Understanding the 4 Fundamental Components of Big Data Ecosystem


Concepts such as components of databases and other attributes related to Data Science have taken the world by storm. The fast development of digital technologies, IoT goods and connectivity platforms, social networking apps, video, audio, and geolocation services has created the potential for massive amounts of data to be collected/accumulated. Previously, organizations dealt with static, centrally stored data collected from numerous sources, but with the advent of the web and cloud services, cloud computing is fast supplanting the traditional in-house system as a dependable, scalable, and cost-effective IT solution. The large amounts of structured and unstructured data stored in a dispersed way and the diverse range of data sources provide challenges in data/knowledge representation and integration, data querying, business analysis, and knowledge discovery. 

Today, the Internet has over 4 billion users. Here’s how the picture looks in data terms: 

  • 9,176 tweets are sent every second
  • Every second, 1,023 Instagram photographs are uploaded
  • 5,036 Skype calls are made every second
  • 86,497 Google searches are performed every second
  • 86,302 YouTube videos are seen every second
  • 2,957,983 emails are sent every second

Ninety-four zettabytes will be created and used worldwide in 2022. That is practically unfathomable data, which will only increase as the number of linked devices to the Internet of Things rises.

This vast volume of data generated at a breakneck pace and in a variety of formats is what we now refer to as Big data. However, storing this data on the standard systems we have been using for almost 40 years is impossible. To handle this large amount of data, we want a far more complicated architecture comprised of numerous components of the database performing various tasks rather than just one. 

Real-life Examples of Big Data In Action 

Traditional data processing technologies have presented numerous obstacles in analyzing and researching such massive amounts of data. To address these issues, Big Data technologies such as Hadoop were established. These Big Data tools aided in the realization of Big Data applications. 

Education Sector 

The education business is inundated with data about students, instructors, courses, results, and so on. We’ve learned that a thorough examination and analysis of this data can yield insights that can be used to improve educational institutions’ operational efficacy and efficiency.

The following are some of the disciplines in education that Big Data-driven changes have revolutionized: 

  • Learning Programs That are Customized and Dynamic
    By using the data acquired based on each student’s learning history, customized programs and schemes for specific pupils can be produced. This enhances overall student performance.

  • Reframing Course Content
    Reframing course material based on data acquired depending on what a student learns and to what extent by real-time monitoring of course components of database is useful to students. 
  • System of Grading
    As a result of proper data analysis, new developments in grading methods have been created. 
  • Prediction of a Career
    Appropriate examination and research of each student’s records will assist in understanding each student’s progress, skills, limitations, interests, and other characteristics. It would also aid in evaluating which professional path would be best for the student in the future.

By contributing to e-learning solutions, Big Data applications have helped to solve one of the most serious flaws in the educational system: the one-size-fits-all academic setup. 

The University of Alabama has over 38,000 students and a massive amount of data, and some of them seemed useless in the past when there were no genuine ways to examine that much data. Administrators may now employ analytics and data visualizations to uncover patterns in student data, transforming the university’s administration, recruitment, and retention efforts. 

Big Data in Healthcare Industry 

Healthcare is yet another business that will generate massive amounts of data. The following are some examples of how Big Data has aided healthcare:

  • Because there are fewer chances of performing unnecessary diagnostics, Big Data lowers treatment expenses. 
  • It aids in predicting epidemic outbreaks and determining what preventive actions should be implemented to mitigate their impact. 
  • It aids in preventing preventable diseases by recognizing them in their early stages, and it keeps them from worsening, making therapy easier and more effective. 
  • Patients can be given evidence-based treatment that has been identified and prescribed after reviewing previous medical data.

In the healthcare industry, wearable gadgets and sensors have been launched that can transmit real-time data to a patient’s electronic health record. Apple is one such technology. Apple has created three apps: Apple HealthKit, CareKit, and ResearchKit. The primary purpose is to enable iPhone users to save and retrieve real-time health records on their devices. 

Here are some more instances of how businesses use Big Data:

  • Big data assists oil and gas businesses in identifying potential drilling locations and monitoring pipeline operations; similarly, utilities use it to track power networks. 
  • Financial services firms use big data platforms for risk management and real-time market data analysis. 
  • Big data is used by manufacturers and transportation businesses to manage supply chains and optimize delivery routes. 
  • Other government applications include disaster response, crime prevention, and smart city initiatives. 

Understanding The Ecosystem of Big Data 

The understanding of a vast functional component with numerous enabling technologies is referred to as a Big Data ecosystem. The Big Data ecosystem’s capabilities include computing and storing Big Data and the benefits of its systematic platform and Big Data analytics potential. As a result, the maturity of Big Data ecosystem application is classified into three stages based on provided solutions from evaluated literature and Big Data capabilities: 

Stage 1: presenting a Big Data framework and platform. 

Stage 2: obtaining cloud computing resources for Big Data processing and storage. 

Stage 3: Big data analysis using multiple methods for applications 

In conjunction with technologies that facilitate Big Data analytics, big data processing and storage systems have become frequent components of business data management architectures.  

The three V’s of Big Data are frequently used to describe it:

  • the massive amount of data in various environments 
  • a vast range of data kinds that are typically kept in Big Data systems 
  • the rate at which much data is generated, collected, and processed 


Although Big Data does not have a defined amount, Big Data deployments frequently contain terabytes, petabytes, and even exabytes of data created and collected over time. 

