The data collection process is rapidly advancing. With the help of IoT devices, we are now able to keep track of our every movement, including our health, sleep, exercise, transactions, and other aspects of our online presence. On other side, companies are collecting large amounts of data to understand users' patterns and provide personalized recommendations.
To truly benefit from this data, companies must constantly analyze and process it to extract value. Otherwise, the collected data will simply be treated as digital garbage. As the volume and diversity of data increases, traditional data analysis and data science methods become inadequate. This has led to the emergence of a new field called Big Data, which is specifically designed to analyze and process this massive amount of collected data. In this article, we will provide a brief overview of the idea behind Big Data.
Big Data refers to a level of complexity in data that makes it difficult for traditional storage, processing, and analytical methods to handle. Big Data cannot be accessed using standard SQL approaches or stored in traditional relational databases. As a result, advanced distributed systems have been developed to support the processing of Big Data.
The emergence of Big Data is directly linked to the growth of companies like Google and Facebook, which collect and process large amounts of data daily. These companies and others like them generate and store petabytes of data, which includes various forms of data such as text, images, videos and social media interactions. This data is highly diverse, unstructured and dynamic, which makes it challenging to process and analyze.
Big Data can be classified into three main categories based on their structure:
<rec><name>Ravish Raj</name><sex>Male</sex><age>25</age></rec>
JSON Data Example:
[
{
color: "red",
value: "#f00"
},
{
color: "green",
value: "#0f0"
},
{
color: "blue",
value: "#00f"
}
]
Many companies collect and process large amounts of data, known as Big Data, for various purposes. Some examples of popular sources of Big Data include:
The five most known characteristics of Big Data, also known as the Five V's of Big Data, are:
Velocity refers to the rate at which data is generated, collected, and processed. For example, millions of users simultaneously watch videos on YouTube and the data about their activity, such as the videos they are watching, the time spent on the platform, and other information is stored every second, providing a real-time snapshot of usage on the platform.
Velocity is crucial for organizations to understand because it determines the resources needed for collecting, storing, and processing data. As the data is generated at a fast rate, the organization needs to have the capability to process and analyze this data in real time in order to provide meaningful insights.
Volume refers to the quantity of data being uploaded. Recently the world population crossed the 8 Billion mark and most of these people are connected to the internet. It produces tons of data regularly, requiring enormous databases to store and process them. Some estimations show that approximately 3 Quintillion bytes of data are recorded daily. 1 Quintillion is equivalent to 1 Billion Gigabytes (GBs), or 1 Million Terabytes (TBs). One can pause to digest this number.
Data collected can be present in multiple formats like text data, video data, audio data, health data, film data, and many more. Let's take an example of Facebook/Meta; it contains textual posts, video reels, short films, profile pictures, and many more. With this wider variety, challenges grow exponentially. Variety also refers to the various sources of the dataset. Data comes from devices, people, the internet, processes, and sometimes from nature.
The biggest problem with today's data is verifying its quality and authenticity. Data is collected from millions of different sources, making traceability extremely difficult. With the ease in availability of technology and ignorance about fact-checking, it becomes critical to know whether the information in the form of data is true or false.
Value refers to the techniques using which we make data useful. Value need not always be monetary; sometimes, it can help verify the critical hypothesis, for example, in the medical or defence domains. Engineers and Scientists are investing a humongous amount of time in Big Data techniques to extract value from the data.
Big Data is becoming increasingly important for businesses, requiring advanced technologies to store and process large amounts of information. To meet this demand, a variety of scalable databases and techniques have emerged, including distributed computing systems. These systems allow companies to effectively handle the vast amount of data they collect. The technologies used for Big Data can be divided into two categories: operational technologies and analytical technologies.
Operational technologies, such as MongoDB, Apache Cassandra, and CouchDB, support real-time operations on large datasets.
Analytical technologies, such as MapReduce, Hive, Apache Spark, and Massive parallel processing (MPP), provide the ability to perform complex analytical computations.
Among these solutions, Hadoop stands out as the leading framework that supports both operational and analytical technologies. As an open-source platform, Hadoop can scale from a single server to thousands of machines and provides distributed computing through simple programming. In our next blog post, we will dive deeper into the introduction of Hadoop.
Large companies such as Google, Apple, Amazon and many others are expanding their presence in India due to the country's significant population. This population provides businesses with a valuable resource: data. With more data, companies can make more personalized recommendations, resulting in increased customer retention and even more data. This benefits not only businesses but also consumers through:
Big Data engineers face several significant challenges, including:
In this article, we explored the growing field of Big Data, which is addressing the challenge of managing vast amounts of complex and diverse data. We discussed what constitutes Big Data, the types of datasets involved, the various technologies used in Big Data, and the significant challenges faced by engineers working with Big Data. Additionally, we highlighted the personal and professional benefits of Big Data. In our next blog post, we will delve further into the Hadoop framework, which is a key technical support for Big Data. We hope you found the article informative and enjoyable.