thank the Department of Computer Science at United College Of
Engineering, Allahabad for providing an opportunity to pursue my Bachelor’s degree and guiding
me all the way to become a successful student.
Last but not the least, I am to my family for their constant support and belief in me, their words of
wisdom and moral support helped me to overcome all the challenges. With their guidance, I was
able to successfully complete my project.
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LIST OF FIGURES
TABLE OF CONTENTS
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S.No Content Page No.
1 Certificate--------------------------------------- 2
2 Declaration------------------------------------- 3
3 Acknowledgement---------------------------- 4
4 Abstract---------------------------------------- 5
6 List of Figures--------------------------------- 6
7 Chapter 1-------------------------------------- 9
7.1 Introduction--------------------------------- 9
8 Chapter 2-------------------------------------- 12
8.1 Background--------------------------------- 12
9 Chapter 3-------------------------------------- 15
10 Chapter 4--------------------------------------- 24
11 Chapter 5--------------------------------------- 34
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11.2 Analysis Metrics----------------------------- 39
12 Chapter 6--------------------------------------- 42
12.1 Conclusion------------------------------------ 42
13 References-------------------------------------- 44
CHAPTER 1
INTRODUCTION
With rapid innovations and surge of internet companies like Google, Yahoo, Amazon, eBay and a
rapidly growing internet savvy population, today's advanced systems and enterprises are generating
data in a very huge volume with great velocity and in a multi-structured formats including videos,
images, sensor data, weblogs etc. from different sources. This has given birth to a new type of data
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called Big Data which is unstructured sometime semi structured and also unpredictable in nature.
This data is mostly generated in real time from social media websites which is increasing
exponentially on a daily basis.
According to Wikipedia, “Big Data is a term for data sets that are so large or complex that
traditional data processing applications are inadequate to deal with them. Analysis of data sets can
find new correlations to spot business trends, prevent diseases, combat crime and so on." With
millions of people using Twitter to tweet about their most recent brand experience or hundreds of
thousands of check-ins on Yelp, thousands of people talking about a recently released movie on
Facebook and millions of views on IPL for a recently released movie trailer, we are at a stage
wherein we are heading into a social media data explosion. Companies are already facing
challenges getting useful information from the transactional data from their customers (for e.g. data
captured by the companies when a customer opens a new account or sign up for a credit card or a
service). This type of data is structural in nature and still manageable. However, social media data is
primarily unstructured in nature. The very unstructured nature of the data makes it very hard to
analyze and very interesting at the same time.
Whereas RDBMS is designed to handle structured data and that to only certain limit, RDBMS
fails to handle this kind of unstructured and huge amount of data called Big Data. This inability of
RDBMS has given birth to new database management system called NoSQL management system.
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data for particular users or employees etc. to handle. For that we need MapReduce
function to get the aggregated result as per the query.
5. Hive: Hive is a data warehouse system for Hadoop that facilitates ad hoc queries
and analysis of large data sets stored in Hadoop.
6. HQL: Hive uses a SQL like language called Hive. HQL is a popular choice for
Hadoop analytics.
Cricket one of the most loved and favourite sports entertainment specially in India. Here I present
data analysis for IPL (Indian Premier League) matches from the beginning till year 2016. This can
be useful for all the cricket lovers to analyse and made quick decisions based out from this. Few of
which can be like; Which cricketer has scored the most for a particular season ? or Which factors
affect winning rate ? few of which can be toss decision and venue of the match played ! Various
visual along with custom one have been used with filters and drill down capability.
2. This project requires various hadoop tools like HDFS, hive etc.
3. The extracted data is first stored in HDFS file and then the data is loaded into HIVE
database. The queries are run into HIVE database so that the IPL data can be mined
intelligently and the findings can be shared with the management.
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CHAPTER 2
BACKGROUND
"IPL has over a billion viewers and every day people watch matches of hours on television or live
and generate billions of views To analyze and understand the activity occurring on such a massive
scale, a relational SQL database is not enough. Such kind of data is well suited to a massively
parallel and distributed system like Hadoop.
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The main objective of this project is to focus on how data generated from IPL can be mined and
utilized by different companies to make targeted, real time and informed decisions about their
weakness that can increase their performance. This can be done by using Hadoop concepts. The
given project will focus on how data generated from IPL can be mined and utilized. There are
multiple applications of this project. Companies can use this project to understand how effective
and penetrative their auction programs are. This can tell the companies when is the slow period or
spike in performance and attribute the same to certain marketing campaign. Applications for IPL
data can be endless. For example, Companies can analyze how much to spend on each player
during auction. This project can also help in analyzing new emerging trends and knowing about
players changing behavior with time. Also people in different cities have different preferences.
