Techniques and tools for representing complex data through graphical interfaces to reveal patterns, trends, and insights.
Tools range from libraries (Matplotlib, D3.js) to platforms (Tableau, Power BI) and Hadoop-integrated tools (Zeppelin, Superset).
Key techniques: time-series charts, heatmaps, geospatial mapping, network graphs, and interactive dashboards.
Big data challenges: sampling strategies for large datasets, real-time streaming visualizations (Kafka + Spark), and distributed rendering.
Integrates with Hadoop/Spark pipelines - e.g., visualizing ML model results or aggregated NoSQL query outputs.
Critical for exploratory data analysis (EDA), business intelligence, and communicating data-driven insights.