The term “Big Data” refers to the evolution and application of technologies that provide the right user with the right information at the right time from a mass of data that has been growing exponentially in our society for some time. It may also refer to the heterogeneous mass of digital data produced by businesses and individuals, the characteristics of which (large volume, processing speed, and variety of forms) necessitate specific and increasingly sophisticated computer storage and analysis.
Large amounts of data, colloquially known as Big Data, present a new opportunity for organizations and businesses to derive new value and gain a competitive advantage from their most valuable asset. For a business to be more successful and competitive, Big Data must be at its core, integrating structured and unstructured data with real-time feeds and queries, as well as opening new paths and innovation. Today, we live in an information society with advanced technology in an era known as the fourth industrial revolution. In order to extract better knowledge, we need a larger amount of data to be well equipped. This information is critical in the economic, cultural, and political stages.
The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
The three Vs of big data
|The amount of data matters. With big data, you’ll have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as Twitter data feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.
|Velocity is the fast rate at which data is received and (perhaps) acted on. Normally, the highest velocity of data streams directly into memory versus being written to disk. Some internet-enabled smart products operate in real time or near real time and will require real-time evaluation and action.
|Variety refers to the many types of data that are available. Traditional data types were structured and fit neatly in a relational database. With the rise of big data, data comes in new unstructured data types. Unstructured and semi structured data types, such as text, audio, and video, require additional preprocessing to derive meaning and support metadata.
Evolution of Big Data
Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and ‘70s when the world of data was just getting started with the first data centers and the development of the relational database. Big data has a long history that predates the current buzz around Big Data. The first attempt to quantify data growth rate in terms of volume of data was made seventy years ago. This is known colloquially as a “information explosion.” We will discuss some significant turning points in the evolution of “big data.
Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open-source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.
The development of open-source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data, but it’s not just humans who are doing it.
With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.
While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.
What are the Types of Big Data?
As the Internet age surges on, we create an unfathomable amount of data every second. So much so that we’ve denoted it simply as big data. Naturally, businesses and analysts want to crack open all the different types of big data for the juicy information inside. But it’s not so simple. The different types leverage varying big data tools and have different complications that accompany working with each individual data point plucked out of the vast ether.
Big data is classified in three ways:
- Structured Data
- Unstructured Data
- Semi-Structured Data
Application of Big Data
As the amount of data collected by businesses across Africa grows, tools that can process this valuable asset and turn it into actionable information are becoming increasingly important for companies.
Big data solutions, including artificial intelligence, predictive analytics and machine learning, are able to sort through huge data sets and return commercially useful insights, which conventional technologies are unable to perform. According to a report from consulting firm Frost & Sullivan, the Middle East and Africa’s big data analytics market is forecast to grow by 28% every year until 2025, reaching revenue of $68bn.
Countless bytes of data are generated by humans every day through everything from online shopping to phone apps, watching on-demand TV to buying insurance. While much of this data is left unstructured or analyzed, harvesting just a small section of relevant data can prove extremely valuable.
Big Data is regarded as the most valuable and powerful fuel capable of powering the massive IT industries of the twenty-first century. Big Data is the most widely used technology, with applications in almost every business sector. In this section, students will learn how big data is used to address real world problems.
Tools for Big Data Analysis
For Big Data Mining and Analysis, a variety of commercial and open-source software is available.
Hadoop for Big Data
The Apache Software Foundation created Hadoop, a software framework. The Hadoop framework provides solutions to all Big Data issues. It is intended to store and process massive amounts of data (known as Big Data). Hadoop stores and processes data in clusters of low-cost machines. Hadoop clusters are made up of nodes that are linked together by a network. Hadoop clusters can store large amounts of data. Hadoop clusters are made up of nodes that are linked together by a network. Data can be stored in Hadoop clusters in sizes ranging from terabytes to petabytes. It is capable of storing and processing structured, semi-structured, and unstructured data. It is an open-source framework that is very affordable.
Hadoop clusters are capable of processing petabytes of data in minutes. Hadoop is made up of three main components. They are as follows:
Hadoop HDFS: The storage layer in Apache Hadoop is the Hadoop Distributed File System (HDFS). Hadoop HDFS is a distributed file system that stores data across multiple nodes. It partitions data into blocks and distributes them across multiple machines. The two Hadoop HDFS daemons that run on a Hadoop cluster are DataNode and NameNode.
Hadoop MapReduce: Hadoop MapReduce is the framework’s heart. It is Hadoop’s processing layer. It provides a software framework for developing applications. applications that handle massive amounts of data In a Hadoop cluster, Hadoop MapReduce distributes data processing.
Hadoop YARN: Yet Another Resource Negotiator, abbreviated as YARN, is Hadoop’s resource management layer. It is in charge of allocating resources among the Hadoop cluster’s applications.
- Authentication improvements when using HTTP proxy server
- Specification for Hadoop Compatible Filesystem effort
- Support for POSIX-style filesystem extended attributes
- It has big data technologies and tools that offers robust ecosystem that is well suited to meet the analytical needs of developer
- It brings Flexibility In Data Processing
- It allows for faster data Processing
The big data trend has dramatically impacted every industry, so it is little surprise that big data in GIS has significant implications for how we acquire and leverage spatial information. As we consider the way organizations are using geographic information science and technology, one of the clearest themes is that usage is expanding rapidly; whereas, historically, the largest adopters of geospatial data have been government agencies, it is now easy to find widespread GIS adoption in every business sector. The convergence of GIS with big data means that the potential applications of the two will become limitless.
The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.
Earth observation systems and model simulations are generating massive volumes of disparate, dynamic, and geographically distributed geospatial data with increasingly finer spatiotemporal resolutions. Meanwhile, the ubiquity of smart devices, location-based sensors, and social media platforms provide extensive geo-information about daily life activities. Efficiently analyzing those geospatial big data streams enables us to investigate complex patterns and develop new decision-support systems, thus providing unprecedented values for sciences, engineering, and business. However, handling the five “Vs” (volume, variety, velocity, veracity, and value) of geospatial big data is a challenging task as they often need to be processed, analyzed, and visualized in the context of dynamic space and time.