Using Cloud Storage to Archive High-Volume Streaming Data

 🔹 Key Components of the Architecture

1. Data Ingestion Layer

You need a reliable method to collect and stream data into the cloud.


Services:


AWS: Kinesis Data Streams / Firehose


Azure: Event Hubs


Google Cloud: Pub/Sub


These services are designed for high-throughput, low-latency data ingestion.


2. Processing Layer (Optional)

Before archiving, you might want to process or transform the data.


Streaming processors:


Apache Flink, Apache Beam, or Spark Streaming


Managed services: AWS Lambda (with Firehose), Google Dataflow, Azure Stream Analytics


This step is useful for filtering, enrichment, or formatting data before storage.


3. Storage/Archiving Layer

Use object storage designed for durability and cost efficiency.


AWS: Amazon S3 (Standard, Infrequent Access, Glacier)


Azure: Blob Storage (Hot, Cool, Archive tiers)


Google Cloud: Cloud Storage (Standard, Nearline, Coldline, Archive)


You can set lifecycle policies to automatically transition data from hot storage to cheaper cold/archive tiers over time.


🔹 Best Practices

Use compression (e.g., gzip, Parquet) to reduce storage cost.


Partition data by time, region, or source for easier querying and management.


Implement lifecycle rules to move older data to cheaper storage tiers.


Secure your data with encryption at rest and in transit.


Monitor and alert using built-in cloud tools (e.g., AWS CloudWatch, Azure Monitor).


🔹 Example: AWS-Based Pipeline

Ingest: Use Kinesis Firehose to collect IoT sensor data.


Buffer/Transform: Firehose buffers and optionally transforms data.


Archive: Delivers compressed files to Amazon S3.


Lifecycle Rule: After 30 days, data is moved to S3 Glacier Deep Archive.


🔹 When to Use Cloud Storage for Streaming Data Archival

When data volume exceeds traditional on-prem storage capacity.


When long-term storage is needed for regulatory compliance.


When you want cost-effective, durable, and scalable storage.


When you might later run batch analytics or ML on archived data.

Learn Google Cloud Data Engineering Course

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Cloud Storage - Specialized Use Cases & Security

Building a Pub/Sub Message Router with Cloud Run

Creating a Multi-Tenant Event Bus with Cloud Pub/Sub

Using Pub/Sub as an Audit Trail for Regulatory Compliance

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