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Amazon Web Services (AWS) logoIntermediate

DEA-C01: AWS Data Engineer – Associate

Build reliable data pipelines on AWS — from ingestion to the data lake to analytics — and prepare for the DEA-C01 exam.

  • 24 hours of live, pipeline-building sessions
  • Hands-on with Glue, Kinesis, Athena, Redshift and Lake Formation
  • Batch and streaming patterns side by side
  • Mapped to the four DEA-C01 domains
  • Practice questions with detailed explanations
Aligned to Amazon Web Services (AWS) exam DEA-C01
Duration
3 Days | 24 Hrs
Next batch
Batches on request
Delivery
Live Online · Onsite · Corporate
Level
Intermediate
Exam
DEA-C01
Language
English

Why learn DEA-C01 with Ace Skills

  • Pick the right data store

    Learn the trade-offs between S3, Redshift, DynamoDB and RDS for real analytical workloads.

  • Pipelines that keep running

    Orchestrate, monitor and recover pipelines instead of building one-off scripts.

  • Governed by design

    Apply fine-grained permissions and encryption so the data lake is safe to share.

  • Exam-aligned throughout

    Each module maps to a DEA-C01 domain and ends with practice questions.

Course overview

Data engineers make data usable: they move it from source systems, shape it into trustworthy datasets, store it cost-effectively and keep pipelines running. On AWS that work spans a wide set of services, and choosing the right one for a given volume, latency and budget is the core skill this course develops.

Day one is about ingestion and transformation. You will compare batch and streaming ingestion with Kinesis, Amazon MSK, AWS DMS and AppFlow, then transform data with AWS Glue jobs and PySpark, choosing file formats and partitioning schemes that keep queries fast and cheap. Day two moves to data store management: designing an S3 data lake, cataloguing with the Glue Data Catalog, modelling in Amazon Redshift, and choosing between DynamoDB, RDS and purpose-built stores.

Day three covers operations, security and governance. You will orchestrate pipelines with Step Functions and Amazon MWAA, monitor them with CloudWatch, check data quality, and secure the lake with Lake Formation permissions, encryption and auditing. Throughout the course, SQL and Python are used in short, practical doses so that you can follow along even if you are stronger in one than the other.

Labs connect into one pipeline: raw files and a stream land in S3, are catalogued and transformed, then queried with Athena and loaded into Redshift. The final session reviews exam technique and practice questions. This course is aligned to the DEA-C01 exam and prepares you for it; confirm the current exam guide on the AWS Certification site before booking.

Key features

  • One end-to-end pipeline built across the labs
  • Batch and streaming ingestion compared in practice
  • Glue and PySpark transformation exercises
  • Data lake governance with Lake Formation
  • Orchestration with Step Functions and MWAA
  • Practice questions with explained answers
  • Course completion certificate from Ace Skills Consulting

Who should attend

  • Data engineers working with or moving to AWS
  • ETL and BI developers modernising pipelines
  • Database administrators moving into data engineering
  • Analytics engineers building data lakes

Prerequisites

  • Working SQL knowledge
  • Basic Python programming
  • Familiarity with AWS core services (Cloud Practitioner level or above)
  • Understanding of data formats such as CSV, JSON and Parquet

Curriculum

3 Days · 24 Hours · 9 Modules · 6 Labs

  1. Ingestion patterns

    • Batch versus streaming ingestion
    • Kinesis Data Streams and Firehose
    • Amazon MSK overview
    • AWS DMS for database replication
    • Amazon AppFlow for SaaS sources
    • Lab: Stream events into S3 with Kinesis Data Firehose · 45 min

    Transformation with AWS Glue

    • Glue jobs, DynamicFrames and PySpark
    • Crawlers and the Data Catalog
    • Job bookmarks and incremental loads
    • Glue DataBrew for visual preparation
    • Lab: Transform raw CSV to partitioned Parquet with a Glue job · 60 min

    Designing for performance and cost

    • Columnar formats and compression
    • Partitioning and bucketing
    • Serverless versus provisioned processing
    • Amazon EMR at a glance

  2. Building the data lake

    • S3 layout: raw, curated and consumption zones
    • Lifecycle policies and storage classes
    • Open table formats such as Apache Iceberg
    • Schema evolution
    • Lab: Query the curated zone with Amazon Athena · 40 min

    Amazon Redshift

    • Distribution and sort keys
    • COPY, UNLOAD and Redshift Spectrum
    • Redshift Serverless
    • Materialised views and workload management
    • Lab: Load curated data into Redshift Serverless and tune a query · 55 min

    Operational data stores

    • DynamoDB data modelling
    • RDS and Aurora for transactional sources
    • Choosing a store by access pattern

  3. Orchestration and operations

    • Step Functions workflows
    • Amazon MWAA (managed Airflow)
    • Glue workflows and triggers
    • Monitoring pipelines with CloudWatch
    • Data quality checks
    • Lab: Orchestrate the pipeline with Step Functions and add failure alerts · 50 min

    Security and governance

    • Lake Formation permissions and tags
    • Encryption with KMS
    • Masking sensitive data
    • Auditing with CloudTrail
    • Lab: Grant column-level access with Lake Formation · 35 min

    Exam readiness

    • DEA-C01 domain recap
    • Service-selection question patterns
    • Timed practice set with review
Download full syllabus (PDF)

Skills you will gain

  • AWS Glue
  • PySpark
  • Amazon Kinesis
  • AWS DMS
  • Amazon S3 data lakes
  • Amazon Athena
  • Amazon Redshift
  • Lake Formation
  • Step Functions
  • Amazon MWAA
  • Data modelling
  • Data quality

Tools & technologies

  • AWS Glue Studio
  • Amazon Athena
  • Amazon Redshift Query Editor
  • AWS CLI
  • Jupyter notebooks
  • Apache Airflow

Certification exam

Exam details
Exam nameAWS Certified Data Engineer – Associate
Exam codeDEA-C01
Number of questions65
Duration130 minutes
Passing score720 / 1000 (scaled)
Voucher includedNo — book directly with the vendor
Question formatMultiple choice, Multiple response
DeliveryOnline proctored or Test centre
Validity & renewal3 years

Skill areas measured

  1. Data Ingestion and Transformation
  2. Data Store Management
  3. Data Operations and Support
  4. Data Security and Governance

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

  • 24 hours of live, trainer-led sessions
  • 6 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 1 · 60 min

    Transform raw CSV to partitioned Parquet with a Glue job

  • Day 3 · 50 min

    Orchestrate the pipeline with Step Functions and add failure alerts

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 Amazon Web Services (AWS) certification itself is awarded by Amazon Web Services (AWS) when you pass exam DEA-C01.

Frequently asked questions

How much coding is involved?

Moderate. You will write SQL and short PySpark scripts, with templates provided so the focus stays on design decisions.

Do I need Spark experience?

No. Spark concepts are introduced as needed, and Glue Studio offers a visual option for learners new to it.

Is this course relevant for Databricks or Snowflake users?

Yes. The ingestion, partitioning and governance principles carry over, although the labs use AWS-native services.

What should I study after the course?

We recommend revisiting the labs, then focusing on service-selection questions, which make up much of the exam.

Can our data team attend together?

Yes. Private batches can use sample data shaped like your own sources.

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Train your team on DEA-C01

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