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What is Big Data – Characteristics, Types, Benefits & Examples

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What is Big Data – Characteristics, Types, Benefits & Examples

Lately the term ‘Big Data’ has been under the limelight, but not many people know what is big data. Businesses, governmental institutions, HCPs (Health Care Providers), and financial as well as academic institutions, are all leveraging the power of Big Data to enhance business prospects along with improved customer experience.

Simply Stating, What Is Big Data?

Simply stating, big data is a larger, complex set of data acquired from diverse, new, and old sources of data. The data sets are so voluminous that traditional software for data processing cannot manage it. Such massive volumes of data are generally used to address problems in business you might not be able to handle.

IBM maintains that businesses around the world generate nearly 2.5 quintillion bytes of data daily! Almost 90% of the global data has been produced in the last 2 years alone.

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So we know for sure that the best way to answer ‘what is big data’ is mentioning that it has penetrated almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the globe. But, at this point, it is important to know what is big data? Lets talk about big data, characteristics of big data, types of big data and a lot more.

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What is Big Data? Gartner Definition 

According to Gartner, the definition of Big Data – 

“Big data” is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”

This definition clearly answers the “What is Big Data?” question – Big Data refers to complex and large data sets that have to be processed and analyzed to uncover valuable information that can benefit businesses and organizations.

However, there are certain basic tenets of Big Data that will make it even simpler to answer what is Big Data:

  • It refers to a massive amount of data that keeps on growing exponentially with time.
  • It is so voluminous that it cannot be processed or analyzed using conventional data processing techniques.
  • It includes data mining, data storage, data analysis, data sharing, and data visualization.
  • The term is an all-comprehensive one including data, data frameworks, along with the tools and techniques used to process and analyze the data.
Big Data Applications That Surround You

Types of Big Data

Now that we are on track with what is big data, let’s have a look at the types of big data:

Structured

Structured is one of the types of big data and By structured data, we mean data that can be processed, stored, and retrieved in a fixed format. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by simple search engine algorithms. For instance, the employee table in a company database will be structured as the employee details, their job positions, their salaries, etc., will be present in an organized manner. 

Read: Big data engineering jobs and its career opportunities

What is big data technology and its types? Structured one of the types of big data is easy to input, store, query and analyze thanks to its predefined data model and schema. Most traditional databases and spreadsheets hold structured data like tables, rows, and columns.

This makes it simple for analysts to run SQL queries and extract insights using familiar BI tools. However, structuring data requires effort and expertise during the design phase. As data volumes grow to petabyte scale, rigid schemas become impractical and limit the flexibility needed for emerging use cases. Also some data like text, images, video etc. cannot be neatly organized in tabular formats.

Therefore, while structured data brings efficiency, scale and variety of big data necessitates semi-structured and unstructured types of digital data in big data to overcome these limitations. The value lies in consolidating these multiple types rather than relying solely on structured data for modern analytics.

Unstructured

Unstructured data refers to the data that lacks any specific form or structure whatsoever. This makes it very difficult and time-consuming to process and analyze unstructured data. Email is an example of unstructured data. Structured and unstructured are two important types of big data.

Unstructured types of big data constitutes over 80% of data generated today and continues to grow exponentially from sources like social posts, digital images, videos, audio files, emails, and more. It does not conform to any data model, so conventional tools cannot give meaningful insights from it. However, unstructured data tends to be more subjective, rich in meaning, and reflective of human communication compared to tabular transaction data. 

With immense business value hidden inside, specialized analytics techniques involving NLP, ML, and AI are essential to process high volumes of unstructured content. For instance, sentiment analysis of customer social media rants can alert companies to issues before mainstream notice.

Text mining of maintenance logs and field technician reports can improve future product designs. And computer vision techniques on image data from manufacturing floors can automate quality checks. While analysis requires advanced skill, the unstructured data’s scale, variety, and information density deliver new opportunities for competitive advantage across industries.

