Chapter 1 Module 1

[Audio] What is Big Data? Understanding the 4 V's of Big Data Chapter 1 - Module 1.

[Audio] Learning Objectives By the end of this module, you will be able to: Define Big Data in an enterprise IT context Explain each of the 4 V's: Volume, Velocity, Variety, Veracity Understand why traditional tools fail at Big Data scale Explain why Big Data challenges led to SIEM platforms like Splunk Identify real-world Big Data examples in operations and security Chapter 1 - Module 1.

[Audio] What Is Big Data? Definition: Big Data refers to data that is too large, too fast, too diverse, or too unreliable to be handled by traditional data processing systems. Key Points: Not defined by size alone Defined by operational limitations Traditional tools struggle with: Storage Processing speed Searchability Data structure Chapter 1 - Module 1.1.1.

[Audio] Why Big Data Matters Modern IT Reality: Systems are distributed (cloud, containers, APIs) Events happen continuously Failures and attacks are buried in noise Impact Areas: Security: Detect threats across millions of events Operations: Identify outages before users complain Compliance: Store and search years of audit data Analytics: Understand behavior, trends, and anomalies Chapter 1 - Module 1.1.1.

[Audio] Why Traditional Tools Fail Traditional Tools Big Data Reality Excel spreadsheets Millions of events per second Single SQL database Distributed data across regions Manual searching Real-time threat detection Static schemas Constantly changing data formats Chapter 1 - Module 1.1.1.

[Audio] The 4 V's of Big Data Volume Velocity Variety Veracity Chapter 1 - Module 1.1.2.

[Audio] 1.1.2 - Volume – How Much Data? Definition: Volume refers to the massive amount of data generated and stored. Examples: Firewall logs Authentication events Application logs Cloud audit trails Endpoint telemetry Key Challenge: Storing and searching terabytes to petabytes efficiently. SIEM Context: A SIEM may ingest billions of events per day. Chapter 1 - Module 1.1.2.

[Audio] Volume in the Real World Security Example: 10,000 endpoints Each sends ~5,000 events/day 50 million events/day Operational Problem: How do you: Store this data? Search it in seconds? Retain it for compliance? Answer: Distributed indexing and scalable platforms. Chapter 1 - Module 1.1.2.

[Audio] Velocity – How Fast Data Arrives Definition: Velocity refers to the speed at which data is generated, transmitted, and processed. Examples: Login attempts in real time API calls per second Network traffic bursts Attack traffic spikes Key Challenge: Data must be processed as it arrives, not hours later. Chapter 1 - Module 1.1.3.

[Audio] Why Velocity Is Critical Security Impact: Attacks happen in seconds Delayed detection = breach Operational Impact: Real-time alerts prevent outages Slow processing = missed incidents SIEM Requirement: Near real-time ingestion and correlation. Chapter 1 - Module 1.1.3.

[Audio] Variety – Different Types of Data Definition: Variety refers to the different formats and structures of data. Types: Structured: Tables, databases Semi-Structured: JSON, XML, CSV Unstructured: Plain text logs, error messages Reality: Most machine data is semi-structured or unstructured. Chapter 1 - Module 1.1.4.

[Audio] Variety in Security Data Examples: Windows Event Logs Linux syslog Firewall logs Cloud audit logs Application logs Challenge: Each source speaks a different language. SIEM Solution: Normalize and extract fields at search time. Chapter 1 - Module 1.1.4.

[Audio] Veracity – Can You Trust the Data? Definition: Veracity refers to the accuracy, quality, and reliability of data. Common Issues: Duplicate events Missing fields Incorrect timestamps Noisy or irrelevant logs Risk: Bad data leads to bad decisions. Chapter 1 - Module 1.1.5.

[Audio] Why Veracity Matters Security Risks: False positives overwhelm analysts False negatives hide real threats Poor context delays response SIEM Focus: Data validation Normalization Context enrichment Chapter 1 - Module 1.1.5.

[Audio] From Big Data to SIEM Platforms Problem: Massive logs High speed Many formats Trust issues Solution: Distributed storage Parallel search Schema-on-read Real-time analytics Outcome: Platforms like SIEMs were built specifically to solve Big Data problems. Chapter 1 - Module 1.1.6.

[Audio] Big Data in Action Security: Brute-force detection Insider threat analysis Malware investigation Operations: Outage detection Performance monitoring Capacity planning IT Analytics: User behavior analysis Trend identification Root cause analysis Chapter 1 - Module 1.1.7.

[Audio] Visualizing the 4 V's Chapter 1 - Module 1.1.1.

[Audio] Key Takeaways Big Data is not just about size Speed, format, and trust matter just as much Traditional tools cannot handle Big Data SIEM platforms exist because of Big Data challenges Understanding the 4 V's is foundational for SIEM mastery Final Message: Big Data is the problem — SIEM is part of the solution. Chapter 1 - Module 1.1.9.

[Audio] What's Next? From Big Data → Machine Data → SIEM Architecture.