Big Data Analytics Winter 2022 GTU Paper Solution | 3161607

Here, We provide Big Data Analytics GTU Paper Solution Winter 2022. Read the Full BDA GTU paper solution given below.

Big Data Analytics GTU Old Paper Winter 2022 [Marks : 70] : Click Here

(a) What is Big Data? Explain characteristics of Big Data.

Big data refers to extremely large and complex data sets that require advanced technologies and methods to capture, store, manage, and analyze. Big data is characterized by the 3 Vs: volume, velocity, and variety.

  1. Volume: Big data refers to massive amounts of data that can range from terabytes to petabytes or even exabytes in size. This data can be structured, semi-structured, or unstructured and can come from various sources such as social media, sensors, or business transactions.
  2. Velocity: Big data is generated and collected at an unprecedented speed, requiring real-time or near real-time analysis. For example, social media data is generated continuously, and stock market data changes rapidly.
  3. Variety: Big data comes in different forms, such as structured, semi-structured, and unstructured. Structured data is highly organized and can be easily analyzed using traditional methods, while unstructured data is not organized and can be more difficult to analyze. Semi-structured data falls in between these two categories.
  4. Veracity: Big data can be noisy and contain inaccuracies, making it challenging to extract insights.
  5. Value: Big data can provide valuable insights that can inform business decisions, optimize processes, and improve customer experience.
  6. Variability: Big data can be inconsistent, and the meaning of data may change over time.

(b) Define features of Big Data.

(c) Explain Map-Reduce framework in detail. Draw the architectural
diagram for Physical Organization of Computer Nodes.

(a) Explain unstructured, semi-structured and structured data with one
example of each.

Data can be classified into three main categories based on their level of organization and structure: unstructured, semi-structured, and structured data.

Unstructured data refers to data that does not have a defined structure or format. This type of data is usually not organized and can be difficult to analyze using traditional methods. Examples of unstructured data include social media posts, videos, images, audio recordings, and text documents such as emails.

For example, unstructured data can be an unedited video of a customer review about a product or service. It can be difficult to extract valuable insights from this data since it may contain irrelevant or redundant information, and it requires advanced natural language processing techniques to identify key themes and topics.

Semi-structured data refers to data that has some form of structure or format but does not fit into the rigid structure of traditional databases. Semi-structured data can contain tags, metadata, or other markers that help to identify the structure and relationships between different elements in the data. Examples of semi-structured data include XML and JSON files.

For example, semi-structured data can be a customer review in JSON format that includes structured information such as product rating, date, and reviewer name. The data may also include unstructured information such as the review text. The structured information can be easily analyzed using traditional methods, while the unstructured information requires advanced text analytics techniques.

Structured data refers to data that has a defined structure and format and can be easily analyzed using traditional database methods. Structured data is highly organized and can be easily queried and analyzed using SQL or other similar tools. Examples of structured data include customer data, sales data, and financial data.

For example, structured data can be a database table that contains customer information such as name, address, phone number, and email address. This data can be easily queried and analyzed using SQL to extract valuable insights such as customer demographics and purchase behavior.

(b) Write the use and syntax of following HDFS commands:
i. put

ii. chmod
iii. get

(c) Write Map Reduce code for counting occurrences of words in the input
text file.

(c) What is apache hadoop? Explain hadoop Eco-system.

(a) Explain “Shuffle & Sort” phase and “Reducer Phase” in MapReduce.

The Shuffle and Sort phase and the Reducer phase are two important steps in the MapReduce data processing model.

The Shuffle and Sort phase occurs after the Map phase has completed. In this phase, the MapReduce framework takes the output of the Map tasks and groups them by their keys. The data is then sorted by key, so that all the values with the same key are grouped together. This process is called shuffling and sorting, and it ensures that all the values for a given key are processed together in the Reducer phase. The Shuffle and Sort phase is responsible for transferring data from the Map tasks to the Reducer tasks.

The Reducer phase is the final phase in the MapReduce processing model. In this phase, the Reducer tasks receive the output from the Map tasks, which has been shuffled and sorted by key. The Reducer tasks then process the data and produce the final output. The Reducer tasks process one group of values at a time, with each group containing all the values for a particular key. The Reducer tasks can perform various operations on the data, such as counting, summing, averaging, or any other operation that is required for the processing task. The output of the Reducer tasks is then stored in the Hadoop Distributed File System (HDFS) or any other output location specified by the MapReduce job.

(b) Mention few applications where big data analytics are useful. Describe
in brief.

(c) Define HDFS. Describe namenode, datanode and block. Explain HDFS
operations in detail.


(a) Discuss Machine Learning with MLlib in SPARK.

Machine learning is the process of training computer algorithms to make predictions or take actions based on data. Apache Spark provides a powerful and scalable platform for running machine learning algorithms through its MLlib library.

MLlib is a distributed machine learning library for Spark that provides various algorithms and tools for machine learning tasks such as classification, regression, clustering, and collaborative filtering. MLlib is designed to work seamlessly with Spark’s distributed data processing capabilities, allowing it to efficiently process and analyze large datasets.

Some of the key features of MLlib include:

  1. Scalability: MLlib is designed to work with large datasets, and can distribute computations across multiple machines in a Spark cluster to achieve high scalability.
  2. Performance: MLlib is built on top of Spark’s distributed computing framework, which allows it to process data in-memory and achieve high performance.
  3. Ease of use: MLlib provides a simple and easy-to-use API that allows users to quickly build and train machine learning models without requiring deep knowledge of distributed systems or parallel programming.

