Looker employs a multifaceted process to assemble charts, combining data, visualizations, and interactivity to present information clearly and concisely. Let's delve into the key steps involved:
Looker connects to various data sources, including relational databases, APIs, CSV files, and cloud services.
Raw data is transformed and prepared for analysis, including cleaning, filtering, aggregation, and creating calculated measures.
Data is organized into dimensional models, defining relationships between tables and fields.
The chart type is chosen based on the data type and the message intended to be conveyed. Common options include line charts, bar charts, pie charts, tables, and maps.
Relevant data fields are selected to be used for the chart's axes, series, and legends.
Colors, styles, labels, and other visual elements are customized to enhance the visual communication and aesthetics of the chart.
Filters are applied to segment data and focus on specific subsets, enabling more in-depth analysis.
Calculated measures are created to generate new indicators from existing data, expanding the possibilities for analysis.
Drill-down allows for exploring details at more granular levels, while drill-up allows for returning to an overview.
Chart elements can be selected to highlight specific data and reveal additional information.
Filters can be applied interactively by clicking on chart elements, enabling dynamic data exploration.
Actions and links can be configured to connect the chart to other visualizations or reports, facilitating navigation and insight discovery.
The chart layout is adjusted to optimize viewing and readability across different devices.
Colors and styles can be applied dynamically based on data values, highlighting patterns and outliers.
Charts can be easily shared with other users, enabling collaboration and discussion around insights.
In summary, Looker combines data visualization technology with powerful analysis capabilities to create informative and interactive charts that aid in decision-making.