This project uses following concepts and tools throughout its lifecycle.
1. Java API
2. Hadoop
3. Hive
4. Unix
IPL Data Analysis is a Big Data project. One primary goal of the project is to make it possible for
users to extract relevant content from collections on the scale of terabytes. There are various
challenges to consider when working with this amount of data. As discussed, it is not possible to
store all the data in a single commodity disk, let alone load it into memory for any processing or
data analytics task.
As the Hadoop team, our objective is to make information retrieval scalable in the IPL Data
Analysis project. Additionally, we are responsible for designing a general schema to store the data
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in the Hadoop cluster. The goal is that teams modify a unified data representation instead of
producing disjoint results across the system.
1. Design a schema for the storage of Twitter data and web page data.
2. Decide on whether to use HDFS, HBase, or some other framework.
3. Instruct other teams about the schema and propose data formatting standards
4. Load data into the cluster.
5. Coordinate with the cluster administrator (Sunshin Lee) for this requirement.
7. Coordinate with the other teams to make sure that they take advantage of the parallel
computing capabilities of Hadoop.
8. Provide support for writing and running MapReduce jobs.
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The text file output generated from the console application is then loaded from HDFS file into
HIVE database. HDFS is a primary Hadoop application and a user can directly interact with HDFS
using various shell-like commands supported by Hadoop. Then we run queries on Big Data using
HIVE to extract the meaningful output which can be used by the management for analysis.
Loaded in HDFS
IPL data extraction
Output Results
Wikipedia defines Big Data as "a collection of data sets so large and complex that it becomes
difficult to process using the available database management tools. The challenges include how to
capture, curate, store, search, share, analyze and visualize Big Data” . In today's environment, we
have access to more types of data. These data sources include online transactions, social
networking activities, mobile device services, internet gaming etc.
Big Data is a collection of data sets that are large and complex in nature. They constitute both
structured and unstructured data that grow large so fast that they are not manageable by
traditional relational database systems or conventional statistical tools. Big Data is defined as any
kind of data source that has at least three shared characteristics:
Extremely large Volumes of data Extremely high
Velocity of data Extremely wide Variety of data
According to Big Data: Concepts, Methodologies, Tools, and Applications,
1. A typical large stock exchange captures more than 1 TB of data every day.
2. There are over 5 billion mobile phones in the world which are producing
enormous amount of data on daily basis.
4. Large social networks such as Twitter and Facebook capture more than 10 TB of
data daily.
5. There are more than 30 million networked sensors in the world which further
produces TBs of data every day.
Structured and semi-structured formats have some limitations with respect to handling large
quantities of data. Hence, in order to manage the data in the Big Data world, new emerging
approaches are required, including document, graph, columnar, and geospatial database
architectures. Collectively, these are referred to as NoSQL, or not only SQL, databases. In essence
the data architectures need to be mapped to the types of transactions. Doing so will help to ensure
the right data is available when you need it.
What is Hadoop?
As organizations are getting flooded with massive amount of raw data, the challenge here is that
traditional tools are poorly equipped to deal with the scale and complexity of such kind of data.
That's where Hadoop comes in. Hadoop is well suited to
meet many Big Data challenges, especially with high volumes of data and data
with a variety of structures.
At its core, Hadoop is a framework for storing data on large clusters of commodity hardware —
everyday computer hardware that is affordable and easily available — and running applications
against that data. A cluster is a group of interconnected computers (known as nodes) that can work
together on the same problem. Using networks of affordable compute resources to acquire business
insight is the key value proposition of Hadoop.
2. A distributed file system known as the Hadoop Distributed File System, or HDFS. In
Hadoop you can do any kind any kind of aggregation of data whether it is one-
month old data or one-year-old data. Hadoop provides a mechanism called MapReduce model to
do distributed processing of large data which internally takes care of data even if one machine
goes down.
Hadoop Ecosystem
Hadoop is a shared nothing system where each node acts independently throughout the system. A
framework where a piece of work is divided among several parallel MapReduce task. Each task
operated independently on cheap commodity servers. This enables businesses to generate values
from data that was previously considered too
expensive to be stored and processed in a traditional data warehouse or OLTP (Online
Transaction Processing) environment. In the old paradigm, companies would use a traditional
enterprise data warehouse system and would buy the biggest data warehouse they could afford
and store the data on a single machine. However, with the increasing amount of data, this
approach is no longer affordable nor practical.
