Data Pipeline

Let’s break this down into a clear project plan and learning roadmap you can follow step-by-step, using the GCP Free Tier and open-source tools.

✅ ​Project Theme: Unified Data Pipeline — Real-Time + Batch Processing with Metrics/KPIs

🎯 ​Objective:

Build an end-to-end data platform that:

• Ingests real-time data (e.g., user activity, sensor data, etc.) via ​GCP Pub/Sub.
• Processes batch data using Apache Spark (e.g., stored logs, CSVs, historical data).
• Orchestrates workflows using Apache Airflow on Cloud Composer or local.
• Calculates KPIs and stores them in a warehouse (e.g., BigQuery or ​Cloud Storage) for analytics/dashboards.

🧠 Learning Goals

• GCP Cloud Fundamentals (IAM, Billing, Networking basics)
• Pub/Sub for real-time streaming
• Dataflow or Spark for ETL
• Airflow for orchestration
• BigQuery for analytics
• KPI Metrics (e.g., Daily Active Users, Average Session Duration, etc.)

🗂️ Project Architecture (Combined Pipeline)

                    +---------------------+
                    |   Real-Time Events  |
                    |  (JSON via API or   |
                    |   simulation script)|
                    +----------+----------+
                               |
                          [1] Pub/Sub
                               |
                  +------------+-------------+
                  |                          |
        [2A] Dataflow (Optional)      [2B] Log Raw Data
        or Streaming Job             to Cloud Storage
             (real-time)                 (parquet/JSON)
                  |                          |
         [3] Write to BigQuery        [4] Batch Data Files
                               +-------------+
                               |             |
                   +-----------v---------+   |
                   |    Apache Airflow   |---+
                   |     (DAGs)          |
                   +-----------+---------+
                               |
                  [5] Spark Batch Jobs (Dataproc or local)
                               |
                        [6] KPI Calculations
                               |
                       Write to BigQuery Table
                               |
                      BI Layer (e.g., Looker Studio)

🧩 Component Breakdown

🔴 Real-Time Pipeline

• ​Simulate data: Python script to send data to Pub/Sub (e.g., user clicks, IoT data).
• ​GCP Pub/Sub: Acts as message broker.
• Optional: Dataflow (or you can use a Python subscriber): Process and clean streaming data.
• Write to BigQuery or Cloud Storage.

🟡 Batch Pipeline

• ​Source: CSV or Parquet files (e.g., daily logs, transaction history).
• ​Apache Spark (on Dataproc or locally) : Clean, transform, and enrich data.
• ​Write to Cloud Storage or BigQuery.

🔵 Workflow Orchestration

• Apache Airflow (in Cloud Composer or local):

  • Schedule batch jobs.
  • Handle retries and dependencies.
  • Trigger KPI calculation jobs.

🟢 KPI Metrics Calculation

Examples:

• Total Users per Day
• Session Duration
• Conversion Rate
• Events per Minute (for real-time)
• Average Load Time, etc.

Calculated using:

• SQL in BigQuery
• PySpark
• Pandas (if small-scale)

🛠️ Tools & Stack

📅 Suggested Learning/Project Timeline

📌 Tips for Execution

• ​Use sample datasets: Simulate realistic e-commerce, IoT, or user activity data.
• ​Start small: Get one part working (e.g., just Pub/Sub → BigQuery), then expand.
• ​Log everything: For debugging and tracking progress.
• ​Keep the code organized: Use folders like /scripts, /dags, /spark_jobs, /sql.
• ​Write a README: Explain architecture, how to run it, and technologies used.

🧾 Deliverables (for Resume or GitHub)

• ​README.md with project overview, architecture diagram, tech stack
• Screenshots: GCP Console, BigQuery tables, Looker dashboards
• Code repo: Airflow DAGs, Spark jobs, Pub/Sub scripts
• KPIs calculated and stored in a BigQuery table

✅ Final Verdict

👉 ​YES — it's 100% okay to start with GCP Free Tier, and this project idea combining Pub/Sub (streaming) + Spark + Airflow (batch)