What Is Big Data?

Big data refers to data sets whose volume, velocity and variety exceed what traditional databases and tools can capture, store and analyze efficiently. Working with big data requires distributed storage, parallel processing and specialized platforms that turn very large, fast-moving data into useful information for decision-making and AI.

This guide covers the 4 Vs of big data, how big data differs from conventional data, the main components of a big data ecosystem, what big data professionals do, and how to validate your knowledge with the CertiProf Big Data Professional Certification (BDPC™).

Big data, explained

Every app, payment, sensor, website and machine generates data. When an organization collects so much of it, so quickly and in so many formats that a single database server can no longer keep up, it has a big data problem. The solution is not a bigger server but a different approach: distribute the data and the workload across many machines and process them in parallel.

Big data matters because of what it enables: personalized recommendations, real-time fraud detection, demand forecasting, predictive maintenance and, more recently, the training data and pipelines behind machine learning and generative AI. Well-managed data leads to better business decisions.

The 4 Vs of big data

Big data is commonly described by four characteristics, all of which the BDPC™ covers:

  • Volume

    The amount of data: terabytes or petabytes of transactions, logs, images and documents, more than a single machine can store or process.

  • Velocity

    How fast data arrives and must be processed, from nightly batches to real-time streams of clicks, payments or sensor readings.

  • Variety

    The range of formats: structured tables, semi-structured JSON and logs, and unstructured text, images, audio and video.

  • Veracity

    How trustworthy the data is: its accuracy, completeness and consistency. Large data sets are useful only if they can be trusted.

Big data vs. conventional data

The difference goes beyond size. Speed, formats, and how data is stored and processed also change:

Big data Conventional data
Size Terabytes to petabytes and growing Megabytes to gigabytes
Speed Continuous streams, often real time Periodic updates or batches
Formats Structured, semi-structured and unstructured Mostly structured tables
Storage Distributed: data lakes, lakehouses, NoSQL, cloud object storage A single relational database
Processing Parallel across a cluster of machines A single server running SQL queries
Typical use Personalization, fraud detection, IoT, AI training data Accounting, inventory, standard reports

The big data ecosystem: key components

A big data platform is a chain of layers. Data flows from left to right, and governance spans every layer:

  1. Sources

    Apps, transactions, websites, mobile devices, sensors, social media and third-party data.

    Databases, logs, APIs, IoT

  2. Ingestion

    Collects data and moves it into the platform, in batches or as continuous streams.

    Batch loads, event streaming (e.g. Apache Kafka)

  3. Storage

    Stores raw and processed data at scale and at low cost.

    Data lakes, data warehouses, lakehouses, NoSQL, HDFS

  4. Processing

    Cleans, joins and transforms data in parallel across many machines.

    Apache Spark, Hadoop MapReduce, stream processing

  5. Analytics and AI

    Turns processed data into reports, dashboards, predictions and machine learning models.

    SQL engines, BI tools, data science and ML platforms

  6. Governance and security

    Governs quality, access, privacy and compliance across every layer.

    Data catalogs, access control, encryption, data quality rules

The tools listed are examples of widely used open-source and cloud technologies, not requirements.

Big data challenges and how they are solved

Managing big data poses technological challenges, each with well-established solutions:

  • ChallengeStoring ever-growing data affordably
    Solution

    Distributed file systems, cloud object storage and data lakes that scale out by adding machines.

  • ChallengeProcessing data fast enough
    Solution

    Parallel processing frameworks that split each job across a cluster, plus in-memory computing.

  • ChallengeHandling real-time data
    Solution

    Event streaming and stream processing that analyze data as it arrives, not hours later.

  • ChallengeCombining many formats
    Solution

    Schema-on-read storage, NoSQL databases and lakehouse table formats that accept structured and unstructured data.

  • ChallengeTrusting the data
    Solution

    Data quality checks, metadata, lineage and clear ownership of each data set.

  • ChallengeProtecting privacy and security
    Solution

    Access control, encryption, anonymization and compliance with data protection laws.

What does a big data professional do?

“Big data professional” covers several roles. They share the same core concepts but focus on different parts of the platform:

Data engineer Data analyst Data scientist Data architect
Focus Builds and runs the pipelines that move and prepare data Answers business questions with data Builds predictive and machine learning models Designs how data is stored, integrated and governed
Big data work Ingestion, storage, Spark jobs, streaming SQL on large data sets, dashboards Feature engineering, model training at scale Platform choice, data models, security
Related CertiProf credentials BDPC™ BDPC™, BIAPC™ BDPC™, AIFPC™ BDPC™

Big data career outlook

The U.S. Bureau of Labor Statistics does not track “big data” as a separate occupation, but two closely related occupations it does track show strong pay:

USD 120,230

Median annual wage for data scientists in the United States in May 2025. Employment is projected to grow 35% from 2025 to 2035, much faster than average.

USD 139,500

Median annual wage for database architects in the United States in May 2025. They design the systems that store and organize an organization’s data.

Source: U.S. Bureau of Labor Statistics, Occupational Outlook Handbook (Data Scientists, Database Administrators and Architects). Pay varies by country, experience, company and job title.

How to start a career in big data

  1. Learn data fundamentals

    Understand databases, SQL, file formats and the difference between structured and unstructured data.