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Semi-structured

Semi structured is the third type of big data. Semi-structured data pertains to the data containing both the formats mentioned above, that is, structured and unstructured data. To be precise, it refers to the data that although has not been classified under a particular repository (database), yet contains vital information or tags that segregate individual elements within the data. Thus we come to the end of types of data. Lets discuss the characteristics of data.

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Semi-structured variety in big data includes elements of both structured and unstructured data. For example, XML, JSON documents contain tags or markers to separate semantic elements, but the data is unstructured free flowing text, media, etc. Clickstream data from website visits have structured components like timestamps and pages visited, but the path a user takes is unpredictable. Sensor data with timestamped values is semi-structured. This hybrid data abstraction effortlessly incorporates the variety and volume of big data across system interfaces. 

For analytic applications, semi-structured data poses technical and business-level complexities for processing, governance, and insight generation. However, flexible schemas and object-oriented access methods are better equipped to handle velocity and variety in semi-structured types of digital data in big data at scale. With rich contextual information encapsulated, established databases have expanded native JSON, XML, and Graph support for semi-structured data to serve modern real-time analytics needs.

Characteristics of Big Data

Back in 2001, Gartner analyst Doug Laney listed the 3 ‘V’s of Big Data – Variety, Velocity, and Volume. Let’s discuss the characteristics of big data.
These characteristics, isolatedly, are enough to know what is big data. Let’s look at them in depth:

1) Variety

Variety of Big Data refers to structured, unstructured, and semistructured data that is gathered from multiple sources. While in the past, data could only be collected from spreadsheets and databases, today data comes in an array of forms such as emails, PDFs, photos, videos, audios, SM posts, and so much more.

Variety is one of the important characteristics of big data. The traditional types of data are structured and also fit well in relational databases. With the rise of big data, the data now comes in the form of new unstructured types. These unstructured, as well as semi-structured data types, need additional pre-processing for deriving meaning and support of metadata.

2) Velocity

Velocity essentially refers to the speed at which data is being created in real-time. In a broader prospect, it comprises the rate of change, linking of incoming data sets at varying speeds, and activity bursts. The speed of data receipt and action is simply known as velocity. The highest velocity for data will stream directly into the memory against being written to the disk. Few internet-based smart products do operate in real-time or around real-time. This mostly requires evaluation as well as in real-time.

Learn: Mapreduce in big data

The velocity of variety in big data is crucial because it allows companies to make quick, data-driven decisions based on real-time insights. As data streams in at high speeds from sources like social media, sensors, mobile devices, etc., companies can spot trends, detect patterns, and derive meaning from that data more rapidly. High velocity characteristics of big data combined with advanced analytics enables faster planning, problem detection, and decision optimization. For example, a company monitoring social media chatter around its brand can quickly respond to emerging issues before they spiral out of control.

3) Volume

Volume is one of the characteristics of big data. We already know that Big Data indicates huge ‘volumes’ of data that is being generated on a daily basis from various sources like social media platforms, business processes, machines, networks, human interactions, etc. Such a large amount of data are stored in data warehouses. Thus comes to the end of characteristics of big data.

The data volume matters when you discuss the big data characteristics. In the context of big data, you will need to process a very high volume of low-density or unstructured data. This will be data related to an unknown value. Example data feeds on Twitter, clickstreams on web pages or mobile apps, or even sensor-based equipment. For a few organizations, it means ten times a few terabytes of data. For some others, it could mean hundreds of times petabytes.

Big Data Roles and Salaries in the Finance Industry

Advantages of Big Data (Features)

  • One of the biggest advantages of Big Data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks.
  • By harnessing data from social media platforms using Big Data analytics tools, businesses around the world are streamlining their digital marketing strategies to enhance the overall consumer experience. Big Data provides insights into the customer pain points and allows companies to improve upon their products and services.
  • Being accurate, Big Data combines relevant data from multiple sources to produce highly actionable insights. Almost 43% of companies lack the necessary tools to filter out irrelevant data, which eventually costs them millions of dollars to hash out useful data from the bulk. Big Data tools can help reduce this, saving you both time and money.
  • Big Data analytics could help companies generate more sales leads which would naturally mean a boost in revenue. Businesses are using Big Data analytics tools to understand how well their products/services are doing in the market and how the customers are responding to them. Thus, the can understand better where to invest their time and money.
  • With Big Data insights, you can always stay a step ahead of your competitors. You can screen the market to know what kind of promotions and offers your rivals are providing, and then you can come up with better offers for your customers. Also, Big Data insights allow you to learn customer behaviour to understand the customer trends and provide a highly ‘personalized’ experience to them.

