Cloud Computing and Data Analytics Integration Training Course

Course overview

Cloud platforms and analytics have stopped being separate disciplines. Storage, compute, and modeling now sit inside the same environment, which means the value of data increasingly depends on how well a cloud architecture is designed to move, transform, and interrogate it. This course examines that intersection in depth, treating cloud computing and data analytics as one connected system covering service models, storage design, pipelines, in-cloud machine learning, real-time processing, governance, and cost control.

Participants study how organizations combine IaaS, PaaS, and SaaS with data lakes, warehouses, and ETL or ELT workflows to support reliable analytics. Discussion draws on documented practice from AWS, Microsoft Azure, and Google Cloud as subject matter, alongside frameworks for security, compliance, and financial management. The emphasis stays on judgment: understanding which architectural choices serve which analytical goals, and why integration decisions carry long-term consequences for performance, spend, and trust in results.

Why cloud and analytics integration decides who wins with data

A decade ago, an organization could treat its analytics stack as a project bolted onto existing infrastructure. That approach no longer holds. Data volumes have grown faster than on-premises capacity can absorb, streaming sources such as sensors and transaction logs demand elastic compute, and business teams expect insight measured in minutes. When cloud and analytics are integrated deliberately, a company can scale a Spark workload for a seasonal spike and release it afterward, keep a governed data lake feeding a warehouse, and expose curated datasets to analysts without duplicating storage. When they are not, the result is fragmented pipelines, runaway bills, and dashboards nobody trusts.

Poor integration also raises the stakes on regulation and cost. GDPR obligations, encryption requirements, and residency rules all become harder to honor when data sprawls across disconnected services, and unmanaged consumption is one of the most common reasons cloud analytics initiatives overrun their budgets. Understanding how modeling connects to this foundation is central to the discipline, and the connection between architecture and predictive work is explored further in Machine Learning for Business Intelligence. Getting the integration right early is what separates organizations that compound value from data from those that merely accumulate it.

What you will be able to do afterwards

• Map an analytics need to cloud service, storage, and processing models.
• Compare data lake, warehouse, and lakehouse designs for a workload.
• Explain how ETL/ELT, orchestration, and ingestion fit together.
• Assess governance, encryption, and GDPR compliance controls.
• Apply FinOps thinking to cloud analytics cost and ROI.

Course outline

Unit 1: Introduction to cloud and data analytics

• IaaS, PaaS, and SaaS responsibility boundaries
• Analytics value chain: ingestion to decision
• AWS, Azure, and Google Cloud service categories
• Failure patterns without an integration plan

Unit 2: Cloud computing architectures

• VMs, containers, and serverless compute
• Regions, availability zones, and data residency
• Managed versus self-managed services
• Storage and compute separation for scaling

Unit 3: Data storage and management in the cloud

• Object storage: S3, Azure Blob, Cloud Storage
• Warehouses: Redshift, BigQuery, Snowflake
• Lakehouse pattern with Parquet and Delta
• Partitioning, cataloging, and metadata

Unit 4: Building data pipelines in the cloud

• ETL versus ELT in cloud warehouses
• Batch versus streaming ingestion
• Orchestration with Airflow and AWS Glue
• Data quality, schema evolution, idempotency

Unit 5: Analytics tools and cloud integration

• BI platforms: Power BI, Tableau, Looker
• Query engines: Athena and BigQuery
• Semantic layers and governed datasets
• Live connections versus cached extracts

Unit 6: Machine learning in the cloud

• SageMaker, Azure ML, and Vertex AI
• Model lifecycle: training to monitoring
• ML in descriptive and diagnostic analytics
• Feature stores, model drift, retraining

Unit 7: Real-time and big data analytics

• Distributed processing with Apache Spark
• Streaming: Kafka, Kinesis, Pub/Sub
• Windowing, event time, and late data
• Lambda and Kappa architectures

Unit 8: Governance, security, and compliance

• IAM, least privilege, separation of duties
• Encryption, key management, tokenization
• GDPR, data residency, personal data
• Data lineage, auditing, and cataloging

Unit 9: Cloud cost optimization and ROI

• FinOps shared cost accountability
• Pricing: on-demand, reserved, spot
• Waste: idle clusters, oversized warehouses
• Estimating ROI for analytics initiatives

Unit 10: Hybrid and multi-cloud analytics

• Drivers: resilience, regulation, lock-in
• Data gravity and egress costs
• Open formats and portable orchestration
• Federated query and data virtualization

Unit 11: Communicating analytics insights

• Clear visualization and chart selection
• Narratives that connect findings to decisions
• Tailoring depth for different audiences
• Misleading scales and false precision

Unit 12: Capstone cloud and analytics integration project

• Translating a need into a cloud architecture
• Selecting storage, pipeline, and processing
• Layering governance, security, and cost
• Defending choices against alternatives

How the course is delivered

Sessions run through structured discussion, documented case studies, and worked examples that trace real integration decisions from requirement to design. Participants do not write code or operate cloud consoles, and the course includes no software administration; instead, the emphasis falls on understanding architectures, weighing trade-offs, and reading how the pieces fit together so that decisions can be made with confidence back in the workplace.

Who should attend

• Cloud architects and engineers who want a clearer view of how analytics workloads shape infrastructure choices.
• Data analysts and scientists seeking to understand the cloud foundations beneath their tools and models.
• IT and digital transformation managers responsible for platform strategy and integration decisions.
• Business intelligence professionals and governance leaders accountable for reliable, compliant reporting.

About EuroQuest International Training

EuroQuest International Training has delivered professional courses since 2015, with a catalog of more than 1,000 titles attended by over 15,000 participants and training venues in cities including Dubai, London, Barcelona, Istanbul, Vienna, Paris, and Geneva. Courses are practical in focus and grounded in current professional practice.

Frequently asked questions

Do participants operate cloud platforms or write any code during the course?

No. The course is discussion-based and conceptual, so participants do not operate cloud consoles, administer services, or write code. Learning comes from case studies, worked examples, and structured analysis of how integrated cloud and analytics systems are designed.

Do I need a deep technical background to benefit?

A general familiarity with data or IT concepts helps, but the material is explained from first principles and builds gradually. Both technical specialists and managers who oversee analytics work find the framing accessible and relevant to their decisions.

Does this course award a certification?

The course is educational and does not provide certification or a formal qualification. Participants leave with practical frameworks and a record of attendance from EuroQuest International Training.

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Register for this course

To confirm dates, locations, and registration details, contact EuroQuest International Training or visit the course page on our website.