  2. Learn big data concepts

    Study the 4 Vs, distributed storage and processing, batch vs. streaming, and data lakes vs. data warehouses.

  3. Get hands-on experience

    Practice with an open-source tool such as Apache Spark, or use a cloud free tier with a public data set.

  4. Get certified

    Validate your knowledge with the CertiProf Big Data Professional Certification (BDPC™) online exam. The exam requires no programming.

  5. Choose your path

    Go deeper into analysis with Business Intelligence Analyst (BIAPC™), data presentation with Data Storytelling (DSTPC™), or AI with AI Foundation (AIFPC™).

Who the BDPC™ is for

  • Data analysts and data engineers
  • IT professionals
  • Business analysts and managers
  • Data scientists
  • Academics and researchers
  • Students who want a career in data

Get your Big Data Professional certification

CertiProf Big Data Professional Certification (BDPC™)

Validates your understanding of big data, its ecosystem, its technological challenges and the solutions that address them.

What the BDPC™ covers

  • Understand big data and its business benefits
  • Distinguish big data from conventional data
  • Explain the 4 Vs: volume, velocity, variety and veracity
  • Describe the big data ecosystem and its key components
  • Assess the main technological challenges in managing big data
  • Match each challenge to the technology solutions that address it
Format
Multiple choice
Questions
40
Passing score
32 of 40 (80%)
Duration
60 minutes
Delivery
Online
Languages
English, Spanish, Portuguese, French
Certification type
Professional
Validity
3 years
Prerequisites
None
  • 2 exam attempts
  • Voucher emailed immediately after purchase
  • Voucher valid for 6 months
  • Digital badge issued through BadgeClaimed (Open Badges 3.0)
See pricing and buy

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BDPC™ success stories

Muy feliz por este nuevo logro. Fue una muy buena experiencia tener la oportunidad de estudiar con un grupo de profesionales y personas dispuestas a ayudarnos unos a otros y a compartir sus experiencias sobre Big Data.

Original in Spanish

Karolina PiedrahitaPetroleum Engineer
Vamos por más!!! Siempre Continuous Learning, echaba de menos no poder dedicarme a uno de mis hobbies favoritos. Volviendo a las pistas!!!

Original in Spanish

Eduardo Sánchez VillegasCloud Manager at Deloitte

Companies with BDPC™-certified professionals

  • Guide
  • Banco Azteca
  • Grupo Salinas
  • IDAT
  • Elektra
  • DC Dinsmore
  • Kyndryl
  • Claro
  • UTEC
  • Certus

Frequently asked questions

What is big data?

Big data refers to data sets whose volume, velocity and variety exceed what traditional databases and tools can capture, store and analyze efficiently. Organizations manage it with distributed storage and parallel processing to turn it into insights, predictions and AI models.

What are the 4 Vs of big data?

The 4 Vs are volume (how much data there is), velocity (how fast it arrives and must be processed), variety (how many formats it comes in, from tables to text, images and video) and veracity (how accurate and trustworthy it is). Some authors add a fifth V, value: the business benefit the data delivers.

What is the difference between big data and conventional data?

Conventional data is mostly structured, fits in a single relational database and is updated in batches. Big data is much larger, often arrives continuously and mixes structured, semi-structured and unstructured formats, so it requires distributed storage and parallel processing across many machines.

What does a big data professional do?

A big data professional designs, builds or uses systems that collect, store, process and analyze very large data sets. Depending on the role, the work includes building data pipelines (data engineer), designing the data platform (data architect), analyzing data (data analyst) or training models (data scientist).

How is big data related to artificial intelligence?

Machine learning and generative AI models learn from large amounts of data, and big data platforms and pipelines supply that data. Data quality, governance and veracity directly affect how accurate and fair an AI model is.

Do I need programming skills for the BDPC™ certification?

No. The Big Data Professional Certification (BDPC™) exam requires no programming skills. It tests your understanding of big data concepts, the big data ecosystem, its technological challenges and the solutions that address them. A basic understanding of data, storage and distributed processing is helpful.

What is the BDPC™ exam like?

The BDPC™ exam is taken online and has 40 multiple-choice questions with a 60-minute time limit. You need 32 correct answers (80%) to pass. The exam is available in English, Spanish, Portuguese and French, and the certification is valid for 3 years.

What happens if I don’t pass the BDPC™ exam?

The BDPC™ voucher includes 2 exam attempts, so you can retake the exam once. The voucher is valid for 6 months from purchase; additional attempts require a new voucher.

What is the difference between BDPC™ and BIAPC™?

The Big Data Professional Certification (BDPC™) covers the concepts, architecture and challenges of managing and processing large volumes of data. The Business Intelligence Analyst Professional Certification (BIAPC™) covers hands-on analysis and dashboards with Power BI and Qlik Sense. Choose BDPC™ for data platforms and scale, and BIAPC™ for reporting and visualization.

Is a big data certification worth it?

Yes, if you work with data or want to move into data engineering, analytics or AI. The BDPC™ gives you a structured overview of big data concepts and technologies, plus a verifiable digital badge you can share with employers and on LinkedIn.

Ready to become a certified big data professional?

Take the BDPC™ exam online, any day.

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