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PDE: Google Professional Data Engineer

Build scalable batch and streaming data platforms on Google Cloud — and prepare for the Professional Data Engineer exam.

  • 32 hours of live, hands-on data engineering
  • Labs with BigQuery, Dataflow, Pub/Sub and Dataproc
  • Batch and streaming pipelines built end to end
  • Covers all Professional Data Engineer exam sections
  • Design scenarios with whiteboard reviews
Aligned to Google Cloud exam PDE
Duration
4 Days | 32 Hrs
Next batch
Batches on request
Delivery
Live Online · Onsite · Corporate
Level
Advanced
Exam
PDE
Language
English

Why learn PDE with Ace Skills

  • BigQuery done well

    Design tables, partitions and queries that stay fast and affordable at scale.

  • Batch and streaming

    Build both pipeline styles with Dataflow and learn when each one fits.

  • Governed data platforms

    Apply security, lineage and quality controls that make data trustworthy.

  • Professional credential

    Prepare for a certification widely recognised for senior data engineering roles.

Course overview

Data engineers on Google Cloud design platforms that turn raw events and records into trusted, analysis-ready data. They choose between many services — BigQuery, Dataflow, Pub/Sub, Dataproc, Bigtable, Spanner and more — and balance latency, scale, cost and governance. This four-day course teaches those choices through hands-on pipelines and design scenarios.

Day one covers designing data processing systems: requirements, security and compliance, data migration and choosing the right storage and processing services. Day two is about ingestion and processing, with Pub/Sub, Dataflow pipelines in Apache Beam for batch and streaming, Dataproc for Spark workloads, and orchestration with Cloud Composer.

Day three focuses on storing data and preparing it for analysis: BigQuery table design, partitioning and clustering, cost controls, Bigtable and Spanner use cases, data lakes on Cloud Storage with BigLake and Dataplex, and preparing data for reporting and machine learning with BigQuery ML. Day four covers maintaining and automating workloads — monitoring, troubleshooting, scheduling, CI/CD for pipelines — and finishes with exam preparation.

Labs form one platform: events stream through Pub/Sub into Dataflow, land in BigQuery and Cloud Storage, are governed with Dataplex and analysed with SQL and BigQuery ML. This course is aligned to the Professional Data Engineer exam and prepares you for it; confirm the current exam guide on the Google Cloud certification site before booking.

Key features

  • End-to-end streaming and batch pipeline
  • Apache Beam on Dataflow labs
  • BigQuery design and cost optimisation
  • Orchestration with Cloud Composer
  • Governance with Dataplex and BigLake
  • Scenario-based practice questions
  • Course completion certificate from Ace Skills Consulting

Who should attend

  • Data engineers working with or moving to Google Cloud
  • ETL and big data developers
  • Analytics engineers and BI developers
  • Data architects designing cloud data platforms

Prerequisites

  • Strong SQL skills
  • Working knowledge of Python or Java
  • Familiarity with data warehousing and ETL concepts
  • Basic Google Cloud experience recommended

Curriculum

4 Days · 32 Hours · 12 Modules · 9 Labs

  1. Design foundations

    • Gathering data requirements
    • Choosing processing services
    • Choosing storage services
    • Designing for reliability and portability

    Security and compliance

    • IAM for data services
    • Encryption and Cloud KMS
    • Sensitive Data Protection (DLP)
    • Data residency and sovereignty
    • Lab: Mask sensitive fields with Sensitive Data Protection · 40 min

    Data migration

    • Migrating warehouses to BigQuery
    • Database Migration Service
    • Storage Transfer Service and Transfer Appliance
    • Validating migrated data
    • Lab: Load and validate a migrated dataset in BigQuery · 40 min

  2. Messaging and ingestion

    • Pub/Sub topics, subscriptions and ordering
    • Datastream for change data capture
    • Batch ingestion patterns
    • Lab: Publish events to Pub/Sub and inspect subscriptions · 30 min

    Dataflow and Apache Beam

    • PCollections and transforms
    • Windowing, triggers and late data
    • Templates and Flex Templates
    • Autoscaling and debugging pipelines
    • Lab: Build a streaming Dataflow pipeline from Pub/Sub to BigQuery · 75 min

