GCP vs AWS vs Azure for Data Engineering

An engineer who has built a pipeline on one cloud can read an architecture diagram from another and recognise almost everything except the labels. Files land in object storage, a service moves and transforms them, a warehouse serves SQL, something schedules the work, and something else governs access. GCP vs AWS vs Azure is therefore less a contest than a translation exercise, with a few real differences in how each platform is assembled. This comparison covers both.

The same pipeline, three vocabularies

Google and Microsoft each publish a table mapping their services to the other clouds. The map below is drawn from those tables (Google Cloud: compare AWS and Azure services; Microsoft: compare AWS and Azure analytics services).

Two of the Google names are recent. Managed Service for Apache Spark was Dataproc (Google Cloud: Managed Service for Apache Spark), and Managed Service for Apache Airflow was Cloud Composer (Google Cloud: Managed Airflow overview). Microsoft's table still lists both under the earlier names.

Where the vendors' own maps disagree

Treat the table as a starting point. Both vendors say so: Microsoft's page warns that matched services might not have identical features. And the two maps do not always agree with each other.

• BigQuery. Google pairs it with Azure Synapse Analytics. Microsoft pairs it with Fabric Data Warehouse and Azure Databricks (Microsoft: Google Cloud to Azure services comparison).
• Dataflow. Google pairs it with Azure Stream Analytics. Microsoft pairs it with Azure Databricks and HDInsight.

Neither is wrong. BigQuery is a warehouse, a query service over files and a streaming destination at once, so it has no single counterpart. A mapping tells you where to start reading, not what will behave identically.

How each platform is shaped

The larger difference is how much each platform bundles.

Google Cloud is built around one warehouse. BigQuery is serverless: there is no cluster to size, and storage and compute are separate layers that scale independently (Google Cloud: BigQuery overview). Work that needs a separate service elsewhere, such as querying files in storage, receiving streamed rows or training a simple model, is often done inside it. The stack has fewer pieces, and the first design question is usually how to use BigQuery well.

AWS is a set of parts. Microsoft's comparison describes it accurately: AWS provides individual analytics services that you compose into a solution. S3 holds the lake, Glue catalogues and transforms, Athena queries files, Redshift is the warehouse, EMR runs Spark. The freedom is real, and so is the number of decisions.

Azure offers both an integrated platform and standalone services. Microsoft Fabric bundles data integration, engineering, warehousing and Power BI over one storage layer called OneLake, while Azure Data Factory, Azure Databricks, Event Hubs and Stream Analytics remain available as standalone services. Microsoft's own mapping now names a Fabric workload first in several rows, which suggests where its emphasis lies.

One load, written three ways

Loading files from object storage into a warehouse table is the most common task on any of them. Here it is on each. The statements follow each vendor's documented syntax; names are placeholders.

BigQuery (Google Cloud: LOAD DATA statement):

LOAD DATA INTO sales.orders
FROM FILES (
  format = 'CSV',
  skip_leading_rows = 1,
  uris = ['gs://example-landing/orders/2026-10-01/*.csv']
);

Amazon Redshift (AWS: COPY from Amazon S3):

COPY sales.orders
FROM 's3://example-landing/orders/2026-10-01/'
IAM_ROLE 'arn:aws:iam::123456789012:role/RedshiftLoadRole'
FORMAT AS CSV
IGNOREHEADER 1;

Azure Synapse Analytics (Microsoft: COPY INTO):

COPY INTO sales.orders
FROM 'https://exampleaccount.blob.core.windows.net/landing/orders/2026-10-01/*.csv'
WITH (
  FILE_TYPE = 'CSV',
  FIRSTROW = 2,
  CREDENTIAL = (IDENTITY = 'Managed Identity')
);

What is the same matters more than what differs. Each names a table, a location in object storage and a file format, and each skips a header row. Someone who understands one can read the others at sight.

The differences are instructive:

• Authorisation is stated differently. Redshift names an IAM role inside the statement. Synapse names a credential, here a managed identity. BigQuery names nothing: the person or service account submitting the job needs permission to read the objects as well as to load the table (Google Cloud: load CSV data from Cloud Storage).
• Table creation differs. Redshift requires the table to exist. BigQuery's statement can create it, and leaves the table unchanged if the load fails.
• The dialects differ in small ways. One skips a count of rows, another names the first row to read. These are the details to look up, not to memorise.

Accounts, hierarchy and access

Each cloud organises resources in a tree, with access granted high up flowing down.

Google Cloud's hierarchy runs from the organization through optional folders to projects, and roles granted high up are inherited by everything below (Google Cloud: resource hierarchy). Microsoft's comparison describes the Azure subscription as the equivalent of an AWS account, and notes that Azure adds resource groups as a required unit beneath it (Microsoft: compare AWS and Azure accounts).

The last row is the one that matters daily. A pipeline should authenticate with an identity that has no stored password: see service accounts on Google Cloud, IAM on AWS and managed identities on Azure.

What transfers and what does not

Transfers almost completely:

• SQL, data modelling, partitioning and incremental loading.
• Spark. PySpark written for one managed service needs little change on another, mostly paths and connectors.
• Airflow. Each cloud offers the open source project as a managed service, and a DAG is a DAG.
• The architecture itself: landing, staging, serving, and the reasons for each.

Has to be relearned:

• The access model. Roles, policies and inheritance differ in detail on each platform, and mistakes here are security incidents.
• Pricing. Paying for bytes scanned, for a running cluster or for reserved capacity changes how you design.
• Limits, quotas and networking.
• The console and the command line.

A fair estimate of the work: the concepts are learned once, and each additional cloud costs a fraction of the first.

Choosing one to learn first

There is no technically correct answer, only practical ones.

1. Follow the job. If your employer, or the employers you are applying to, use one cloud, learn that one. Read the job descriptions before choosing.
2. Follow your existing stack. A team on SQL Server and Power BI will find Azure familiar. A team already on Google Workspace has identity in place for Google Cloud.
3. If nothing else decides, pick the one you can practise on. BigQuery has a sandbox that needs no payment card, which removes the first obstacle for a beginner.
4. Then go deep, not wide. One cloud learned to the point of running a pipeline in production is worth more than three learned to the point of a diagram.

Roadmaps for each: Google Cloud, AWS and Azure Data Factory. For the warehouses alone, see BigQuery vs Snowflake vs Redshift.

The Data Engineering course covers Azure, AWS and GCP alongside SQL, Spark and Airflow.

Sources

• Compare AWS and Azure services to Google Cloud (Google Cloud Documentation) — Google Cloud
• Google Cloud to Azure services comparison (Microsoft Learn) — Microsoft Learn
• Compare AWS and Azure analytics services (Microsoft Learn) — Microsoft Learn
• Compare AWS and Azure accounts (Microsoft Learn) — Microsoft Learn
• About resource hierarchy (Google Cloud Documentation) — Google Cloud
• BigQuery overview (Google Cloud Documentation) — Google Cloud
• Load statements in GoogleSQL (Google Cloud Documentation) — Google Cloud
• Using the COPY command to load from Amazon S3 (Amazon Redshift Database Developer Guide) — AWS Documentation
• COPY INTO (Transact-SQL) for Azure Synapse Analytics (Microsoft Learn) — Microsoft Learn
• Managed Service for Apache Spark on clusters overview (Google Cloud Documentation) — Google Cloud
• Managed Airflow overview (Google Cloud Documentation) — Google Cloud
• Load CSV data from Cloud Storage (Google Cloud Documentation) — Google Cloud