ITTA offers an AWS Data Engineering catalogue: Building Batch Data Analytics Solutions on AWS (Glue, EMR, Athena, Redshift) and Deep Learning on AWS for data + AI profiles. Courses prepare data engineers, data architects and data scientists to build managed analytics pipelines on AWS. Audience: data engineers, data architects, BI, cloud data scientists. Sessions delivered in Geneva, Lausanne and interactive virtual classroom.
FILTER BY
- Domains
- Editors
- Location
- Format
- Level
- Certifying
- Confirmed training
In this course, you will learn about AWS’s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works.
Certifying
AWS – Building Batch Data Analytics Solutions on AWS”: Train to create efficient data analytics pipelines on AWS using Amazon EMR
Certifying
The AWS Data Engineering sub-publisher regroups at ITTA the data courses on AWS. In 2026, AWS offers a very complete data stack, organised around a managed lakehouse architecture: S3 object storage as data lake, AWS Glue for ETL and data catalog, Amazon Athena for serverless SQL on S3, Amazon Redshift as data warehouse, Amazon EMR for managed Spark, Amazon Kinesis for streaming, Amazon QuickSight for BI. The Apache Iceberg table format is now native on AWS (Glue, Athena, EMR, Redshift), aligning AWS with modern lakehouse practices (ACID transactions, time travel, schema evolution).
The AWS Certified Data Engineer Associate (DEA-C01) certification, launched in 2024 and stabilised in 2026, validates data engineering skills on AWS. It complements the AWS Certified Machine Learning Specialty certification for data + AI profiles. This certification structure reflects the increasing specialisation of data roles: data engineer (pipelines), data scientist (models), MLOps (industrialisation), data analyst (BI), data architect (architecture).
Our AWS Data Engineering catalogue at ITTA:
Building Batch Data Analytics Solutions on AWS is an official AWS course covering data analytics services in batch mode: designing a data lake on S3, partitioning and formats (Parquet, ORC, Iceberg), AWS Glue for ETL jobs and data catalog, Amazon Athena for serverless SQL queries, Amazon Redshift as data warehouse (architecture, distribution, sort keys, Spectrum to query S3), Amazon EMR for large-scale Spark workloads, AWS Lake Formation for governance and fine permissions, BI integration (QuickSight, third-party tools). Deep Learning on AWS complements with deep learning on SageMaker, distributed training and inference deployment.
AWS is a widely used cloud provider on data projects in French-speaking Switzerland and internationally. Mastering Glue, Athena, Redshift, EMR and S3 has become a highly demanded skill for data engineers, whether they work in banking, insurance, public sector, industry, retail or SaaS. Learning this stack with a structured path considerably shortens upskilling compared to self-learning. The course also addresses trade-offs with alternatives: Databricks on AWS, Snowflake on AWS, Dremio, as well as Azure and GCP equivalent services.
AWS Data Engineering fits in the broader AWS catalogue. To start on AWS, the AWS Foundation publisher covers fundamentals (CLF-C02 Cloud Practitioner, Solutions Architect Associate). The root publisher AWS (Amazon Web Services) regroups all AWS certifications. For DevOps and containers on AWS, the AWS DevOps publisher covers EKS, ECS, Fargate.
On the broader data side, the data science sub-domain brings the analytical uses on data engineering and AI. The data and databases sub-domain covers administration and BI competencies. For profiles combining AWS and applied AI, the ITTA Artificial Intelligence publisher allows extending toward generative AI and MLOps uses.
Our AWS Data Engineering audience is varied: data engineers building or maintaining AWS pipelines, data architects validating a lakehouse choice on AWS for a new project, data analysts wanting to understand the technical environment under their Athena or Redshift dashboards, data scientists needing to industrialise their models on EMR or SageMaker, DBAs in transition to cloud, data consultants intervening on data warehouse modernisation projects toward lakehouse in French-speaking Switzerland and internationally.
The 2026 data landscape is rich in options. The native AWS stack (S3 + Glue + Athena + Redshift + EMR) is widely used by teams already anchored in the AWS ecosystem wanting native integration with IAM, VPC, observability, IaC. Databricks on AWS brings a unified lakehouse experience with Delta Lake, Unity Catalog and MLflow, particularly suited to demanding data + AI organisations. Snowflake on AWS offers a cloud-native multi-provider data warehouse with simple SQL experience. The choice depends on priorities. Our course addresses this positioning at session start.
You come from an on-premise environment (Hadoop, classic ETL, traditional data warehouse) and need to switch to AWS. Building Batch Data Analytics Solutions gives you the technical foundation to drive this transition, understanding Glue, Athena, Redshift and EMR in their use context.
You drive a data warehouse modernisation toward AWS lakehouse. The course gives you technical trade-offs (Iceberg vs raw Parquet formats, Redshift vs Athena, EMR vs Glue) to structure the target and migration trajectory.
You want to understand the environment under your Athena queries or QuickSight dashboards. The course gives you understanding of data lake and data warehouse layers without imposing complete data engineering expertise.
Several trends shape AWS data engineering in 2026. Apache Iceberg has asserted itself as standard lakehouse table format, natively supported by Glue, Athena, EMR and Redshift. Data governance via AWS Lake Formation has become central for regulated organisations (finance, healthcare, public). Real-time streaming via Kinesis and MSK (Managed Streaming for Kafka) gains adoption for near-real-time use cases. Data + AI integration industrialises via SageMaker, Bedrock (foundation models) and vector databases (OpenSearch, RDS pgvector). Data FinOps is a sensitive topic (Athena, Redshift, EMR costs) integrated into data engineering practices. Finally, Spark + SQL + Python convergence under a single platform (lakehouse) simplifies architectures.
Our AWS Data Engineering sessions are scheduled in Geneva, Lausanne and in interactive virtual classroom with a live trainer. The format is very practice-oriented on a real AWS environment. Material modalities are communicated in advance by our education team. For data teams seeking grouped upskilling on their real AWS architecture, we organise in-house sessions calibrated on your stack (Glue jobs, Redshift cluster, Athena, Lake Formation, fine governance).
Do I need Python knowledge before this course?
Python culture helps for Glue PySpark jobs and EMR notebooks. The course remains accessible with beginner to intermediate Python level. A Python data path upstream is advised for profiles without prior experience.
Redshift or Athena to start?
Athena is serverless and economical for ad hoc queries on S3. Redshift is a managed data warehouse suited to high-volume BI workloads with complex repeated queries. The choice depends on use cases. Our course addresses these trade-offs.
Does the course prepare for DEA-C01 certification?
The Building Batch Data Analytics course is an important brick for AWS Certified Data Engineer Associate (DEA-C01), but does not cover all exam domains. For complete DEA-C01 preparation, complementary sessions (Kinesis streaming, Step Functions orchestration, fine data security) should be planned.
AWS vs Databricks for a new data project?
Native AWS is relevant for teams already anchored AWS wanting native integration. Databricks brings a more mature unified lakehouse experience for demanding data + AI projects. Our course honestly addresses these trade-offs.
ITTA offers a coherent AWS catalogue from fundamentals (Cloud Practitioner CLF-C02) to Associate (Solutions Architect, Developer, SysOps, Data Engineer DEA-C01), Professional and Specialty certifications. This continuity allows addressing a complete data trajectory, from AWS fundamentals to data engineering and AI specialisations. Our AWS Data trainers are data engineers and architects active on AWS data projects in French-speaking Switzerland, providing concrete and current examples. Sessions available in Geneva, Lausanne and interactive virtual classroom, in-house and inter-company.
Monday to Friday
8:30 AM to 6:00 PM
Tel. 058 307 73 00
ITTA
Route des jeunes 35
1227 Carouge, Suisse
Monday to Friday, from 8:30 am to 06:00 pm.
