This topic will cover the basics of big data analytics, including what big data is, the importance of analyzing big data, and how big data analytics can provide valuable insights for businesses.
Introduction to Big Data Analytics
Key concepts, workflow, tools, and real‑world applications
| Dimension | Description | Typical Scale |
|---|---|---|
| Volume | Massive amount of data generated every second | Terabytes → Exabytes |
| Velocity | Speed at which data is created, collected, and processed | Real‑time streams, high‑frequency logs |
| Variety | Different formats and sources (structured, semi‑structured, unstructured) | Text, images, video, sensor data, logs |
| Veracity | Trustworthiness & quality of data | Noise, missing values, bias |
| Value | Potential business or scientific insight extracted | Revenue growth, risk reduction, scientific discovery |
The “3Vs” (Volume, Velocity, Variety) are the classic definition; many frameworks add Veracity and Value.
| Technique | Typical Use Cases | Tools / Libraries |
|---|---|---|
| Descriptive statistics | Summarize sales, traffic | Pandas, R, SQL |
| Clustering (k‑means, DBSCAN) | Customer segmentation | Spark MLlib, Scikit‑learn |
| Classification (logistic regression, XGBoost) | Fraud detection, churn prediction | Spark ML, H2O, LightGBM |
| Regression (linear, ridge, LASSO) | Demand forecasting | Statsmodels, Spark ML |
| Time‑series analysis (ARIMA, Prophet) | Sensor data, stock prices | Facebook Prophet, PyFlux |
| Anomaly detection | Network intrusion, equipment failure | Isolation Forest, Azure Anomaly Detector |
| Graph analytics | Social network influence, recommendation | Neo4j, GraphX, |