Companies employ Big Data in their systems to enhance operations, provide better customer service, generate targeted marketing campaigns, and take other activities that can boost revenue and profitability. Businesses who use it effectively can make faster and more informed business decisions, giving them a possible competitive advantage over those that don’t. 

 Although Big Data does not refer to a specific number of data, Big Data deployments frequently entail terabytes, petabytes, and even exabytes of data created and collected over time. 

 Companies use Big Data in their systems to improve operations, provide better customer service, generate targeted marketing campaigns, and take other activities that, in turn, can raise revenue and profits. Businesses that use it effectively have a potential competitive advantage over those that do not because they can make faster and more informed business decisions. 

 Many Big Data settings employ a distributed design that integrates various systems; for example, a central data lake may be coupled with additional platforms such as relational databases or a data warehouse. In Big Data systems, data can be left in its raw form and subsequently filtered and structured as needed for specific analytical needs. In other circumstances, it is preprocessed using data mining methods and data preparation software to prepare it for ordinary applications. 

Components of Database of the Big Data Ecosystem 

In order to comprehend what are the main components of Big Data, one must understand the basic layers of Big Data components of database stack together to form a stack. It is not as simple as converting data into insights. Big data analytics tools establish a process that raw data must follow in order to achieve information-driven action in a business. 

Data must first be ingested from sources, then translated and stored before being processed and presented in a comprehensible fashion. It’s a time-consuming, laborious process that can take months or even years to complete. However, the benefits might be game changers: a solid Big Data pipeline can be a tremendous differentiation for a company.

In this post, we’ll explain each Big Data component along with the Big Data ecosystem. You’ll also learn about the Big Data infrastructure and details about a few useful tools that will aid you in your further pursuit of the data study. 

The process of preparing data for analysis is known as extract, transform, and load (ETL). While the ETL workflow itself is becoming obsolete, it still serves as a broad term for the data preparation layers of a Big Data ecosystem. Data wrangling and extraction, loading, and transformation are becoming increasingly popular, although they all refer to the pre-analysis prep process. Working with large data necessitates substantially more preparation than working with smaller forms of analytics.

With various data forms and formats, it is critical to approach data analysis with a comprehensive plan that addresses all incoming data. Sometimes you’re dealing with entirely unstructured audio and video, and other times you’re dealing with a lot of highly structured, organized material, but all with different schemas that need to be realigned. 

The Big Data ecosystem generally consists of the following components: 


The ingestion layer is the initial step in bringing in raw data. It is derived from internal sources, relational databases, nonrelational databases, and outside sources, among others. It could also come from social media, emails, phone calls, or other sources. Data ingestion can be classified into two types:

Batch-Large sets of data are acquired and supplied in batches. Data collection might be conditionally triggered, scheduled, or ad hoc. 

Streaming-It refers to the continuous flow of data. This is required for real-time data analysis. It finds and retrieves data as it is generated. Because it is always watching for changes in data pools, it necessitates more resources. 


The loading procedure is the final step in the ETL process. This is where the transformed data is kept and later processed in a data lake or warehouse. It is the physical manifestation of Big Data: a massive accumulation of useable, homogeneous data as opposed to a massive collection of random, incoherent data.

Many believe the data lake/warehouse is the most important component of a Big Data ecosystem. They should only contain comprehensive, relevant data to make insights as valuable as possible. It must be efficient with as little redundancy as feasible to enable faster processing. For the same reason, it must be available with a high output bandwidth. 


It is the Big Data component where all the dirty work takes place. Data is transmitted via numerous tools in the analysis layer before being transformed into meaningful insights.

Big data analytics can be classified into four types: diagnostic, descriptive, predictive, and prescriptive. 

Diagnostic: Explains why an issue occurs. Big data analytics enables analysts to delve deeply into things like customer information, marketing metrics, and key performance indicators to explain why particular actions did not generate the desired results. Projects are undertaken with the anticipation of particular outcomes based on market, customer, and other similar estimates.

Predictive: Forecasts future outcomes using historical data. Predictive analytics forecasts future efforts by emphasizing trends and evaluating trajectories of key indicators. Prescriptive analytics goes a step further by forecasting the best future attempts.  

Prescriptive: Prescriptive analytics enables organizations to select how to put their best foot forward by adjusting inputs and changing actions. Varied actions will have different outcomes, and prescriptive analytics assists decision makers in determining the optimum course of action. The analysis layer is evolving in tandem with the ETL layer. AI and machine learning are redefining what analysis can achieve, particularly in the predictive and prescriptive domains. We can now discover insights that were previously unattainable through human examination. 

Consumption: The final Big Data component entails delivering the information in a consumable style to the end user. This can take the shape of tables, complex visualizations, or even single numbers if necessary. This is what corporations utilize to initiate new processes. 

 The most critical aspect of this layer is ensuring that the output’s aim and meaning are clear. Until now, everyone actively involved in the process has been a Data Scientist or literate in components of Data Science. However, executives and decision-makers join the picture in the consumption layer, and they must be able to decipher what the data is saying. 


Big Data has the potential to change everything. Many businesses are turning to data to drive strategic decisions and provide a better consumer experience. A minor improvement in efficiency or the tiniest savings can result in a large profit, which is why most firms are shifting to Big Data. If you’re interested in a career in this field, do check out UNext Jigsaw’s PG Certificate Program in Data Science and Machine Learning 


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