Definition of Big Data
If someone says Big Data, it means huge volume, complex, and diversified datasets that are generated at high rate from distinct sources. These datasets are too big to be processed, stored, or analyzed using legacy data processing infrastructure. Instead, they require sophisticated frameworks and technologies for efficient processing.
Big Data Characterized in terms of V’s
- Volume: Refers to the amount of data. Big Data may have thousands of entities or elements in billions of records.
- Velocity: Refers to the speed at which data is captured, generated, or shared.
- Variety / Variability: Refers to the forms in which data is captured or delivered. Big Data requires storage of multiple formats; data structure is often inconsistent within or across data sets.
- Viscosity: Refers to how difficult the data is to use or integrate.
- Volatility: Refers to how often data changes occur and, how long the data is useful.
- Veracity: Refers to how trustworthy, accurate and reliable the data is.
- Value: The potential business insights and benefits, derived from analyzing Big Data.
Types of Big Data: Big: Data can be classified into three main categories.
- Structured Data
- Organized data that follows a defined tabular format and can be easily stored in RDBMS/SQL databases, and spreadsheets).
- Examples: employee profiles, customer records, financial transactions.
- Semi-Structured Data
- Partially organized data that does not fit properly into SQL Databases but still has some kind of structure.
- Examples: JSON, XML, logs, emails etc.
- Unstructured Data
- Data without any predefined structure, causing it difficult to store and analyze. It normally requires NoSQL Databases.
- Examples: Videos, images, audio files, social media posts, and IOT devices, and sensor data.
Sources of Big Data: Big Data is generated from different sources, which may include
- Social Media: Platforms like Linkedin, Twitter, Facebook, and Instagram generate huge amount of user-generated content.
- IoT Devices: Sensors, smart appliances, and wearable devices continuously produce real-time data.
- E-commerce and Transactions: Online shopping, banking, and POS (Point-of-Sale) systems create extensive transactional data, at high rate.
- Government and Public Sector: Census data, weather forecasting, and surveillance systems also contribute significantly to Big Data.
- Enterprise Data: Business operations, Customer Data Platforms, CRM systems, and financial records add to corporate Big Data.
Challenges of Big Data: Despite its benefits, Big Data presents several challenges such as
- Storage and Scalability: Handling petabytes or exabytes of data efficiently, is not a negligible challenge from Scalability, and Storage perspective.
- Data Integration: State-of-the-art tools, and technologies are required to combine structured and unstructured data from different sources.
- Processing Speed: An appropriately designed infrastrucute is essential for analyzing massive datasets in real-time.
- Data Quality and Veracity: In addition to leveraging latest technological capabilities, skilled manpower is essential to ensure accurate, clean, and reliable data.
- Security and Privacy: Emerging threats necessitate protecting sensitive information under regulations like the PDPL, and GDPR etc.
- Cost Management: Managing infrastructure costs for storage and computing is another great challenge for organizations.
Future of Big Data: The future of Big Data looks like promising, with emerging trends for example
- AI and Machine Learning Integration – Advanced algorithms will improve predictive analytics and automation.
- Edge Computing – Data processing at the edge (for example IoT Devices) will reduce latency.
- Blockchain for Data Security – Enhancing data integrity and security.
- Big Data in the Metaverse – Real-time data streaming for immersive virtual experiences.
Further Recommended Resources
- Big Data vs. Traditional Data, Data Warehousing, AI, and Beyond
- A Comparative Analysis – OBIEE vs. GA4 vs. Power BI
- Big Data Transformation Across Industries
- Big Data Security, Privacy, and Protection, & Addressing the Challenges of Big Data
- Designing Big Data Infrastructure and Modeling
- Leveraging Big Data through NoSQL Databases
- BDaaS (Big Data As-a-Service) – Data Governance Principles
- BDaaS (Big Data As-a-Service) – Compliance Features
- BDaaS (Big Data As-a-Service) – Data Governance Frameworks
- BDaaS (Big Data As-a-Service) – Real World Use Cases, and Scenarios
- BDaaS (Big Data As-a-Service) – General Activation Steps
- BDaaS (Big Data As-a-Service) – Enablement Methodology
- BDaaS (Big Data As-a-Service) – Challenges & Risks in BDaaS Implementation
- BDaaS (Big Data As-a-Service) – Shared Responsibility Model – MDM Team
- BDaaS (Big Data As-a-Service) – Continuous Improvement Cycle
- Data Strategy vs. Data Platform Strategy
- ABAC – Attribute-Based Access Control
- Consequences of Personal Data Breaches
- Key Prerequisites for Successful KSA PDPL Implementation
- KSA PDPL (Personal Data Protection Law) – Initial Framework
- KSA PDPL – Consent Not Mandatory
- KSA PDPL Article 4, 5, 6, 7, 8, 9, 10, 11, & 12
- KSA PDPL Article 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, & 31
- KSA NDMO – Data Catalog and Metadata
- KSA NDMO – Personal Data Protection – Initial Assessment
- KSA NDMO – DG Artifacts Control – Data Management Issue Tracking Register
- KSA NDMO – Personal Data Protection – PDP Plan, & PDP Training, Data Breach Notification
- KSA NDMO – Classification Process, Data Breach Management, & Data Subject Rights
- KSA NDMO – Privacy Notice and Consent Management
- Enterprise Architecture Governance & TOGAF – Components
- Enterprise Architecture & Architecture Framework
- TOGAF – ADM (Architecture Development Method) vs. Enterprise Continuum
- TOGAF – Architecture Content Framework
- TOGAF – ADM Features & Phases
- Data Security Standards
- Data Steward – Stewardship Activities
- Data Modeling – Metrics and Checklist
- How to Measure the Value of Data
- What is Content and Content Management?