Read: Career Scope for big data jobs.

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Who is using Big Data? 5 Applications

The people who’re using Big Data know better that, what is Big Data. Let’s look at some such industries:

1) Healthcare

Big Data has already started to create a huge difference in the healthcare sector. With the help of predictive analytics, medical professionals and HCPs are now able to provide personalized healthcare services to individual patients. Apart from that, fitness wearables, telemedicine, remote monitoring – all powered by Big Data and AI – are helping change lives for the better.

The healthcare industry is harnessing big data in various innovative ways – from detecting diseases faster to providing better treatment plans and preventing medication errors. By analyzing patient history, clinical data, claims data, and more, healthcare providers can better understand patient risks, genetic factors, environmental factors to customize treatments rather than follow a one-size-fits-all approach.

Population health analytics on aggregated EMR data also allows hospitals to reduce readmission rates and unnecessary costs. Pharmaceutical companies are leveraging big data to improve drug formulation, identify new molecules, and reduce time-to-market by analyzing years of research data. The insights from medical imaging data combined with genomic data analysis enables precision diagnosis at early stages.

2) Academia

Big Data is also helping enhance education today. Education is no more limited to the physical bounds of the classroom – there are numerous online educational courses to learn from. Academic institutions are investing in digital courses powered by Big Data technologies to aid the all-round development of budding learners.

Educational institutions are leveraging big data in dbms in multifaceted ways to elevate learning experiences and optimize student outcomes. By analyzing volumes of student academic and behavioral data, predictive models identify at-risk students early to recommend timely interventions. Tailored feedback is provided based on individual progress monitoring.

Curriculum design and teaching practices are refined by assessing performance patterns in past course data. Self-paced personalized learning platforms powered by AI recommend customized study paths catering to unique learner needs and competency levels. Academic corpus and publications data aids cutting-edge research and discovery through knowledge graph mining and natural language queries.

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3) Banking

The banking sector relies on Big Data for fraud detection. Big Data tools can efficiently detect fraudulent acts in real-time such as misuse of credit/debit cards, archival of inspection tracks, faulty alteration in customer stats, etc.

Banks and financial institutions depend heavily on big data in dbms and analytics to operate services, reduce risks, retain customers, and increase profitability. Predictive models flag probable fraudulent transactions in seconds before completion by scrutinizing volumes of past transactional data, customer information, credit history, investments, and third-party data. Connecting analytics to the transaction processing pipeline has immensely reduced false declines and improved fraud detection rates. Client analytics helps banks precisely segment customers, contextualise engagement through the right communication channels, and accurately anticipate their evolving needs to recommend the best financial products.

Processing volumes of documentation and loan application big data types faster using intelligent algorithms and automation enables faster disbursal with optimized risks. Trading firms leverage big data analytics on historical market data, economic trends, and news insights to support profitable investment decisions. Thus, big data radically enhances banking experiences by minimizing customer risks and maximizing personalisation through every engagement.

4) Manufacturing

According to TCS Global Trend Study, the most significant benefit of Big Data in manufacturing is improving the supply strategies and product quality. In the manufacturing sector, Big data helps create a transparent infrastructure, thereby, predicting uncertainties and incompetencies that can affect the business adversely.

Manufacturing industries are optimizing end-to-end value chains using volumes of operational data generated from sensors, equipment logs, inventory flows, supplier networks, and customer transactions. By combining this real-time structured and unstructured big data types with enterprise data across siloed sources, manufacturers gain comprehensive visibility into operational performance, production quality, supply-demand dynamics, and fulfillment. Advanced analytics transforms this data into meaningful business insights around minimizing process inefficiencies, improving inventory turns, reducing machine failures, shortening production cycle times, and meeting dynamic customer demands continually.

