Project Overview

This project demonstrates a multicloud data pipeline that ingests data into AWS S3, processes it in GCP BigQuery, and stores final analytics in Azure Synapse Analytics.

Architecture

• AWS S3: Raw data storage, CSV or JSON files
• GCP BigQuery: Data processing, transformations, aggregation
• Azure Synapse: Centralized analytics warehouse for reporting
• ETL Automation: Using Python scripts or Apache Airflow

Step 1: Prepare AWS S3 Bucket

aws s3 mb s3://multicloud-raw-data
aws s3 cp local-data.csv s3://multicloud-raw-data/

Step 2: Set up GCP BigQuery Dataset

gcloud datasets create multicloud_dataset
bq mk -t multicloud_dataset.raw_data raw_data_schema.json
bq load --source_format=CSV multicloud_dataset.raw_data gs://bucket-name/local-data.csv

Step 3: Transform Data in BigQuery

-- Example SQL Transformation
CREATE TABLE multicloud_dataset.processed_data AS
SELECT id, name, COUNT(*) AS event_count
FROM multicloud_dataset.raw_data
GROUP BY id, name;

Step 4: Export Data to Azure Blob Storage

-- Export BigQuery processed data to CSV
bq extract --destination_format CSV multicloud_dataset.processed_data gs://gcp-export-bucket/processed_data.csv

-- Then upload to Azure Blob
az storage blob upload --container-name analytics --file processed_data.csv --name processed_data.csv

Step 5: Load Data into Azure Synapse

-- Using Synapse COPY command
COPY INTO analytics_table
FROM 'https://.blob.core.windows.net/analytics/processed_data.csv'
WITH (FILE_TYPE = 'CSV');

Step 6: Automate the Pipeline

• Use **Apache Airflow** to schedule ETL tasks:

from airflow import DAG
from airflow.providers.amazon.aws.operators.s3 import S3CreateBucketOperator
from airflow.providers.google.cloud.operators.bigquery import BigQueryInsertJobOperator
from airflow.providers.microsoft.azure.operators.synapse import SynapseCopyOperator
from datetime import datetime

with DAG('multicloud_pipeline', start_date=datetime(2025, 1, 1), schedule_interval='@daily') as dag:
    create_bucket = S3CreateBucketOperator(bucket_name='multicloud-raw-data')
    bigquery_job = BigQueryInsertJobOperator(configuration={...})
    synapse_load = SynapseCopyOperator(task_id='load_to_synapse', ...)  

    create_bucket >> bigquery_job >> synapse_load

• Automate daily ingestion and transformations

Benefits of Multicloud Approach

• Avoid vendor lock-in
• Leverage best-of-breed services from each provider
• Global redundancy and failover
• Flexible scaling depending on workload