You don't need to learn all three. Pick one, go deep, and understand that the others exist as "the same concepts, different names."

The big three, mapped

Concept AWS GCP Azure
Object storage S3 Cloud Storage ADLS
Serverless SQL over files Athena BigQuery Synapse
Managed orchestration MWAA (managed Airflow) Composer Data Factory
Data catalogue Glue Data Catalog Purview
Notebooks SageMaker Colab / Vertex AI Azure ML Notebooks

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Recommendation: if you're learning from scratch, start with GCP. BigQuery's free sandbox and Colab give you a genuinely free path to real practice without a credit card. AWS is the most commonly required in job postings, so circle back to it once you're comfortable with the concepts.

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The free-tier reality check (read this before you start)

Service Actually free?
AWS S3 Yes: 5GB always-free storage
AWS Glue Yes: the first 1 million catalogue requests a month
AWS Athena No. There's no free query tier at all. Every query costs at least $0.00005 (a 10MB minimum billed), even for a trivial test. New accounts (post-July 2025) get up to $200 in credits that can offset this while learning.
GCP BigQuery sandbox Yes: a genuinely free tier with generous limits, no credit card required
Google Colab Yes: a free tier with usable compute

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The Athena catch trips people up constantly. A "free tier" doesn't mean every service within it is free, so check the specific service rather than assuming the account tier covers it.

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What to actually do

  1. Pick GCP or AWS based on the recommendation above
  2. Load a small public dataset into free storage (S3 or Cloud Storage)
  3. Query it with the free-tier-friendly option (BigQuery sandbox, or Athena if you keep the query-cost catch above in mind)
  4. Once comfortable, learn the equivalent service names on the other two clouds using the mapping table. You're translating concepts, not starting over