BigData Assignment Help

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Big Data Assignment Help

‘Big Data’ is a term that defines the huge volume of data that is growing exponentially. In machine Learning BigData analytics help to manage big data, sometimes data is huge number of records then big data use to analyze the data, the speed to big data analytics techniques is more than normal techniques

Need Big Data Assignment Help?

Do you want to search person who can help you to do your Big Data Assignment? Then realcode4you.com is the right place.  Realcode4you provides provided top rated online platform that students who are struggling with this area due to lack to time, lots of work in short time frame. We offer our services at  affordable prices then the other services for all students and professionals. Realcode4you team covers all requirements which is given by your professor or industries and also provided the code assistance with low price so you can understand the code flow easily.

Big Data Assignment Help

Our Machine Learning Expert Provide Big Data Assignment help & Big Data homework help. Our expert are able to do your Big Data homework assignments at bachelors , masters & the research level. Here you can get top quality code and report at any basic to advanced level. We are solve lots of projects and papers related to Big Data and Machine Learning research paper so you can get code with more experienced expert.

Characteristics Of Big Data

There are different types of characteristics of Big Data which is listed below:

  • Volume:  Data is collected from a large number of sources, online and offline. The more the volume of good quality data the better the analysis

  • Velocity: Velocity means the rate at which data is generated. It deals with determining how fast the data is generated from various sources through the real world, online and offline

  • Variety: Data is available in many formats like text, images, videos, emails, offline document records and online sources. 

Importance Of Big Data
  • Risk management.

  • Find the reason of failure in policies of businesses and eliminating the causes in future.

  • Time-to-time offers for the customers based on their purchases.

  • Detecting any fraudulent activity.

Get Help In Big Data PySpark

Apache Spark is written in Scala programming language. PySpark has been released in order to support the collaboration of Apache Spark and Python, it actually is a Python API for Spark.

Our expert provide all PySpark related help; Big Data PySpark Coding Help, Big Data PySpark Assignment Help, Big Data PySpark Homework Help, Big Data PySpark Coursework Help, etc. 

Here you can get help in:

  • PySpark Installation

  • PySpark SQL Related Assignment

  • PySpark Context

  • And More Other

Configure PySpark

!apt-get install openjdk-8-jdk-headless -qq > /dev/null
!wget -q http://www-us.apache.org/dist/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz
!tar xf spark-2.4.0-bin-hadoop2.7.tgz
!pip install -q findspark

import os
os.environ["JAVA_HOME"] = "C:\work\Java\jre1.8.0_301"
os.environ["SPARK_HOME"] = "C:\work\Spark\spark-3.1.2-bin-hadoop2.7"
os.environ["HADOOP_HOME"] = "C:\work\Spark\spark-3.1.2-bin-hadoop2.7"

import findspark

from pyspark import SparkContext
from pyspark.sql import SQLContext
sc = SparkContext.getOrCreate()
sqlContext = SQLContext(sc)

Get Help In Big Data Map-Reduce

In a MapReduce program, Map() and Reduce() are two functions. The Map function performs actions like filtering, grouping and sorting. While Reduce function aggregates and summarizes the result produced by map function. The result generated by the Map function is a key value pair (K, V) which acts as the input for Reduce function.

Basic Steps To Perform Map Reduce:

Input Splits:

It is a fixed-size pieces called input splits Input split is a chunk of the input that is consumed by a single map



In this phase data in each split is passed to a mapping function to produce output values. In our example, a job of mapping phase is to count a number of occurrences of each word from input splits (more details about input-split is given below) and prepare a list in the form of <word, frequency>



This phase consumes the output of Mapping phase. Its task is to consolidate the relevant records from Mapping phase output. In our example, the same words are clubed together along with their respective frequency.



In this phase, output values from the Shuffling phase are aggregated. This phase combines values from Shuffling phase and returns a single output value. In short, this phase summarizes the complete dataset.

Get Help In Big Data Hadoop/HDFS

The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. HDFS employs a NameNode and DataNode architecture to implement a distributed file system that provides high-performance access to data across highly scalable Hadoop clusters.

Here you can get good quality code from our expert. Our expert focus to deliver code without any Plagiarism and within your due. 

Big Data can be characterized by:
  • Volume - the amount and scale of data

  • Velocity - the speed at which data travels in and out

  • Variety - the range and complexity of data types, structures, and sources


  • Financial markets - 7 billion shares change hands every day in the U.S. markets

  • Google, Twitter, GPS data, Facebook, YouTube, etc.

MapReduce is a framework for processing large-scale data using parallel and distributed computing technologies with a large number of computers. Apache Hadoop is an open-source implementation of MapReduce.

Big Data Analytics using Spark SQL

PySpark SQL is a module in Spark which integrates relational processing with Spark's functional programming API. We can extract the data by using an SQL query language. We can use the queries same as the SQL language. If you have basic understanding related to RDBMS, SQL, and Data Analytics then you can easily work with this. 

Feature of PySpark SQL: Consistence Data Access, Incorporation with Spark, Standard Connectivity, User-Defined Functions, Hive Compatibility

Visualization of big data

Big data visualization is the process of displaying data in charts, graphs, maps, and other visual forms. It is used to help people easily understand and interpret their data at a glance, and to clearly show trends and patterns that arise from this data.

Realcode4you also covers all big data visualization help which is related to any programming languages; Python, R, Java, C# etc. If you are looking to hire big data visualization expert then we are the right choice for you. Our expert coverts all types of visualizations like: Bar Plot, Line Plot, Pie Plot, Scatter Plot , Histogram, Heat MapDashboardsAnd more others.

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Data Visualization Using Tableau.JPG

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Big Data Sample Code

#Download dataset from github

import wget

link_to_data = 'https://github.com/tulip-lab/sit742/raw/master/Jupyter/data/mtcars.csv'
DataSet = wget.download(link_to_data)

#Read Dataset

import pandas as pd
mtcars = pd.read_csv('mtcars.csv')

#Initialize SQLContext

!apt-get install openjdk-8-jdk-headless -qq > /dev/null
!wget -q http://www-us.apache.org/dist/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz
!tar xf spark-2.4.0-bin-hadoop2.7.tgz
!pip install -q findspark

import os
os.environ["JAVA_HOME"] = "C:\work\Java\jre1.8.0_301"
os.environ["SPARK_HOME"] = "C:\work\Spark\spark-3.1.2-bin-hadoop2.7"
os.environ["HADOOP_HOME"] = "C:\work\Spark\spark-3.1.2-bin-hadoop2.7"

import findspark

#Add SQLContext

from pyspark import SparkContext
from pyspark.sql import SQLContext
sc = SparkContext.getOrCreate()
sqlContext = SQLContext(sc)

#Creating Spark DataFrames

sdf = sqlContext.createDataFrame(mtcars) 

#You can also directly load this csv file into a Spark DataFrame.

sdf2 = sqlContext.read.format("com.databricks.spark.csv").load("mtcars.csv")

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