Splunk: Pivot charts and visualizations with Pivot Editor

Splunk’s Pivot Editor is a powerful tool that allows you to create charts and visualizations from your data. The Pivot Editor allows you to pivot your data and create tables, charts, and other visualizations that can help you better understand your data and make informed decisions.

To create a chart or visualization with the Pivot Editor, follow these steps:

1. Open the Pivot Editor by clicking on “Pivot” in the Splunk navigation bar.
2. Select the data you want to analyze by entering your search query in the “Search” box.
3. Click on the “Data Model” tab to see the available data fields.
4. Drag and drop the fields you want to analyze into the “Rows” and “Columns” boxes.
5. Click on the “Visualizations” tab to select the type of chart or visualization you want to create.
6. Choose the chart type that best represents your data. Some of the options include column charts, pie charts, and line charts.
7. Customize your chart by adjusting the settings in the “Chart Settings” tab. You can change the chart title, labels, colors, and other attributes.
8. Preview your chart by clicking on the “Preview” button.
9. Save your chart by clicking on the “Save” button.

The Pivot Editor also allows you to create dashboards that can display multiple charts and visualizations at once. You can share these dashboards with others in your organization, allowing everyone to stay up-to-date on the latest data trends and insights.

Overall, the Pivot Editor is a powerful tool that can help you gain valuable insights from your data. Whether you’re creating charts, tables, or dashboards, the Pivot Editor makes it easy to visualize your data and make informed decisions.

On switching between Pivot visualization types:

In Splunk’s Pivot Editor, you can easily switch between different visualization types to help you better analyze and understand your data. Here are some tips for switching between Pivot visualization types:

1. Select the chart type that best represents your data: When you create a new Pivot visualization, you’ll be asked to choose the chart type that best suits your data. It’s important to choose a chart type that effectively communicates the information you’re trying to convey. For example, if you’re analyzing sales data over time, a line chart may be the best option. If you’re comparing data between different categories, a bar chart or pie chart may be more appropriate.
2. Experiment with different visualization types: If you’re not sure which chart type to choose, or if you want to explore different options, don’t be afraid to experiment with different visualization types. Splunk’s Pivot Editor offers a wide variety of chart types, including column charts, bar charts, pie charts, line charts, scatter plots, and more. Try out different options to see which one works best for your data.
3. Use the “Switch to” feature: If you’ve already created a visualization and want to switch to a different chart type, you can use the “Switch to” feature in the “Visualizations” tab. This allows you to easily switch between different visualization types without having to recreate your chart from scratch.
4. Customize your visualization: Once you’ve selected a visualization type, you can customize it to better suit your needs. For example, you can change the colors, labels, axis titles, and other settings to make your visualization more informative and visually appealing.

Overall, switching between Pivot visualization types is easy and can help you better analyze and understand your data. By experimenting with different chart types and customizing your visualizations, you can create powerful and informative charts that help you make data-driven decisions.

Controls that are common to all charts and single value visualizations:

There are several controls that are commonly used in all charts and single value visualizations, including:

1. Legend: A legend is used to identify the meaning of each color or pattern used in the chart or visualization. This control helps users understand the information presented in the chart.
2. Title: A title gives context to the chart or visualization and provides a brief description of what is being presented.
3. Axis labels: Axis labels provide information about the scale and units used in the chart or visualization. This control helps users understand the data presented and interpret the results.
4. Tooltips: Tooltips provide additional information about specific data points or values when a user hovers over them with their mouse. This control helps users to gain more insights from the chart or visualization.
5. Zooming and panning: Zooming and panning are useful controls for examining data in more detail. They allow users to focus on specific areas of the chart or visualization and explore the data in a more granular way.
6. Data filtering: Data filtering allows users to remove or hide certain data points or values from the chart or visualization. This control can help users to focus on specific parts of the data that are most relevant to their needs.
7. Exporting: Exporting allows users to download the chart or visualization as an image or a file. This control enables users to share the data with others or use it in other applications.