Some of the popular machine learning algorithms supported by MLlib include:

  1. Linear regression
  2. Logistic regression
  3. Decision trees
  4. Random forests
  5. Gradient-boosted trees
  6. Support vector machines
  7. K-means clustering
  8. Principal component analysis
  9. Collaborative filtering

To use MLlib in Spark, you need to first import the relevant libraries and initialize a SparkSession. Once you have loaded your data into a Spark DataFrame, you can use MLlib’s API to train and evaluate machine learning models. Here is an example of how to train a logistic regression model in Spark using MLlib:

from import LogisticRegression

# Load data into a DataFrame
data ="libsvm").load("sample_libsvm_data.txt")

# Split the data into training and test sets
train, test = data.randomSplit([0.7, 0.3])

# Initialize a logistic regression model
lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)

# Train the model on the training data
model =

# Evaluate the model on the test data
result = model.transform(test)

(b) Explain “Map Phase” and “Combiner Phase” in MapReduce.

(c) Write short note on Hive components with a neat diagram.

(a) What is the need of Data Stream? Explain.

Data stream refers to a continuous flow of data that is generated at a high velocity, in real-time or near real-time. The data in a data stream is unbounded, meaning that it does not have a definite beginning or end, and it keeps arriving continuously.

The need for data stream arises from the fact that in many domains, such as finance, healthcare, transportation, and social media, data is generated at an unprecedented rate and speed. This data needs to be processed, analyzed, and acted upon in real-time or near real-time to extract valuable insights and make informed decisions. In such scenarios, traditional batch processing techniques, which process data in batches after a certain interval, are not effective.

Data stream processing systems are designed to handle this kind of continuous and fast data by processing it in real-time or near real-time as it arrives. These systems use specialized algorithms and techniques that are designed to handle the unbounded and high-velocity nature of data streams.

The need for data stream processing has grown rapidly in recent years due to the increasing amount of data generated by various sources such as IoT devices, social media platforms, sensors, and other data sources. It is also becoming increasingly important for businesses to make quick decisions based on real-time data insights. Hence, data stream processing has become an essential tool for various industries such as finance, healthcare, telecommunications, and retail, among others.

(b) Which types of databases used in NoSQL?

(c) Explain the concept of Estimating Moments


(a) Define NO SQL Database.

A NoSQL (not only SQL) database is a non-relational database that provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. NoSQL databases are designed to handle large volumes of unstructured, semi-structured, and structured data, and they can scale horizontally across many commodity servers to provide high availability and fault tolerance.

Unlike traditional relational databases, NoSQL databases do not use fixed schemas or table structures. They allow data to be stored in a flexible and dynamic way, using a variety of data models such as document, key-value, column-family, and graph models. This makes them well-suited for handling data that is rapidly changing or has complex relationships, such as social media feeds, sensor data, and user-generated content.

NoSQL databases are often used in big data and real-time applications where scalability, performance, and flexibility are important. They provide a flexible and scalable architecture that can handle large volumes of data, high traffic, and real-time queries. Some popular examples of NoSQL databases include MongoDB, Cassandra, Redis, Couchbase, and Apache HBase.

(b) What is Decaying Window Algorithm?

(c) Explain with a neat diagram about Stream data model and its

(a) Difference Between Hbase and Hive.

HBase and Hive are both data storage and processing technologies in the Hadoop ecosystem, but they have some key differences.

  1. Data Model: HBase is a NoSQL database that uses a column-family data model, where data is organized into columns and rows. It is optimized for real-time, random read/write access to large datasets. In contrast, Hive is a data warehousing tool that uses a relational data model, where data is organized into tables with rows and columns. It is optimized for querying and analyzing large datasets using SQL-like queries.
  2. Data Storage: HBase stores data in HDFS (Hadoop Distributed File System), while Hive can store data in HDFS, HBase, or other data sources such as Amazon S3.
  3. Data Processing: HBase is designed for real-time, low-latency data access and processing, while Hive is optimized for batch processing of large volumes of data.
  4. Data Access: HBase provides low-level APIs for accessing and manipulating data, while Hive provides a SQL-like interface for querying and analyzing data.
  5. Use Cases: HBase is commonly used for real-time data processing and analytics, such as fraud detection, log processing, and social media analytics. Hive is commonly used for batch processing and analytics, such as ETL (extract, transform, load) operations, data warehousing, and business intelligence reporting.

(b) Write a short note on Zookeeper.

(c) What does Real-Time Analytics Platform (RTAP) mean? Explain the
various applications of RTAP.


(a) Define features of Apache Spark.

Apache Spark is an open-source distributed computing system designed to process large-scale data in a distributed environment. Some of its key features are:

  1. Speed: Spark is designed to run programs up to 100x faster than Hadoop MapReduce in memory and up to 10x faster on disk.
  2. Ease of Use: Spark provides a simple and easy-to-use API for developers to build distributed applications in various programming languages such as Python, Java, Scala, and R.
  3. Flexible and Scalable: Spark is highly scalable and can handle data processing tasks ranging from gigabytes to petabytes of data, making it suitable for large-scale distributed systems.
  4. Fault Tolerant: Spark provides fault tolerance by tracking the lineage of data and automatically rebuilding lost data in the event of node failures.
  5. Real-time Stream Processing: Spark Streaming enables real-time stream processing and can process data from various sources such as Kafka, Flume, and Twitter, among others.
  6. Advanced Analytics: Spark provides a wide range of advanced analytics capabilities such as machine learning, graph processing, and SQL queries.
  7. Integration: Spark can be easily integrated with other big data technologies such as Hadoop, Hive, and HBase.

(b) Write a short note on Pig.

(c) Discuss about How E-Commerce is Using Big Data to Improve Business
in detail.

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