Some of the components of Hadoop ecosystem are HDFS (Hadoop Distributed File System),
MapReduce, Yarn, Hive and Hbase. Hadoop has two core components. ‘Storage’ part to store the
data and ‘Processing’ part to process the data. The storage part is called ‘HDFS’ and the processing
part is called as ‘YARN’.
As stated above, the Hadoop Distributed File System (HDFS) is the storage component of
the core Hadoop Infrastructure. HDFS provides a distributed architecture for extremely large scale
storage, which can easily be extended by scaling out. It is important to mention the difference
between scale up and scale out. In its initial days, Google was facing challenges to store and
process not only all the pages on the internet but also its users’ web log data. At that time, Google
was using scale up architecture model where you can increase the system capacity by adding CPU
cores, RAM etc to the existing server. But such kind of model had was not only expensive but also
had structural limitations. So instead, Google engineers implemented Scale out architecture model
by using cluster of smaller servers which can be further scaled out if they require
more power and capacity. Google File System (GFS) was developed based on this
architectural model. HDFS is designed based on similar concept.
The core concept of HDFS is that it can be made up of dozens, hundreds, or even thousands of
individual computers, where the system's files are stored in directly attached disk drives. Each of
these individual computers is a self-contained server with its own memory, CPU, disk storage, and
installed operating system (typically Linux, though Windows is also supported). Technically
speaking, HDFS is a user-space-level file system because it lives on top of the file systems that are
installed on all individual computers that make up the Hadoop cluster.
The above figure shows that a Hadoop cluster is made up of two classes of servers: slave nodes,
where the data is stored and processed and master nodes, which
govern the management of the Hadoop cluster. On each of the master nodes and slave nodes,
HDFS runs special services and stores raw data to capture the state of the file system. In the case
of the slave nodes, the raw data consists of the blocks stored on the node, and with the master
nodes, the raw data consists of metadata that maps data blocks to the files stored in HDFS.
HDFS is a system that allows multiple commodity machines to store data from a single source.
HDFS consists of a NameNode and a DataNode. HDFS operates as master slave architecture as
opposed to peer to peer architecture. NameNode serves as the master component while the
DataNode serves as a slave component. NameNode comprises of only the Meta data information of
HDFS that is the blocks of data that are present on the Data Node
How many times the data file has been replicated? When does the
NameNode start?
How many DataNodes constitute a NameNode, capacity of the NameNode and space utilization?
The DataNode comprises of data processing, all the processing data that is stored on the DataNode
and deployed on each machine.
The actual storage of the files being processed and serving read and write request for the clients
In the earlier versions of Hadoop there was only one NameNode attached to the DataNode which
was a single point of failure. Hadoop version 2.x provides multiple NameNode where secondary
NameNode can take over in the event of a primary
NameNode failure. Secondary NameNode is responsible for performing periodic check points in
the event of a primary NameNode failure. You can start secondary NameNode by providing
checkpoints that provide high availability within HDFS.
Let’s take look at a data warehouse structure example where we have one machine and with HDFS
we can distribute the data into more than one machine. Let’s say we have 100 GB of file that takes
20 minutes to process on a machine with a given number of channel and hard drive. If you add four
machines of exactly the same configuration on a Hadoop cluster, the processing time reduces to
approximately one fourth of the original processing time or about 5 minutes.
But what happens if one of these four machines fails? HDFS creates a self-healing architecture by
replicating the same data across multiple nodes. So it can process the data in a high availability
environment. For example, if we have three DataNodes and one NameNode, the data is transferred
from the client environment into HDFS DataNode. The replication factor defines the number of
times a data block is replicated in a clustered environment. Let’s say we have a file that is split into
two data blocks across three DataNodes. If we are processing these files to a three DataNode cluster
and we set the replication factor to three. If one of the nodes fails, the data from the failed nodes is
redistributed among the remaining active nodes and the other nodes will complete the processing
function.
