AWS to GCP Data Engineering Bridge
Translate your existing knowledge of Amazon S3, Redshift, Athena, Kinesis, Glue, and AWS IAM directly into Google Cloud Storage, BigQuery, Pub/Sub, Cloud Composer, and Cloud IAM.
Data Engineering Translation Tracks
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Data Lake & Object Storage
From Amazon S3 bucket policies & multi-tier storage to Google Cloud Storage (GCS) uniform namespaces.
In AWS S3, security is governed by a combination of IAM policies, S3 Bucket Policies, and legacy ACLs, while cold storage (Glacier) requires waiting hours for restore jobs. In Google Cloud Storage, buckets share a single global namespace (`gs://`), security is simplified through Uniform Bucket-Level Access (pure Cloud IAM), and even cold Archive objects are accessible with sub-second latency without restore requests.
Data Warehousing & Serverless SQL
From Amazon Redshift cluster slices & Athena to Google BigQuery's serverless slot architecture.
In AWS, data warehousing often forces a choice between cluster maintenance (Amazon Redshift) and serverless ad-hoc queries (Amazon Athena). Google BigQuery combines the best of both worlds into a single platform: it is completely serverless with zero cluster management, yet delivers petabyte-scale sub-second analytical execution powered by Google's global Borg slot allocation and Colossus storage.
Real-Time Streaming & Messaging
From Amazon Kinesis fixed shard throughput & KCL leases to Google Cloud Pub/Sub auto-scaling topics.
In AWS Kinesis, streaming capacity is constrained by physical Shards (1 MB/s write each) that require proactive capacity planning, resharding scripts, and DynamoDB lease coordination. In Google Cloud Pub/Sub, shards do not exist: Topics and Subscriptions auto-scale dynamically across Google's global network, and message routing is decoupled from physical partition boundaries.
Pipeline Orchestration & ETL
From AWS Glue jobs, Step Functions & Amazon MWAA to Google Cloud Composer (managed Apache Airflow).
In AWS, teams often struggle with where orchestration lives: should it be in AWS Step Functions (JSON-based state machines), AWS Glue Workflows (limited triggers), or Amazon MWAA? In Google Cloud, **Cloud Composer (Apache Airflow)** is the universal orchestration hub: you write programmatic Python DAGs that orchestrate everything from raw GCS landing to BigQuery transformations, Spark jobs on Dataproc, and ML models in Vertex AI.
Security, Governance & IAM
From AWS IAM JSON policies & STS roles to Google Cloud's resource hierarchy & Service Accounts.
In AWS IAM, authorization is identity-centric: you craft detailed JSON policies specifying exact API actions (`s3:GetObject`, `redshift:ExecuteStatement`) and attach them to Users or Roles. In Google Cloud IAM, authorization is resource-hierarchy-centric: permissions are grouped into Roles (e.g. `roles/bigquery.dataViewer`) and bound to Principals at the Organization, Folder, Project, or Resource level. Machine authentication relies on Service Accounts and Workload Identity Federation rather than STS assume role.