Overall, equipment effectiveness is improved with predictive maintenance programs. Data-based simulation, scheduling, and control automation increases speed, accuracy, and compliance. Real-time synchronization of operations planning with execution enabled by big data analytics creates the responsive and intelligent factory of the future.

5) IT

One of the largest users of Big Data, IT companies around the world are using Big Data to optimize their functioning, enhance employee productivity, and minimize risks in business operations. By combining Big Data technologies with ML and AI, the IT sector is continually powering innovation to find solutions even for the most complex of problems.
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The technology and IT sectors pioneer big data-enabled transformations across other industries, though the first application starts from within. IT infrastructure performance, application usage telemetry, network traffic data, security events, and business KPIs provide technology teams with comprehensive observability into systems health, utilization, gaps and dependencies. This drives data-based capacity planning, proactive anomaly detection and accurate root cause analysis to optimize IT service quality and employee productivity. User behavior analytics identifies the most valued features and pain points to prioritize software enhancements aligned to business needs.

For product companies, big data analytics features of big data logs, sensor data, and customer usage patterns enhances user experiences by detecting issues and churn faster. Mining years of structured and unstructured data aids context-aware conversational AI feeding into chatbots and virtual assistants. However, robust information management and governance practices remain vital as the scale and complexity of technology data environments continue to expand massively. With positive business outcomes realized internally, IT domain expertise coupled with analytics and AI skillsets power data transformation initiatives across external customer landscapes.

6. Retail

Big Data has changed the way of working in traditional brick and mortar retail stores. Over the years, retailers have collected vast amounts of data from local demographic surveys, POS scanners, RFID, customer loyalty cards, store inventory, and so on. Now, they’ve started to leverage this data to create personalized customer experiences, boost sales, increase revenue, and deliver outstanding customer service.

Retailers are even using smart sensors and Wi-Fi to track the movement of customers, the most frequented aisles, for how long customers linger in the aisles, among other things. They also gather social media data to understand what customers are saying about their brand, their services, and tweak their product design and marketing strategies accordingly. 

7. Transportation 

Big Data Analytics holds immense value for the transportation industry. In countries across the world, both private and government-run transportation companies use Big Data technologies to optimize route planning, control traffic, manage road congestion, and improve services. Additionally, transportation services even use Big Data to revenue management, drive technological innovation, enhance logistics, and of course, to gain the upper hand in the market.

The transportation sector is adopting big data and IoT technologies to monitor, analyse, and optimize end-to-end transit operations intelligently. Transport authorities can dynamically control traffic flows, mitigating congestion, optimising tolls, and identifying incidents faster by processing high-velocity telemetry data streams from vehicles, roads, signals, weather systems, and rider mobile devices. Journey reliability and operational efficiency are improved through data-based travel demand prediction, dynamic route assignment, and AI-enabled dispatch. Predictive maintenance reduces equipment downtime. Riders benefit from real-time tracking, estimated arrivals, and personalized alerts, minimising wait times.

Logistics players leverage big data for streamlined warehouse management, load planning, and shipment route optimisation, driving growth and customer satisfaction. However, key challenges around data quality, privacy, integration, and skills shortage persist. They need coordinated efforts from policymakers and technology partners before their sustainable value is fully realised across an integrated transportation ecosystem.

Big Data Case studies

1. Walmart

 Walmart leverages Big Data and Data Mining to create personalized product recommendations for its customers. With the help of these two emerging technologies, Walmart can uncover valuable patterns showing the most frequently bought products, most popular products, and even the most popular product bundles (products that complement each other and are usually purchased together).

Based on these insights, Walmart creates attractive and customized recommendations for individual users. By effectively implementing Data Mining techniques, the retail giant has successfully increased the conversion rates and improved its customer service substantially. Furthermore, Walmart uses Hadoop and NoSQL technologies to allow customers to access real-time data accumulated from disparate sources. 