Processing Component: Yet Another Resource Negotiator (YARN)
YARN (Yet Another Resource Negotiator) is a resource manager that identifies on which machine a
particular task is going to be executed. The actual processing of the task or program will be done by
Node Manager. In Hadoop 2.2, YARN augments the MapReduce platform and serves as the
Hadoop operating system. Hadoop 2.2 separates the resource management function from data
processing allowing greater flexibility. This way MapReduce only performs data processing while
resource management is isolated in YARN. Being the primary resource manager in HDFS, YARN
enables enterprises to store data in a single place and interact with it in multiple ways with
consistent levels of service. In Hadoop 1.0 the NameNode used job tracker and the DataNode used
task tracker to manage resources. In Hadoop 2.x, YARN splits up into two major functionalities of
the job tracker - the resource management and job scheduling. The client reports to the resource
manager and the resource manager allocates resources to jobs using the resource container, Node
Manager and app master. The resource container splits memory, CPU, network bandwidth among
other hardware constraints into a single unit. The Node Manager receives updates from the resource
containers which communicate with the app master. The Node Manager is the framework for
containers, resource monitoring and for reporting data to the resource manager and scheduler.
Hadoop Framework
MapReduce framework. The Hadoop framework divides the data into smaller chunks and
stores divides that data into smaller chucks and stores each part of the data on a separate node
within the cluster. For example, if we have 4 terabytes of data, the HDFS divides this data into 4
parts of 1TB each. By doing this, the time taken to store the data onto the disk is significantly
reduced. The total time to store this entire data onto the disk is equal to storing 1 part of the data as
it will store all the parts of the data simultaneously on different machines.
In order to provide high availability what Hadoop does is replicate each part of the data onto other
machines that are present within the cluster. The number of copies it will replicate depends on the
replication factor. By default the replication factor is 3, in such a case there will be 3 copies of
each part of the data on three different machines. In order to reduce the bandwidth and latency
time, it will store two copies on the same rack and third copy on a different rack. For example, in
the above example, NODE 1 and NODE 2 are on rack one and NODE 3 and NODE 4 are on rack
two. Then the first two copies of part 1 will be stored on NODE 1 and third copy will be stored
either on NODE 3 or NODE 4. Similar process is followed in storing remaining parts of the data.
The HDFS takes care of the networking required by these nodes in order to communicate.
CHAPTER 4
IPL Data Analysis is a Big Data project. One primary goal of the project is to make it possible for
users to extract relevant content from collections on the scale of terabytes. There are various
challenges to consider when working with this amount of data. As discussed, it is not possible to
store all the data in a single commodity disk, let alone load it into memory for any processing or
data analytics task.
As the Hadoop team, our objective is to make information retrieval scalable in the IPL Data
Analysis project. Additionally, we are responsible for designing a general schema to store the data
in the Hadoop cluster. The goal is that teams modify a unified data representation instead of
producing disjoint results across the system.
Analysis of structured data has seen tremendous success in the past. However, analysis of large
scale unstructured data to perform predictions remains a challenging area. The Indian Premier
League (IPL) is a Twenty20 cricket league tournamnet held in India contested during April and May
of every year where top players from all over the world take part. The IPL is the most-attended
cricket league in the world and ranks sixth among all sports leagues.
The idea is to analyze the IPL data hosted by Kaggle to come up with something interesting
and useful which I would recommend to the Royal Challengers Banglore Team
Management. I have used various graphs and plots for doing this analysis using tools.
The main objective of this project is to show how companies can analyze IPL data using IPL data
to make targeted real time and informed decisions. This project will help in understanding
changing trends among people by analyzing IPL data and fetching meaningful results. For
example, when companies like Disney launch their new movie trailers on IPL, this application
can help Auction in analyzing the perfrmance of people towards a specific match. This
application can analyze home many people liked the trailers, in which country the trailer was
liked the most, whether the comments posted on IPL are generally positive, negative or neutral
etc. This way management can take executive decisions how to spend their marketing budget in
order to maximize their returns.
Since IPL data is getting created in a very huge amount and with an equally great speed, there is a
huge demand to store, process and carefully study this large amount of data to make it usable.
Hadoop is definitely the preferred framework to analyze the data of this magnitude.
While Hadoop provides the ability to collect data on HDFS (Hadoop Distributed File System),
there are many applications available in the market (like MapReduce, Pig and Hive) that can be
used to analyze the data.
Let us first take a closer look at all three applications and then analyze which application is
better suited for IPL Data Analysis project.
MapReduce
MapReduce is a set of Java classes run on YARN with the purpose of processing massive amounts
of data and reducing this data into output files. HDFS works with MapReduce to divide the data in
parallel fashion on local or parallel machines. Parallel structure requires that the data is immutable
and cannot be updated. It begins with the input files where the data is initially stored typically
residing in HDFS. These input files are then split up into input format which selects the files,
defines the input splits, breaks the file into tasks and provides a place for record reader objects. The
input format defines the list of tasks that makes up the map phase. The tasks are then assigned to
the nodes in the system based on where the input files chunks are physically resident. The input
split describes the unit of work that comprises a single map task in a MapReduce program. The
record reader loads the data and converts it into key value pairs that can be read by the Mapper.