    Dataproc and orchestration

    • Dataproc clusters and Serverless Spark
    • Cloud Composer DAGs
    • Workflows for lightweight orchestration
    • Lab: Orchestrate a batch job with Cloud Composer · 45 min

  3. BigQuery in depth

    • Partitioning and clustering
    • Slots, editions and cost control
    • Materialised views and BI Engine
    • Authorised views and row-level security
    • Lab: Optimise a slow, expensive BigQuery workload · 50 min

    Operational and specialised stores

    • Bigtable schema design
    • Spanner for global consistency
    • Cloud SQL and AlloyDB
    • Memorystore

    Data lakes and analysis

    • BigLake and Cloud Storage lakes
    • Dataplex governance and lineage
    • BigQuery ML
    • Sharing data with Analytics Hub
    • Lab: Train and evaluate a model with BigQuery ML · 40 min

  4. Operations

    • Monitoring pipelines with Cloud Monitoring
    • Troubleshooting failed jobs
    • Data quality checks
    • Resource planning and quotas
    • Lab: Add monitoring and alerting to the streaming pipeline · 40 min

    Automation

    • Scheduling queries and workflows
    • CI/CD for data pipelines
    • Infrastructure as code for data platforms
    • Lab: Deploy a Dataflow template through Cloud Build · 40 min

    Exam readiness

    • Service-selection patterns
    • Exam section recap
    • Timed practice set with review
Download full syllabus (PDF)

Skills you will gain

  • BigQuery
  • Dataflow
  • Apache Beam
  • Pub/Sub
  • Dataproc
  • Cloud Composer
  • Bigtable
  • Spanner
  • Dataplex
  • BigQuery ML
  • Data governance
  • Streaming pipelines

Tools & technologies

  • Google Cloud console
  • BigQuery Studio
  • Apache Beam SDK
  • Cloud Composer (Airflow)
  • gcloud CLI
  • Cloud Build
  • Jupyter notebooks

Certification exam

Exam details
Exam nameGoogle Cloud Certified – Professional Data Engineer
Exam codePDE
Number of questions50–60
Duration120 minutes
Voucher includedNo — book directly with the vendor
Question formatMultiple choice, Multiple response
DeliveryOnline proctored or Test centre

Skill areas measured

  1. Designing data processing systems
  2. Ingesting and processing the data
  3. Storing the data
  4. Preparing and using data for analysis
  5. Maintaining and automating data workloads

Exam details are pending verification. Vendors change exams; confirm on the official vendor page before booking. Official exam page (opens in a new tab)

Schedule & Pricing

Live Online

Individual

Price on request

  • 32 hours of live, trainer-led sessions
  • 9 hands-on labs
  • Ace Skills course completion certificate

Onsite

At your office or our centre

Pricing depends on location, group size and dates.

  • Delivered at your premises or an Ace centre
  • Same curriculum and labs as live online
  • Dates planned around your team

Corporate / Team

Private batch for your organisation

A private batch with content adapted to your environment and goals.

  • Customised curriculum
  • Private batch, online or onsite
  • Progress and assessment reports
  • GST invoice and PO-based billing

No public batches scheduled — Talk to an Advisor for a private batch.

Projects & labs you will build

  • Day 2 · 75 min

    Build a streaming Dataflow pipeline from Pub/Sub to BigQuery

  • Day 3 · 50 min

    Optimise a slow, expensive BigQuery workload

Your certificate

Certificate of Completion — Ace Skills Consulting

Awarded on completing the course. It records the course title and training hours, and you can share it on your LinkedIn profile.

The Google Cloud certification itself is awarded by Google Cloud when you pass exam PDE.

Frequently asked questions

Do I need to know Apache Beam?

No. Beam concepts are taught from the beginning, though Python or Java experience is needed to follow the labs.

How much machine learning is covered?

Enough for the exam: preparing data for ML and using BigQuery ML and Vertex AI at a practical level.

Is this course useful without the exam?

Yes. The pipeline you build mirrors common production designs and can serve as a reference for your own work.

How long should I prepare after the course?

Most learners revise for four to six weeks, focusing on service-selection scenarios.

Can our data team attend together?

Yes. Private batches can use sample data and pipeline patterns similar to your own.

Related courses

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Train your team on PDE

Private batches online or at your office in India and the GCC, with a curriculum adapted to your environment.