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PROJECT


Build ETL Pipeline in GCP: Load GCS Data to BigQuery via Dataflow

In this project, we’ll learn to create an extract, transform, load (ETL) pipeline using Google Cloud Platform (GCP) services. We’ll extract the data from GCP storage buckets, clean and transform it using Apache Beam and Dataflow, and finally load it to BigQuery.

Build ETL Pipeline in GCP: Load GCS Data to BigQuery via Dataflow

You will learn to:

Build and implement an ETL pipeline using various GCP tools.

Use data validation, cleaning, transformation, and aggregation techniques using Apache Beam (Python).

Create a Google Cloud Services bucket, BigQuery dataset, table, and view.

Use a GCP service account for proper pipeline access control.

Monitor activities for running Dataflow ETL pipeline.

Execute various GCP command-line interface (CLI) commands.

Skills

Data Pipeline Engineering

Data Extraction

Data Analysis

Data Manipulation

Data Cleaning

Prerequisites

Good understanding of Python

Good understanding of BigQuery

Good understanding of Apache Beam

Good understanding of Google Cloud services

Technologies

GCP logo

GCP

Python

BigQuery logo

BigQuery

Apache Beam logo

Apache Beam

Project Description

In this project, we’ll design a Google Cloud Dataflow ETL pipeline to extract raw Airbnb data from a cloud storage bucket. Then we’ll load it into BigQuery tables and views for analytical purposes after performing various data cleaning and transformation tasks.

Technically, the pipeline will be written using Apache Beam’s Python software development kit (SDK) using the following steps:

  1. Ingest data from a Google Cloud Storage bucket.

  2. Validate the ingested data with various integrity checks for values that are missing, null, or outside the prescribed range.

  3. Clean the data to remove unnecessary columns, special characters and symbols, and duplications.

  4. Transform and enrich the data using various techniques like filtering, grouping, and aggregation.

  5. Load the transformed data to BigQuery tables and views.

Appropriate access control and security mechanisms will be implemented to run the ETL pipeline using GCP identity and access management (IAM) authorization and authentication techniques. Following a real-time approach, we’ll create and run the pipeline through a service account, not a personal user account.

During pipeline execution, we’ll monitor it using Dataflow monitoring tools. Once the pipeline finishes, certain validation and testing steps will be carried out to validate the pipeline’s output in BiQuery. This project focuses on the fundamental ETL process of extracting data from one source and loading it into another, demonstrating how to ETL unaltered data for analysis in Google Cloud.

Project Tasks

1

Introduction

Task 0: Get Started

Task 1: Set Up and Configure GCP Project

2

Upload Data to Google Cloud Storage and Create Service Account

Task 2: Create Cloud Storage Bucket

Task 3: Upload Data to Storage Bucket

Task 4: Create Service Account and Assign Roles

3

Build the ETL Pipeline

Task 5: Getting Started with Apache Beam

Task 6: Create a Pipeline Object with Options

Task 7: Read Data from GCS Bucket in the Pipeline

Task 8: Parse the Input Data

Task 9: Apply Data Validation Steps in the Pipeline

Task 10: Apply Data Cleaning Steps in the Pipeline

Task 11: Enrich the Data

Task 12: Perform Aggregation Operations

4

Write Data to BigQuery

Task 13: Create BigQuery Dataset

Task 14: Upload Final Data to BigQuery Table

Task 15: Execute the Pipeline

Task 16: Create Views

Task 17: Validate the Results

5

Run Complete Python Code using Dataflow Runner

Task 18: Complete Python Code with Dataflow Runner

Task 19: Monitor the Pipeline Execution

Task 20: Validate the Pipeline Results

Task 21: Clean Up

Congratulations!