The Mapper performs the first phase of the MapReduce program. Given a key and a value the
mappers export key and value pairs and send these values to the reducers. The process of moving
mapped outputs to the reducers is known as shuffling. Partitions are the inputs to reduce tasks, the
partitioner determines which key and value pair will be stored and reduced. The set of intermediate
keys are automatically stored before they are sent to the reduce function. A reducer instance is
created for each reduced task to create an output format. The output format governs the way objects
are written, the output format provided by Hadoop writes the files to HDFS.
Hive
Hive provides the ability to store large amounts of data in HDFS. Hive was designed to appeal to a
community comfortable with SQL. Hive uses an SQL like language known as HIVEQL. Its
philosophy is that we don’t need yet another scripting language. Hive supports maps and reduced
transform scripts in the language of the user’s choice which can be embedded with SQL. Hive is
widely used in Facebook, with analyst comfortable with SQL as well as data miners programming
in Python.
Supporting SQL syntax also means that it is possible to integrate with existing tools like. Hive
has an ODBC (Open Database Connectivity JDBC (Java Database Connectivity) driver that
allows and facilitates easy queries. It also adds support for indexes which allows support for
queries common in such environment. Hive is a framework for performing analytical queries.
Currently Hive can be used to query data stored in HBase which is a key value store like those
found in the gods of most RDBMS’s (Relational database management system) and the Hadoop
database project uses Hive query RDBMS tier.
Pig
Pig comes from the language Pig Latin. Pig Latin is a procedural programming language and fits
very naturally in the pipeline paradigm. When queries become complex with most of joins and
filters then Pig is strongly recommended. Pig Latin allows pipeline developers to decide where to
checkpoint data in the pipeline. That is storing data in between operations has the advantage of
check pointing data in the pipeline. This ensures
the whole pipeline does not has to be rerun in the event of a failure. Pig Latin allows users to store
data at any point in the pipeline without disturbing the pipeline execution.
The advantage that Pig Latin provides is that pipelines developers decide where appropriate
checkpoints are in the pipeline rather than being forced to checkpoint wherever the schematics of
SQL impose it. Pig Latin supports splits in the pipeline. Common features of data pipelines is that
they are often graphics and not linear pipelines since disk’s read and write scan time and
intermediate results usually dominate processing of large datasets reducing the number of times
data must be written to and read from disk is crucial for good performance.
Pig Latin allows developers to insert their own code almost anywhere in the data pipeline which is
useful for pipeline development. This is accomplished through a user defined functions UDFS
(User Defined Functions). UDFS allows user to specify how data is loaded, how data is stored and
how data is processed. Streaming allows users to include executables at any point in the data flow.
Pipeline also often includes user defined columns transformation functions and user defined
aggregations. Pig Latin supports writing both of these types of functions in java.
Analysis of Which Technology to Use
The following table [4] shows features and comparison of leading Hadoop Data Analysis
technologies available in the market.
Figure 3 Features and Comparisons of MapReduce, Pig and Hive
IPL sample dataset collected using Kaggle has the following properties:
All Indian Premier League Cricket matches between 2008 and 2016.
This is the ball by ball data of all the IPL cricket matches till season 9.
matches.csv contains details related to the match such as location, contesting teams, umpires,
results, etc.
deliveries.csv is the ball-by-ball data of all the IPL matches including data of the batting team,
batsman, bowler, non-striker, runs scored, etc.
After extracting the sample dataset of IPL , this dataset can be fed into various Hadoop
Technologies and meaningful results can be extracted and analyzed.
Following feature comparison analysis is performed in order to analyz which Hadoop
Technology is suitable for IPL Data Analysis project.
1. If MapReduce is to be used for IPL Data analysis project, then we need to write
complex business logic in order to successfully execute the join queries. We would
have to think from map and reduce view of what is important and what is not
important and which particular code little piece will go into map and which one will
go into reduce side. Programmatically, this effort will become quite challenging as
lot of custom code is required to successfully execute the business logic even for
simplest tasks. Also, it may be difficult to map the data into schema format and lot of
development effort may go in to deciding how map and reduce joins can function
efficiently.
