With the explosive growth of big data, technical approaches for big‑data analytics and data visualization keep evolving. Raw data can be abstract, yet visualization presents it in an intuitive and vivid manner, making it easier for users to analyze data and extract valuable insights. Optimal approaches enable you to leverage big‑data technologies from the most diverse sources. For each dataset presentation, you need to select the visualization that best communicates the underlying message. What are the most practical chart types supported by visualization‑oriented big‑data analytics platforms? Examples are listed below:

- Visual Metric Gauge: Simple gauge‑style visual indicators instantly show whether performance is above or below targets and whether progress is on track.
- Visual Line Chart: Line charts excel at illustrating relationships among data points. They are commonly used to reflect changes and trends or compare multiple components over given time periods.
- Visual Bar Chart: Bar charts compare quantities across different categories or items. The length or height of each bar represents the value of the corresponding category or item.
- Visual Pie Chart and Donut Chart: Pie charts and donut charts are divided into segments representing numerical values. The angle of each segment corresponds to its value. These charts help you compare the proportions of different parts within a whole.
- Visual Area Chart: Area charts deliver insights into overall volume as well as the proportion occupied by each category. They work well for gaining insights into resource and financial planning & allocation.
- Visual Pivot Table: Pivot tables combine tabular graphical elements with key figures, offering instant visual references for entry comparison alongside precise numerical values. They are useful when comparing sub‑components within one category.
- Visual Scatter Plot / Map: This visualization renders markers such as dots, squares or plus signs to reflect variations of two data variables plotted along the X‑axis and Y‑axis. Each marker’s position indicates the value of the item it represents. Scatter plots are ideal for large datasets when you wish to identify correlations based on how closely or widely distributed data points are.
- Visual Bubble Chart: Bubble charts function similarly to scatter plots, except data points are represented as bubbles. Bubbles add an extra dimension by encoding the relative size of individual items or categories besides their XY‑axis coordinates. Accordingly, bubble‑chart visualization demonstrates relationships among at least three metrics: two mapped to the X‑ and Y‑axes, and the third reflected by bubble size.
- Visual Heatmap: Heatmaps plot data points onto familiar layouts such as world maps or stadium diagrams. Values are expressed via different colors or varying intensities of a single color. They perform well for categories with wide value ranges, enabling quick observation of overall and specific trends, identification of weaknesses and discovery of opportunities.
- Visual Treemap: Treemaps deploy color‑coded rectangles nested and adjacent to one another. Rectangles are weighted to reflect their share of the total. Treemaps support value comparisons both across and within categories while retaining visibility of relative importance.
- Visual Polar Chart: Also known as radial summary charts, polar charts resemble pie charts. Instead of encoding share‑of‑total by segment angle, all sectors feature identical angles; values are represented by the radial distance extending from the circle’s center.
- Visual Funnel Chart: Funnel charts show declining values as customers advance through the sales funnel. Conversion rates for each stage can be clearly observed, helping you quickly pinpoint where drop‑offs occur.