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GCPcloud~10 mins

Looker for visualization in GCP - Step-by-Step Execution

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Process Flow - Looker for visualization
Connect to Data Source
Create LookML Model
Define Explores & Views
Build Visualizations
Save & Share Dashboards
Users Interact & Explore Data
Looker connects to data, models it with LookML, builds visualizations, and shares dashboards for users to explore.
Execution Sample
GCP
1. Connect Looker to BigQuery dataset
2. Define LookML model with views
3. Create an Explore for analysis
4. Build charts and tables
5. Save dashboard and share
This sequence shows how Looker connects data, models it, creates visualizations, and shares dashboards.
Process Table
StepActionInput/ConfigResult/OutputNotes
1Connect to Data SourceBigQuery project and dataset infoConnection establishedLooker can query data live
2Create LookML ModelDefine views and explores in LookMLModel ready for analysisModels describe data structure
3Build VisualizationSelect fields and chart typeChart/table renderedVisualizes data from model
4Save DashboardAdd visualizations to dashboardDashboard savedDashboard groups visuals
5Share DashboardSet permissions and share linkUsers can view and exploreEnables collaboration
6User InteractionUsers filter and drill downData updates dynamicallyExploration without coding
7ExitNo further actionsSession endsUser finishes data exploration
💡 User finishes exploring data and closes Looker session
Status Tracker
VariableStartAfter Step 1After Step 2After Step 3After Step 4After Step 5After Step 6Final
Connection StatusNot connectedConnectedConnectedConnectedConnectedConnectedConnectedConnected
LookML ModelNoneNoneDefinedDefinedDefinedDefinedDefinedDefined
VisualizationNoneNoneNoneCreatedCreatedCreatedUpdated by userFinal visual state
DashboardNoneNoneNoneSavedSavedSharedSharedShared
User InteractionNoneNoneNoneNoneNoneNoneActiveEnded
Key Moments - 3 Insights
Why do we need to create a LookML model before building visualizations?
LookML models define the data structure and relationships so Looker knows how to query and display data correctly, as shown in step 2 of the execution_table.
Can users explore data without writing SQL in Looker?
Yes, users interact with visualizations and filters dynamically without coding, as shown in step 6 where user interaction updates data views.
What happens if the data source connection is lost?
Looker cannot query data, so visualizations won't update. Connection status in variable_tracker shows 'Connected' only after step 1.
Visual Quiz - 3 Questions
Test your understanding
Look at the execution_table at step 3, what is the output?
AChart or table rendered
BLookML model is defined
CConnection to data source established
DDashboard saved
💡 Hint
Refer to the 'Result/Output' column in row for step 3 in execution_table
According to variable_tracker, when does the 'Dashboard' variable change from 'None' to 'Saved'?
AAfter Step 2
BAfter Step 5
CAfter Step 4
DAfter Step 6
💡 Hint
Check the 'Dashboard' row in variable_tracker and see the value after each step
If the connection to the data source fails at step 1, what will happen to the 'Connection Status' variable?
AIt will show 'Connected'
BIt will remain 'Not connected'
CIt will change to 'Disconnected' after step 3
DIt will show 'Active'
💡 Hint
Look at the 'Connection Status' row in variable_tracker and what it shows at start and after step 1
Concept Snapshot
Looker connects to data sources like BigQuery.
You define data models with LookML.
Build visualizations by selecting fields and chart types.
Save and share dashboards for collaboration.
Users explore data interactively without coding.
Full Transcript
Looker is a tool that connects to data sources such as BigQuery. First, you establish a connection to your data. Then, you create a LookML model that describes your data structure and relationships. After that, you build visualizations like charts and tables by selecting fields and chart types. These visualizations are saved into dashboards, which you can share with others. Users can then interact with these dashboards by filtering and drilling down into data without needing to write any code. The process ends when the user finishes exploring the data and closes the session.