GCP Services

Before you touch a single service, you need to know why the cloud exists and where you fit in.

• What is Google Cloud Platform, and why organizations choose it
• The data engineer's role in the GCP ecosystem
• Global infrastructure: Regions and Zones, and why they matter for latency, availability, and compliance

You'll learn to: explain GCP's value proposition and navigate its global infrastructure model with confidence.

Access control comes before real services are handed to you. This is how every GCP project stays secure.

• Permissions, Roles, and how they combine (Predefined vs Custom Roles)
• The Least Privilege Principle and why over-permissioning causes real production incidents
• Service Accounts: what they are, why applications need their own identity, and how to name/manage them

You'll learn to: design access policies the way cloud security teams expect, and set up service accounts correctly for any application.

The entry point of virtually every data pipeline. This is where raw data lands first.

• Buckets, objects, and folder-structure conventions
• Storage Classes (Standard, Nearline, Coldline, Archive) and cost optimization
• Location types: Region, Dual-Region, Multi-Region
• Bucket-level access control and industry naming standards
• Core gcloud/gsutil operations: create, upload, copy, move, delete
• IAM roles specific to GCS (Object Viewer, Object Admin, Storage Admin)

You'll learn to: stand up and manage a production-ready data lake layer, and choose the right storage tier for any access pattern.

The analytical warehouse, where raw data becomes business insight.

• Fundamentals94 items

  • Basic SELECT & Filtering6 items

    • SELECT
    • WHERE
    • ORDER BY
    • DISTINCT
    • LIMIT
    • Column aliases
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  • Operators18 items

    • Arithmetic Operators4 items

      • +
      • -
      • *
      • /
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    • Comparison Operators6 items

      • =
      • <
      • >
      • <=
      • >=
      • <>
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    • Logical Operators3 items

      • AND
      • OR
      • NOT
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    • Membership Operators2 items

      • IN
      • NOT IN
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    • Range Operator1 item

      • BETWEEN ... AND
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    • Pattern Matching Operators2 items

      • LIKE
      • NOT LIKE
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  • Data Types5 items

    • Numeric data types
    • String data types
    • Date and time data types
    • Boolean values
    • NULL
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  • Single-Row Functions36 items

    • CASE / Conditional Logic2 items

      • CASE
      • IF
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    • Character Functions15 items

      • UPPER()
      • LOWER()
      • INITCAP()
      • SUBSTR()
      • INSTR()
      • LPAD()
      • RPAD()
      • LTRIM()
      • RTRIM()
      • TRIM()
      • CONCAT()
      • REPLACE()
      • TRANSLATE()
      • REVERSE()
      • LENGTH()
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    • Number Functions5 items

      • ROUND()
      • TRUNC()
      • MOD()
      • POWER()
      • ABS()
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    • Date Functions9 items

      • CURRENT_DATE()
      • DATE_ADD()
      • DATE_SUB()
      • DATE_DIFF()
      • DATE_TRUNC()
      • EXTRACT()
      • LAST_DAY()
      • FORMAT_DATE()
      • PARSE_DATE()
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    • Conversion Functions2 items

      • CAST()
      • EXTRACT()
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    • General / NULL Functions3 items

      • IFNULL()
      • NULLIF()
      • COALESCE()
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  • Aggregate Functions & Grouping7 items

    • Aggregate Functions5 items

      • MIN()
      • MAX()
      • COUNT()
      • AVG()
      • SUM()
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    • Grouping2 items

      • GROUP BY
      • HAVING
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  • Joins4 items

    • INNER JOIN
    • LEFT JOIN
    • RIGHT JOIN
    • FULL OUTER JOIN
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  • Subqueries5 items

    • Subqueries in SELECT
    • Subqueries in WHERE
    • Subqueries in FROM
    • IN subqueries
    • Correlated subqueries
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  • Window / Analytical Functions12 items

    • Window-Specific Functions5 items

      • ROW_NUMBER()
      • RANK()
      • DENSE_RANK()
      • LEAD()
      • LAG()
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    • Aggregate Functions Used as Window Functions5 items

      • MIN()
      • MAX()
      • COUNT()
      • AVG()
      • SUM()
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    • Window Clauses2 items

      • OVER
      • PARTITION BY
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  • CTEs (WITH Clause)1 item

    • WITH clause
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• BigQuery & Table Concepts18 items

  • DDL4 items

    • CREATE
    • ALTER
    • DROP
    • TRUNCATE
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  • DML4 items

    • INSERT
    • UPDATE
    • DELETE
    • MERGE
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  • BigQuery Tables4 items

    • Native Tables
    • Temporary Tables
    • GCS External Tables
    • BigLake Tables
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  • Views & Materialized Views4 items

    • Views
    • Materialized Views
    • When to use
    • When not to use
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  • Partitioning & Clustering2 items

    • Partitioning
    • Clustering
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• BigQuery Optimization5 items

  • Selecting only required columns
  • Efficient filtering
  • Proper join usage
  • Avoiding unnecessary data processing
  • General BigQuery query optimization practices
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You'll learn to: design and query a serverless data warehouse at scale, and write cost-efficient SQL that a senior engineer would approve.

The compute layer. This is where your data actually gets processed.

• Cloud Run Functions: HTTP vs event triggers, scaling behavior, cold starts, IAM roles
• Packaging code with Docker and publishing to Artifact Registry
• Cloud Run Jobs: building the processing layers of a real ETL pipeline (ingestion, raw load, validation, curated transformation, and delivery to downstream systems)

You'll learn to: containerize and deploy serverless data-processing jobs, and understand exactly how a production ETL pipeline is broken into stages.

Once you have storage and compute, you need something to run them in the right order, automatically.

• Cloud Scheduler: time-based triggering (cron-style automation)
• Eventarc: event-driven triggering (react the moment something happens)
• Pub/Sub: asynchronous messaging and the backbone of streaming pipelines
• Cloud Workflows: orchestrating multi-step pipelines, and how it compares to Airflow/Cloud Composer

You'll learn to: automate an end-to-end pipeline without babysitting it, and choose the right trigger mechanism for any scenario (scheduled, event-driven, or message-based).

Knowing every tool is less valuable than knowing which one to reach for.

• Cloud Run vs Dataflow vs Dataproc: a practical decision framework based on data volume, complexity, and team skillset

You'll learn to: justify architecture decisions in a design review or interview: not just use a service, but explain why it was the right choice.

How real teams ship infrastructure and code changes safely.

• Terraform: Infrastructure as Code, core commands (init, plan, apply)
• Git/GitHub branching strategy for multi-environment projects
• CI/CD pipelines: automated build, test, and deployment across dev/QA/prod

You'll learn to: version and deploy cloud infrastructure the way modern engineering teams do, with no manual console clicking in production.