Area and line chart controls:

Area and line charts are two types of charts commonly used to display time-series data. The controls that are specific to these charts include:

1. Data points: Data points represent individual data values in a chart. In a line chart, data points are connected by lines, while in an area chart, they are represented as an enclosed shape.
2. Line thickness: In a line chart, the thickness of the line can be adjusted to make the chart easier to read.
3. Line color: Line color can be used to highlight specific data points or values in the chart.
4. Area fill: In an area chart, the area under the line can be filled with a color or pattern. This can help to distinguish between different data series or highlight important trends.
5. Interpolation: Interpolation controls how the chart connects data points. Options include linear interpolation (straight lines between points), spline interpolation (curved lines), or stepped interpolation (vertical and horizontal lines).
6. Time interval: The time interval controls how frequently data is displayed on the x-axis. This can be adjusted to show data points at different levels of granularity (e.g. by hour, by day, by month).
7. Y-axis range: The y-axis range determines the range of values displayed on the y-axis. This can be adjusted to focus on specific ranges of values or to highlight changes in a particular range.
8. Smoothing: Smoothing is a technique used to reduce noise in the data by averaging nearby data points. This can make trends in the data easier to see.
9. Annotations: Annotations are text or other visual elements that can be added to the chart to provide additional context or highlight specific data points or trends.
10. Trend lines: Trend lines are lines that are added to the chart to represent the overall trend in the data. These can help users to see patterns in the data over time.

Scatter chart controls:

Scatter charts are used to display the relationship between two or more variables. The controls that are specific to scatter charts include:

1. Data points: Data points represent individual data values in a chart. In a scatter chart, each data point is plotted as a point on a Cartesian coordinate system.
2. X-axis and Y-axis: The x-axis and y-axis are used to represent the two variables being compared in the scatter chart. These axes can be labeled with the variable names and units.
3. Axis range: The range of the x-axis and y-axis can be adjusted to focus on specific ranges of values or to highlight changes in a particular range.
4. Data labels: Data labels can be added to the data points to identify each data point and make it easier to interpret the chart.
5. Bubble size: In a bubble chart, the size of each bubble can be adjusted to represent the value of a third variable. This can add an extra dimension to the chart.
6. Trend lines: Trend lines can be added to the scatter chart to represent the overall trend in the data. These can help users to see patterns in the data and make predictions.
7. Color-coded data points: Data points can be color-coded to represent different categories or values of a third variable. This can help users to see patterns in the data and make comparisons between different groups.
8. Data filtering: Data filtering allows users to remove or hide certain data points from the chart. This control can help users to focus on specific parts of the data that are most relevant to their needs.
9. Zooming and panning: Zooming and panning are useful controls for examining data in more detail. They allow users to focus on specific areas of the chart and explore the data in a more granular way.
10. Annotations: Annotations are text or other visual elements that can be added to the chart to provide additional context or highlight specific data points or trends.

Single value visualization controls:

Single value visualizations are used to display a single value or metric. The controls that are commonly used in single value visualizations include:

1. Title: A title gives context to the visualization and provides a brief description of what is being presented.
2. Value display: The value being displayed can be presented in different ways, such as a number, a percentage, or a bar.
3. Color-coded values: Values can be color-coded to represent different categories or thresholds. This can help users to quickly identify whether a value is within an acceptable range or not.
4. Comparison values: Comparison values can be added to the visualization to provide context and help users to make comparisons. For example, a goal value or a previous value can be displayed alongside the current value.
5. Trend indicators: Trend indicators, such as arrows or icons, can be used to show whether the value is increasing, decreasing, or staying the same over time.
6. Units: Units can be displayed alongside the value to provide context and help users to understand the scale of the metric.
7. Time frame: If the metric is time-based, the time frame can be displayed alongside the value to provide context and help users to understand the period being measured.
8. Tooltips: Tooltips can provide additional information about the metric or value being displayed when a user hovers over the visualization with their mouse.
9. Alerts: Alerts can be added to the visualization to notify users when the value is outside of an acceptable range. This can help users to take action and address issues quickly.
10. Exporting: Exporting allows users to download the visualization as an image or a file. This control enables users to share the data with others or use it in other applications.

Gauge visualization controls: