AWS Data Engineer Training Course: Build and operationalise data pipelines on AWS
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Description of the training: AWS Data Engineer Training Course: Build and operationalise data pipelines on AWS
The AWS Data Engineer training course tackles what happens between the raw data and the dashboard: ingestion, transformation, storage — and above all the part nobody talks about: making sure the pipeline still runs in six months, that you know where every figure came from, and what it cost.
Over 3 days, you build a complete chain on AWS: batch and streaming ingestion with Kinesis and DMS, data lake storage on S3 with the columnar formats that change everything (Parquet, partitioning, Iceberg), transformation with Glue and EMR, warehousing with Redshift, ad hoc querying with Athena. Orchestration, data quality and access governance with Lake Formation get full treatment, because that is where data projects fail — rarely on the transformation logic itself.
The programme follows the syllabus of the AWS Certified Data Engineer – Associate (DEA-C01) certification, which replaces the retired Data Analytics Specialty.
If AWS is new to you, our AWS Cloud Practitioner Training Course lays the groundwork, and our AWS Essential Services Training Course covers the S3 and compute services used here. On the streaming side, our Apache Kafka Training Course illuminates the concepts you will meet again in Kinesis. To compare ecosystems, see our Azure Data Lake, Synapse, Data Factory and Spark Training Course and our Azure Databricks Training Course. Finally, running pipelines in production extends our AWS DevOps Engineer Training Course, and keeping the bill under control our AWS FinOps Training Course.
Good to know
Sessions are guaranteed from a single registrant (except in cases of force majeure). A preliminary discussion takes place between the participant and/or a company representative to fully take into account the participant’s profile. Assessment: quizzes, role-play and practical exercises. A certificate of completion is issued at the end of the training. This training prepares you for the AWS Certified Data Engineer – Associate (DEA-C01) certification (exam not included). This training is part of our Cloud Computing Training Courses catalogue. Discover our other cloud training courses to master architectures, services and best practices on AWS, Azure and GCP. To get started on AWS, we recommend our AWS Cloud Practitioner Training Course as a prerequisite, which lays the foundations of the Amazon Cloud.
Objectives of the training: AWS Data Engineer Training Course: Build and operationalise data pipelines on AWS
By the end of the training, participants will be able to:
- Design a data architecture on AWS: data lake, warehouse and lakehouse models.
- Ingest data in batch and in streaming (Kinesis, DMS, DataSync).
- Transform data at scale with AWS Glue and Amazon EMR.
- Choose and optimise storage formats and partitioning on S3 (Parquet, Iceberg).
- Model, load and optimise an Amazon Redshift warehouse.
- Orchestrate pipelines and embed data quality checks within them.
- Govern data access with Lake Formation and keep processing costs under control.
Prerequisites of the training: AWS Data Engineer Training Course: Build and operationalise data pipelines on AWS
- SQL practice is essential: you should be able to write a join and an aggregation without hesitation. Some Python makes the Glue and EMR modules considerably easier, without being formally required.
- Some initial AWS experience (S3, IAM, EC2) is expected. The level of the AWS Cloud Practitioner certification is a sufficient base.
- Because every participant is unique, a personalised discussion with our expert allows us to design a training course perfectly aligned with their objectives.
Target audience of the training: AWS Data Engineer Training Course: Build and operationalise data pipelines on AWS
- Data engineers and ETL developers building pipelines on AWS.
- Analysts and BI developers who now have to feed their own data sources.
- Database administrators moving towards data lake architectures.
- Architects and technical leads preparing for the Data Engineer – Associate certification.
Detailed programme of the training: AWS Data Engineer Training Course: Build and operationalise data pipelines on AWS
Data architectures on AWS: lake, warehouse, lakehouse
Ingestion: batch and streaming
Storage: the data lake on S3
Transformation: Glue and EMR
Warehousing: Amazon Redshift
Querying, orchestration and quality
Governance, security and cost
Course highlights
- builds a complete data chain, from streaming ingestion through to consumption, on realistic volumes;
- follows the syllabus of the AWS Certified Data Engineer – Associate (DEA-C01) certification, which replaces the Data Analytics Specialty;
- confronts cost and data quality head-on, where most data projects come off the rails;
- runs in a group of 1 to 3 participants, which allows work on your own datasets.
FAQ
Do I need to know Python?
It is not blocking, but it helps a great deal in the Glue and EMR modules, where Spark jobs are written in Python. The genuinely indispensable prerequisite is SQL. If Python is unfamiliar, say so during the preliminary discussion: we adapt the labs to stay with the visual approach of Glue Studio.
How does this differ from the retired Data Analytics Specialty certification?
AWS has retired the Data Analytics Specialty and replaced it with Data Engineer – Associate (DEA-C01), which focuses on building and operating pipelines rather than on analysis. This course follows the new syllabus — which is also why a title such as “Data Warehousing”, still found in some catalogues, corresponds to a dated offering.
Redshift or Athena: does the course help me decide?
Yes, and the answer depends mostly on your query profile. Athena suits ad hoc work and irregular volume, billed by data scanned. Redshift makes sense for repeated, predictable analytical workloads. Both are implemented during the course, on the same data, so the comparison is concrete.
Can we work on our own data?
It is possible and often desirable, within the limits of what you can take out of your environment. The preliminary discussion is there to frame this; failing that, we provide realistic datasets.