Big Data Management and Analytics

The following sections are included:

• Dedication
• Foreword
• Preface
• About the Authors
• Acknowledgments
• Contents
• List of Figures
• List of Tables

Digitalization in the modern world leads to the generation of a large amount of data in almost every domain. Due to the sheer volume of data that needs to be managed, big data management and analysis have become essential components of any system. The large amount of data that is being produced as a result of the proliferation of connected devices is growing at an exponential rate, which makes it extremely difficult to store and process all of this data. The challenges associated with storing, transmitting, and understanding this data are so enormous that they have given rise to their very own field of study as well as an industry: Big Data. The management and analysis of large amounts of data have become increasingly popular in virtually all sectors, including the healthcare and financial sectors. Now, the big question is: What actually triggered the big data era? First is the continuously growing torrent of data, and the second is the on-demand cloud computing services. The data are generated continuously at a very fast pace. According to a report by McKinsey Global Institute (MGI), worldwide data volumes are doubling every two years due to social websites, sensors, smartphones, and other sources [1]. The exponential increase of data is fueled by the rising volume and detail of data acquired by businesses, the advent of multimedia, social media, and the Internet of Things. This exploding data can add value to businesses, and there have been instances where with proper analytics, big data reveal useful insights and become a critical factor in almost every industry. MGI studied the impact of big data in several domains like healthcare, retail, manufacturing and the public sector and noted the instances where big data is contributing value to each of these domains [1]. The biggest source of data is mobile devices, which every day and all the time generates data. According to a recent report by Statista, by the end of 2021, the number of mobile devices in use globally will be over 15 billion, and by 2025, the number of mobile devices is predicted to reach 18.22 billion, a 4.2 billion increase above 2020 levels [2], and so will be the data. This serious growth in data demonstrates the immediate need for information on how to handle and manage such data. Apart from this continuously growing data, there is another factor which contributed to the launch of the big data era, and that is cloud computing. Cloud computing is often called on-demand computing. It allows us to perform computing anytime and anywhere, which, when combined with this continuously growing torrent of data, opens new gates for dynamic and scalable data analysis. This novel and dynamic data analysis have the ability to reveal insights about the world which were difficult to imagine before. Hence, cloud computing and the growing torrent of data together launched the new era of Big Data.

In big data applications, data come in high volume with high velocity and lots of variations. Before dealing with such enormous data, there is a need to know what these data look like. Data modeling and management [1] concepts come into the picture to answer such questions. The purpose of data modeling is to meticulously investigate the nature of data to determine how much storage they will need and what type of processing can be done with them. Data management aims to determine what kind of infrastructure support would be required for the data. Here, the focus will be on different storage architectures for big data. Once all the operational requirements are understood for the data coming from a big data application, one could pick the right system to perform all the required tasks.

This chapter focuses on the issues of ingesting and processing big data and also throws light on how the processing of big data is influenced by the data’s variety, volume, and velocity.

Big data’s role in the success of many significant technological businesses is no secret. However, as more companies use it to store, analyze, and extract value from their massive volume of data, it becomes increasingly difficult for them to use the gathered information most effectively [1]. This is where machine learning proves to be helpful. Data always over-power the machine learning algorithm. No matter how good the learning algorithm is, it will be useless without a significant amount of relevant data. The efficiency and accuracy of these machine learning algorithms increase as we feed them with more relevant data. Data act as a blessing for machine learning algorithms [2]. Machine learning systems perform better as they get more and more data. Therefore, adopting machine learning for big data analytics is a natural step for businesses to optimize the adoption of big data. This chapter focuses on applications of machine learning to big data.

Graphs for data analysis have a wide range of applications in both academia and industry. They can be used to model parts of human knowledge and are called semantic networks. Graphs dominate all the technology giants, such as Facebook, LinkedIn, Twitter, and many more companies, which represent, model, and process their data as graphs. If we look at the world wide web, that can also be viewed as a giant graph. This chapter demonstrates how real-world data science problems can be modeled as graphs.

Apache Spark is an open-source big data processing framework that was designed to address the challenges of working with large datasets. It is one of the most popular big data processing tools, particularly for machine learning and data analytics. One of the key benefits of Spark is its ability to handle large-scale data processing tasks with speed and efficiency. Spark achieves this by distributing data processing tasks across multiple nodes in a cluster, allowing computations to be performed in parallel. Spark’s core abstraction is the resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be processed in parallel. Another advantage of Spark is its support for a wide range of data sources, including Hadoop Distributed File System (HDFS), Apache Cassandra, and Apache Kafka. This means that Spark can be used to process data from a variety of sources, making it a flexible tool for big data processing. In addition to its core processing capabilities, Spark includes several libraries and application programming interfaces (APIs) for machine learning, graph processing, and streaming data processing. These libraries, such as MLlib, GraphX, and Spark Streaming, make it easier for developers to build and deploy complex big data applications.