2. American Express

The credit card giant leverages enormous volumes of customer data to identify indicators that could depict user loyalty. It also uses Big Data to build advanced predictive models for analyzing historical transactions along with 115 different variables to predict potential customer churn. Thanks to Big Data solutions and tools, American Express can identify 24% of the accounts that are highly likely to close in the upcoming four to five months. 

3. General Electric

In the words of Jeff Immelt, Chairman of General Electric, in the past few years, GE has been successful in bringing together the best of both worlds – “the physical and analytical worlds.” GE thoroughly utilizes Big Data. Every machine operating under General Electric generates data on how they work. The GE analytics team then crunches these colossal amounts of data to extract relevant insights from it and redesign the machines and their operations accordingly.

Today, the company has realized that even minor improvements, no matter how small, play a crucial role in their company infrastructure. According to GE stats, Big Data has the potential to boost productivity by 1.5% in the US, which compiled over a span of 20 years could increase the average national income by a staggering 30%!

4. Uber

 Uber is one of the major cab service providers in the world. It leverages customer data to track and identify the most popular and most used services by the users. Once this data is collected, Uber uses data analytics to analyze the usage patterns of customers and determine which services should be given more emphasis and importance.

Apart from this, Uber uses Big Data in another unique way. Uber closely studies the demand and supply of its services and changes the cab fares accordingly. It is the surge pricing mechanism that works something like this – suppose when you are in a hurry, and you have to book a cab from a crowded location, Uber will charge you double the normal amount!  

5. Netflix

Netflix is one of the most popular on-demand online video content streaming platform used by people around the world. Netflix is a major proponent of the recommendation engine. It collects customer data to understand the specific needs, preferences, and taste patterns of users. Then it uses this data to predict what individual users will like and create personalized content recommendation lists for them.

Today, Netflix has become so vast that it is even creating unique content for users. Data is the secret ingredient that fuels both its recommendation engines and new content decisions. The most pivotal data points used by Netflix include titles that users watch, user ratings, genres preferred, and how often users stop the playback, to name a few. Hadoop, Hive, and Pig are the three core components of the data structure used by Netflix. 

6. Procter & Gamble

Procter & Gamble has been around us for ages now. However, despite being an “old” company, P&G is nowhere close to old in its ways. Recognizing the potential of Big Data, P&G started implementing Big Data tools and technologies in each of its business units all over the world. The company’s primary focus behind using Big Data was to utilize real-time insights to drive smarter decision making.

To accomplish this goal, P&G started collecting vast amounts of structured and unstructured data across R&D, supply chain, customer-facing operations, and customer interactions, both from company repositories and online sources. The global brand has even developed Big Data systems and processes to allow managers to access the latest industry data and analytics.

7. IRS

Yes, even government agencies are not shying away from using Big Data. The US Internal Revenue Service actively uses Big Data to prevent identity theft, fraud, and untimely payments (people who should pay taxes but don’t pay them in due time).

The IRS even harnesses the power of Big Data to ensure and enforce compliance with tax rules and laws. As of now, the IRS has successfully averted fraud and scams involving billions of dollars, especially in the case of identity theft. In the past three years, it has also recovered over US$ 2 billion.

Careers In Big Data

Big data characteristics are seemingly transforming the way businesses work while also driving growth through the economy globally.

Businesses are observing immense benefits using the characteristics of big data for protecting their database, aggregating huge volumes of information, as well as making informed decisions to benefit organizations. No wonder it is clear that big data has a huge range across a number of sectors.

For instance, in the financial industry, big data comes across as a vital tool that helps make profitable decisions. Similarly, some data organizations might look at big data as a means for fraud protection and pattern detection in large-sized datasets. Nearly every large-scale organization currently seeks talent in big data, and hopefully, the demand is prone to a significant rise in the future as well.

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Wrapping Up

We hope we were able to answer the “What is Big Data?” question clearly enough. We hope you understood about the types of big data, characteristics of big data, use cases, etc. 

