Data pipeline patterns in GCP - Step-by-Step Execution

Process Flow - Data pipeline patterns

Data Source

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Ingest Data

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Process Data

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Store Data

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Analyze / Visualize

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End User / Application

Data flows step-by-step from source through ingestion, processing, storage, and finally to analysis or use.

Execution Sample

GCP

1. Read data from Cloud Storage
2. Process data with Dataflow
3. Store results in BigQuery
4. Visualize with Looker

This pipeline reads raw data, processes it, stores processed data, and then visualizes it.

Process Table

Step	Action	Service Used	Input	Output	Notes
1	Read raw data	Cloud Storage	Raw files	Data stream	Data ingestion starts
2	Process data	Dataflow	Data stream	Transformed data	Data cleaned and enriched
3	Store data	BigQuery	Transformed data	Stored tables	Data ready for queries
4	Visualize data	Looker	Stored tables	Reports/Dashboards	Users see insights
5	End	N/A	N/A	N/A	Pipeline complete

💡 Pipeline ends after data is visualized and available for users

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
Data	Raw files	Data stream	Transformed data	Stored tables	Reports/Dashboards

Key Moments - 2 Insights

Why do we need a processing step after ingestion?

Because raw data often needs cleaning or transforming before storage; see Step 2 in execution_table where Dataflow processes the data.

Can visualization happen before storing data?

No, visualization tools like Looker need structured data in storage like BigQuery; Step 3 stores data before Step 4 visualizes it.

Visual Quiz - 3 Questions

Look at the execution_table, what service processes the data after ingestion?

A Cloud Storage

B BigQuery

C Dataflow

D Looker

💡 Hint

Check Step 2 in execution_table under 'Service Used'

At which step is data stored in BigQuery?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look at the 'Store data' action in execution_table

If the processing step is skipped, what is likely to happen?

A Stored data may be raw and unstructured

B Visualization will work without data

C Data will be clean and ready

D Ingestion will fail

💡 Hint

Refer to Step 2 and Step 3 in execution_table about data transformation

Concept Snapshot

Data pipelines move data from source to analysis in steps:
1. Ingest raw data (Cloud Storage)
2. Process/transform data (Dataflow)
3. Store processed data (BigQuery)
4. Visualize data (Looker)
Each step prepares data for the next, ensuring clean, usable insights.

Full Transcript

A data pipeline in GCP starts by ingesting raw data from sources like Cloud Storage. Then, Dataflow processes this data by cleaning and transforming it. The processed data is stored in BigQuery for efficient querying. Finally, visualization tools like Looker use this stored data to create reports and dashboards for users. Each step depends on the previous one to prepare data properly for the next stage, ensuring reliable and insightful results.