Big data are large, voluminous datasets that require specialized tools and techniques to manage, process, and analyze. Cloud storage, on the other hand, refers to the practice of storing data on remote servers that can be accessed over the Internet.

Cloud storage is an ideal solution for storing and managing big data because it provides virtually unlimited storage capacity and can be accessed from anywhere with an internet connection [1]. Additionally, cloud storage providers typically offer a range of tools and services that are designed to support big data workloads, such as distributed computing frameworks, data processing engines, and analytics tools [2]…

Companies acquire large amounts of unstructured or semi-structured data for use in sophisticated analytics tools, such as predictive modeling and machine learning. These data are referred to as big data. Traditional software cannot handle the complexity of big data collections. Douglas Laney provided the definition that has gained the most traction and acceptance. Volume, velocity, and variety — collectively referred to as the “3 V’s” — were the three dimensions in which Laney saw that (big) data was expanding [1]. The term big data itself alludes to the enormous size of such datasets. Big data is defined not just by its volume but also by its speed and variety. Data collected by a business or system may include transaction records, videos, audio recordings, written documents, and even log files, all of which fall under the category of variety. Velocity is the rate at which information is gathered and made available for analysis. Big data is now typically defined by these three V’s. Almost all research fields, whether they are related to business or academia, are producing and studying big data for a variety of reasons. To understand the meaning of this massive volume of data, it would be necessary to employ revolutionary fusion and artificial intelligence (AI) techniques. Using machine learning (ML) techniques, such as neural networks and other forms of AI, for the tasks of analysis and decision-making would be a significant step forward. Yet, without the right hardware and software, big data might be hard to interpret. In order to effectively analyze this “endless sea” of data and generate actionable insights, we need to develop smarter web services and better methods for handling the data. With the correct data storage and analysis tools, big data can be utilized to increase the effectiveness of vital parts of society’s infrastructure, such as healthcare, security, and communication.

Big data refers to the vast amount of information generated by individuals, organizations, and machines in our modern digital world. The financial industry is one of the areas that generates a large amount of data, including financial transactions, stock prices, economic indicators, and other financial market data [1]. The integration of big data into financial research has the potential to provide insights and predictions that can inform investment decisions, market analysis, and risk management [2]. The financial industry has been quick to embrace big data, and the use of big data in financial research has increased significantly in recent years. However, the use of big data in financial research is not without its challenges. The sheer volume of data can make it difficult to extract meaningful insights, and the accuracy and reliability of the data must be carefully evaluated. This chapter explores the application of big data in financial research, including the sources of financial data, the types of analysis used in financial research, and the challenges associated with using big data in financial research.

The field of big data analytics relies on a wide range of enabling technologies and tools to store, process, and analyze large volumes of data. Some of the most important tools and technologies for big data analytics are as follows [1]:

• Data Warehousing: A data warehouse is a centralized repository that stores large volumes of structured and unstructured data. Data warehouses are designed to support complex analytical queries and provide fast, efficient access to data. Some popular data warehousing technologies include Amazon Redshift, Google BigQuery, and Microsoft Azure SQL Data Warehouse.
• Hadoop: Apache Hadoop is an open-source software framework that provides a distributed storage and processing system for large datasets. Hadoop is designed to handle both structured and unstructured data and can scale to accommodate massive amounts of data. Hadoop includes several key components, including the Hadoop Distributed File System (HDFS) and MapReduce for distributed processing.
• NoSQL Databases: NoSQL databases are designed to handle large volumes of unstructured or semi-structured data. These databases are typically faster and more scalable than traditional relational databases and can handle a wide range of data types. Popular NoSQL databases include Apache Cassandra, MongoDB, and Couchbase.
• Data Integration: Data integration tools are used to combine data from multiple sources and prepare it for analysis. These tools can handle complex data transformations and ensure data quality and consistency. Popular data integration tools include Apache NiFi, Talend, and Informatica.
• Data Visualization: Data visualization tools are used to create interactive, visual representations of data. These tools can help analysts and business users better understand and communicate insights from large datasets. Popular data visualization tools include Tableau, Power BI, and QlikView.
• Machine Learning: Machine learning algorithms are used to analyze large datasets and identify patterns and insights. These algorithms can learn from data and improve their accuracy over time. Popular machine learning libraries include TensorFlow, Scikit-Learn, and Apache Spark MLlib.
• Cloud Computing: Cloud computing platforms provide on-demand access to computing resources, including storage, processing, and analytics tools. Cloud platforms are highly scalable and can be used to process and analyze massive amounts of data. Popular cloud platforms for big data analytics include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform…

The following section is included:

• Index