Requirements of charts

Learn about the minimum requirements of different chart types in Tellius

Measures and dimensions required for each chart

The following are the essential requirements for building effective and insightful charts. Get the minimum number of measures and dimensions needed for each chart type.

Chart
Min. measures
Min. dimensions

KPI

1

0

Pie chart

1

1

Bar chart

1

1

Line chart

1

1

Scatter plot

2

1

Combo chart

1

1

Table

n

n

Detail table

n

n

Pivot table

n

n

Histogram

1

0

Heatmap

1

2

Treemap

1

1

Sankey chart

1

2

Bubble chart

1

1

Boundary map

0

1 (geo dimension)

Location map

0

2 (latitude & longitude)

Waterfall chart

1

1

KPI target chart

1

0

Growth chart

1

1

Funnel chart

1

1

Bar conversion

n

0

Confidence range

3

1

YoY chart

1

1 (date)

Cohort chart

1

2

Data point limits for each chart

In this section, we'll outline the default number of data points allowed for various chart types. Understanding these limits is crucial for creating effective and visually appealing charts.

Type of chart(s)
Default limit of data points

  • Line chart

  • Area chart

  • Growth/Trend chart

  • Combined bar chart

  • Heatmap

  • Confidence range chart

  • Scatter chart

10,000

Bubble chart

5000

Treemap, Bar chart

1000

Location map, Boundary map

1000

Pivot table, Cohort chart

500

Waterfall chart

100

Pie/Donut chart, Funnel chart, Tables

50

Optimizing your chart data

Understanding the default data point limits for different chart types is essential for effective data visualization. However, there may be instances where you need to work with larger datasets. In such cases, consider the following options for better data representation:

  1. Alternative chart types: Explore alternative chart types that are better suited for larger datasets. For example, if you're dealing with extensive data, a scatter plot or a bar chart might be more appropriate than a pie chart.

  2. Data optimization: Before creating your chart, consider optimizing your dataset. This can involve summarizing or aggregating your data to reduce the number of data points while retaining key insights.

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