Organizations actually mine both unstructured as well structured data sets. This helps in leveraging machine learning as well as framing predictive modeling techniques. The latter helps extract meaningful insights. With such findings, a data manager will be able to make data-driven decisions and solve a plethora of main business problems.

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A number of significant technical skills help individuals succeed in the field of big data. Such skills include-

  •       Data mining
  •       Programming
  •       Data visualization
  •       Analytics

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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Abhinav Rai

Blog Author
Abhinav is a Data Analyst at UpGrad. He's an experienced Data Analyst with a demonstrated history of working in the higher education industry. Strong information technology professional skilled in Python, R, and Machine Learning.
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Frequently Asked Questions (FAQs)

1What are the different jobs that professionals learning Big Data can get?

The increase in the usage of Big Data in companies and industries has led to the rise in the demand for Big Data professionals. Many jobs need the knowledge of Big Data and its applications. Data Scientists are the most common and well-known Big Data job profiles. They organise, clean, and consolidate data from many sources. Data engineers are computing experts who are in charge of planning, constructing, and maintaining extensive data infrastructure. While data engineers efficiently handle data, data scientists focus on data analysis. Machine Learning engineers are hard-core computer programmers who create and apply algorithms for analysing Big Data analytics trends. As a result, the system makes decisions based on historical data trends. There are many more jobs for Big Data professionals such as Hadoop scientists, Hadoop engineers, etc.

2What are the responsibilities of Big Data professionals?

Big Data experts are in charge of importing data from many sources, performing high-speed queries, and recommending best practices and standards. Designers, builders, installers, configures, and supporters of Hadoop are anticipated. They manage and implement HBase while also ensuring data security and privacy. These experts also analyse and unearth insights from a large variety of data repositories. Big Data developers are in charge of developing data-tracking web services that are scalable and high-performing. They also create detailed designs for complex technological and functional needs.

3How can I become a successful Big Data Developer?

The first step in becoming a successful Big Data Developer is to learn Hadoop. Hadoop is a comprehensive ecosystem, not just a single phrase. The Hadoop ecosystem includes a variety of technologies that serve various objectives. Secondly, Learning Spark, a real-time distributed processing framework with in-memory computing capabilities, is the ideal choice for Big Data developers who are proficient in any of the real-time processing frameworks. You must have the knowledge of any coding language such as C, SQL, R, or Python too.

4What are the different jobs that professionals learning Big Data can get?

The increase in the usage of Big Data in companies and industries has led to the rise in the demand for Big Data professionals. Many jobs need the knowledge of Big Data and its applications. Data Scientists are the most common and well-known Big Data job profiles. They organise, clean, and consolidate data from many sources. Data engineers are computing experts who are in charge of planning, constructing, and maintaining extensive data infrastructure. While data engineers efficiently handle data, data scientists focus on data analysis. Machine Learning engineers are hard-core computer programmers who create and apply algorithms for analysing Big Data analytics trends. As a result, the system makes decisions based on historical data trends. There are many more jobs for Big Data professionals such as Hadoop scientists, Hadoop engineers, etc.

5What are the responsibilities of Big Data professionals?

Big Data experts are in charge of importing data from many sources, performing high-speed queries, and recommending best practices and standards. Designers, builders, installers, configures, and supporters of Hadoop are anticipated. They manage and implement HBase while also ensuring data security and privacy. These experts also analyse and unearth insights from a large variety of data repositories. Big Data developers are in charge of developing data-tracking web services that are scalable and high-performing. They also create detailed designs for complex technological and functional needs.

6How can I become a successful Big Data Developer?

The first step in becoming a successful Big Data Developer is to learn Hadoop. Hadoop is a comprehensive ecosystem, not just a single phrase. The Hadoop ecosystem includes a variety of technologies that serve various objectives. Secondly, Learning Spark, a real-time distributed processing framework with in-memory computing capabilities, is the ideal choice for Big Data developers who are proficient in any of the real-time processing frameworks. You must have the knowledge of any coding language such as C, SQL, R, or Python too.

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