3. Hive provides a familiar programming model. It operates on query data with a SQL-
based language. It is comparatively faster with better interactive response times,
even over huge datasets. As data variety and volume grows, more commodity
machines can be added without reducing the performance. Hence, Hive is scalable
and extensible. Hive is very compatible and works with traditional data integration
and data analytics tool. If we apply Hive to analyze the IPL data, then we would be
able to leverage the SQL capabilities of Hive-
QL as well as data can be managed in a particular schema. Also, by using Hive, the development
time can be significantly reduced. After looking at the pros and cons, Hive becomes the obvious
choice for this IPL Data Analysis project.
CHAPTER 5
HADOOP SETUP
Step 1 Create a virtual box on your operating system using the link below
http://www.oracle.com/technetwork/serverstorage/virtualbox/downloads/index.html
Step 2 Setup Hadoop on your virtual box using the link below
http://share.edureka.co/pydio/data/public/hadoop
Step 3 Import the file downloaded from “STEP 2” on your virtual machine
NameNode
ResourceManager
DataNode
Jps
NodeManger
This command is used to create a directory to store the compiled java classes.
$ mkdir units
The following commands are used for compiling the ProcessUnits.java program
and creating a jar for the program.
Step 4 Copy your IPL Input from local machine into HDFS using
Step 5 Now run your jar file in Hadoop environment run command
Batting:-
5. Running Between Wickets = (Total Runs - (Fours + Sixes)) / (Total Balls Played - Boundary
Balls)
Bowling:-
4. Crucial Wicket Taking Ability = Number of times Four or Five Wickets Taken / Number of
Innings Played
5. Short Performance Index = (Wickets Taken – Number of Times Four Wickets Taken – Number of
Times Five Wickets Taken) / (Innings Played - Number of Times Four Wickets or Five Wickets
Taken)
SAMPLE QUERY
Here we evaluate that which stadium is most suitable for first batting. Here are the details how can
we do that.
win_by_runs means – Team batted first and won the Match by margin of some runs.
win_by_wickets means – Team batted second and chased the target successfully.
So we will take out the columns toss_decision, won_by_runs, won_by_wickets, venue. From this
we will filter out the columns which are having won_by_runs value as 0 so that we can get the
teams which won by batting first. Here is the HQL to do that.
Has Toss-winning helped in Match-winning?
Having solved those not-so-tough questions above, we are nowhere to extract a critical insight—
which is—Has winning toss actually helped in winning the match?
CONCLUSION
The task of big data analysis is not only important but also a necessity. In fact many organizations
that have implemented Big Data are realizing significant competitive advantage compared to other
organizations with no Big Data efforts. The project is intended to analyze the IPL Data and come
up with significant insights which cannot be determined otherwise.
The output results of IPL data analysis project show key insights that can be extrapolated to other use
cases as well. One of the output results describes that for a specific match, how many likes were runs
scored. The number of wickets fallen -- or "Hattrick" -
A match had has a direct significance to the team’s ranking, according to IPL Analytics. By this
analysis we could determine whether the competitors appear more weak in which pitch.
Another output result gives us insights on if there is a pattern of affinity of interests for certain match
category. This can be done by analyzing the venue count. For e.g., if the number of wins related to
particular venue.
FUTURE WORK
The future work would include extending the analysis of IPL data using other Big Data
analysis Technologies like Pig and MapReduce and do a feature comparison analysis. It would
be interesting to see which technology fares better as compared to the other ones.
One feature that is not added in the project is to represent the output in a Graphical User Interface
(GUI). The current project displays a very simplistic output which does not warrant a GUI interface.
However, if the output is too large and complex, the output can be interfaced in a GUI format to display
the results. The data can then be presented in different format including pie-charts and graphs for better
user experience.
Another possible extension of this project could be the User’s Comment Analysis project. The
current scope of the project includes analyzing the statistics for matches including runs, wicket,
winner etc. By identifying classifying/categorizing ,opinion minding can be performed for a
specific match. This would tell us player’s attitude towards a particular situation or a given subject.
REFERENCES
[1] A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review”,
[2] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, second ed., Morgan
Kaufmann, California, 2005.
[3] P. Berkhin, Survey of clustering data mining techniques, Technical Report, Accrue Software,
Inc., 2002.
[5] M. Filippone, F. Camastra, F. Masulli, and S. Rovetta, “A survey of kernel and spectral methods
for clustering”, Pattern Recognition 41, 176–190, 2008.
[7] L. A. Zadeh, From search engines to question-answering systems the role of fuzzy logic,
University Berkeley, California.
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