Cloud for Data Engineers in India: AWS vs Azure vs GCP, the Services Each One Uses, and Which to Learn First

Which cloud services do data engineers use on AWS, Azure and GCP?

Each cloud offers the same jobs under different names, so learn the job first and the product second: interviewers ask how data moves from a source to a table people query, and the answer has the same shape everywhere. The table maps each job to its service from each vendor's documentation, checked on 15 October 2026. Google has renamed two of them: Dataproc is now Managed Service for Apache Spark, and Cloud Composer is now Managed Airflow.

Which cloud should you learn first in India?

Start with the cloud your target employers name, because that is what a recruiter screens for. With no target yet, the India Tech Hiring Report gives the order: across 1,470 data-engineer postings, Azure was named in 35.7%, AWS in 29.0% and Google Cloud (GCP) in 16.1%, and a posting can name more than one. Databricks, named in 19.1%, documents its platform for all three clouds, and Snowflake, named in 16.5%, hosts accounts on all three, so either one moves with you.

How does a data engineer with AWS build a first pipeline?

A first AWS pipeline lands raw files in S3, transforms them with AWS Glue, and serves them through Athena or Redshift, with Airflow on MWAA running the schedule. AWS describes Glue as a fully managed ETL service whose engines run Spark, PySpark and Python, and Athena as serverless SQL over S3, so there is no cluster to run while you learn. PrepNPlaced's Data Engineering cohort spends the 24 live hours of its Module 5 on this stack, with Kinesis and Lambda added.

1. 1Create an S3 bucket with raw and curated prefixes, and land a public dataset in raw.
2. 2Run a Glue crawler so the Glue Data Catalog holds the table and its schema.
3. 3Write a Glue PySpark job that cleans the data and writes Parquet to curated, partitioned by date.
4. 4Query the curated table in Athena, and compare the data scanned with and without a date filter.
5. 5Add data quality checks, then schedule every step with an Airflow DAG on MWAA, each safe to rerun.
6. 6Write a README with the diagram, what you watched on the bill and one failure you fixed.

How does the Azure data stack fit together?

On Azure, data lands in Azure Data Lake Storage, is moved and scheduled by Azure Data Factory, is transformed in Azure Databricks or Synapse Spark, and is served from Synapse SQL or Power BI. Microsoft documents Data Lake Storage as Blob Storage with the hierarchical namespace switched on. For new projects, Microsoft's Azure Data Factory documentation calls Data Factory in Fabric its next generation and tells newcomers to start there, and Fabric's OneLake is itself built on Azure Data Lake Storage.

What is different about Google Cloud for data engineers?

Google Cloud puts BigQuery at the centre: Google describes it as fully managed and serverless, with storage and compute that scale independently, so there is no cluster to size. Partition tables first, because BigQuery scans only the partitions a filter matches. Events arrive through Pub/Sub, an asynchronous messaging service, and Dataflow runs Apache Beam pipelines with one model for batch and streaming. Google's Professional Data Engineer exam page says the exam will soon be updated for recent branding changes.

Which cloud skills carry across AWS, Azure and GCP?

SQL, Python, Spark, file formats, partitioning, orchestration, access control and cost carry across all three, which is why the report's top two data-engineer skills are not clouds: SQL at 48.6% and Python at 42.7% of 1,470 postings. To test yourself, redraw your own pipeline on a cloud you have not used, naming the service for each role.