Serverless Data Engineering and Scalable Analytics Training Course

Course Introduction

Serverless computing is reshaping modern data engineering by enabling organizations to build scalable data pipelines and analytics platforms without managing traditional server infrastructure. Serverless Data Engineering and Scalable Analytics Training Course provides an advanced exploration of serverless architectures, managed cloud services, event-driven processing, automated scaling, analytical workloads, and operational practices required to engineer modern enterprise data platforms.

This course examines how serverless technologies can be applied across the complete data engineering lifecycle, including ingestion, transformation, orchestration, storage, processing, analytics, APIs, event handling, and data serving. Participants explore architectures built around managed compute, cloud storage, serverless databases, event buses, message services, streaming platforms, query engines, workflow services, and cloud-native analytics capabilities.

A major focus is designing scalable and resilient data pipelines that automatically respond to changing workload volumes. Participants learn how to engineer event-driven and batch pipelines using functions, managed workflows, serverless processing services, and elastic analytical engines. The programme addresses concurrency, execution limits, retry strategies, idempotency, state management, partitioning, parallelism, fault isolation, and workload optimization for enterprise-scale data processing.

The course also explores serverless analytics architectures that support interactive querying, business intelligence, operational intelligence, advanced analytics, and AI-enabled workloads. Participants examine serverless data lakes, lakehouses, cloud warehouses, query engines, semantic services, data APIs, and event-driven analytical applications while learning how to balance scalability, performance, flexibility, governance, and cost across diverse analytical requirements.

Security, governance, observability, and financial management are integrated throughout the programme. Participants examine identity and access management, encryption, secrets, network controls, data classification, policy automation, lineage, quality monitoring, centralized logging, distributed tracing, usage monitoring, and serverless FinOps. These capabilities help organizations operate highly automated data environments while maintaining enterprise security, compliance, reliability, and cost visibility.

By the end of the course, participants will be able to architect, implement, and optimize serverless data engineering and analytics platforms for enterprise environments. They will develop practical strategies for migrating conventional workloads toward serverless architectures, designing event-driven pipelines, improving analytical scalability, implementing operational controls, and establishing cloud-native data platforms capable of continuously adapting to changing business and workload demands.

Duration

10 days

Who Should Attend

• Data engineers responsible for developing scalable cloud-native and serverless data pipelines.

• Cloud architects designing serverless platforms for enterprise data engineering and analytics workloads.

• Data platform engineers building managed, automated, and elastic data processing environments.

• Analytics engineers developing scalable transformation and analytical workloads using serverless technologies.

• Enterprise architects defining modern cloud-native data and analytics reference architectures.

• DevOps and DataOps professionals responsible for deployment automation, platform operations, and engineering reliability.

• Cloud infrastructure engineers transitioning traditional workloads toward managed and serverless architectures.

• Business intelligence architects developing scalable analytical services and self-service analytics platforms.

• Data scientists and AI engineers requiring elastic data processing and analytical infrastructure.

• Data governance, security, and privacy professionals overseeing serverless enterprise data environments.

• Data quality and observability specialists responsible for monitoring serverless pipelines and analytical services.

• Technology managers leading cloud modernization, serverless adoption, and data platform transformation initiatives.

• Database and data warehouse professionals exploring serverless alternatives for analytical processing and data serving.

• Solution designers building event-driven applications that depend on continuously available enterprise data.

• Consultants and technical leaders implementing scalable serverless data engineering and analytics strategies.

Course Objectives

• Explain the architectural principles, capabilities, limitations, and enterprise use cases associated with serverless data engineering and analytics platforms.

• Design serverless data architectures that support ingestion, transformation, storage, orchestration, analytics, event processing, and data serving.

• Engineer scalable event-driven data pipelines using managed functions, workflows, messaging services, streaming platforms, and serverless processing engines.

• Apply serverless architecture patterns for batch, incremental, streaming, real-time, and event-triggered data engineering workloads.

• Design resilient pipelines using idempotency, retries, dead-letter handling, checkpointing, state management, and failure recovery mechanisms.

• Implement scalable analytical architectures using serverless query engines, data lakes, lakehouses, cloud warehouses, and managed analytical services.

• Optimize serverless workloads through partitioning, parallelism, concurrency management, caching, workload isolation, and efficient data layout techniques.

• Establish security and governance controls covering identity, authorization, encryption, data classification, secrets, privacy, and regulatory requirements.

• Implement comprehensive observability for serverless pipelines using metrics, logs, traces, alerts, quality signals, and operational data monitoring.

• Apply serverless FinOps principles to monitor consumption, control execution costs, optimize resource usage, and establish transparent cloud cost management.

• Integrate serverless data platforms with APIs, event-driven applications, machine learning, generative AI, real-time analytics, and intelligent data services.

• Develop enterprise migration and implementation roadmaps for transitioning traditional data engineering workloads toward scalable, automated, and serverless architectures.

Comprehensive Course Outline

Module 1: Foundations of Serverless Data Engineering

• Core principles of serverless computing and their application to modern enterprise data engineering architectures.

• Evolution from infrastructure-managed data platforms toward managed, elastic, consumption-based data processing services.

• Serverless characteristics including automatic scaling, managed infrastructure, event-driven execution, and operational abstraction.

• Architectural considerations for determining when serverless approaches are appropriate for enterprise data workloads.

Module 2: Serverless Data Architecture and Reference Models

• Designing reference architectures that connect serverless ingestion, processing, storage, orchestration, analytics, and consumption services.

• Architectural patterns for combining serverless components with managed databases, data lakes, warehouses, APIs, and streaming platforms.

• Designing loosely coupled serverless data platforms that support independent scaling, deployment, failure isolation, and technology evolution.

• Evaluating trade-offs involving performance, reliability, latency, portability, operational complexity, vendor dependency, and cost.

Module 3: Serverless Data Ingestion and Event Processing

• Designing event-driven ingestion architectures for databases, applications, APIs, files, SaaS platforms, devices, and operational systems.

• Applying managed functions, event buses, message queues, notification services, and streaming technologies for automated data ingestion.

• Engineering ingestion pipelines with schema validation, transformation triggers, metadata capture, retries, and failure handling.

• Designing scalable ingestion patterns for high-volume workloads with bursty, unpredictable, and continuously changing data volumes.

Module 4: Serverless Data Transformation and Processing

• Designing serverless transformation architectures for batch, incremental, micro-batch, and event-triggered data processing workloads.

• Applying managed functions, serverless SQL engines, distributed processing services, and workflow-driven transformations.

• Engineering reusable transformation components with validation, parameterization, dependency management, and automated deployment.

• Optimizing transformation workloads through partitioning, parallel execution, data locality, efficient formats, and workload-aware processing strategies.

Module 5: Serverless Data Lakes and Storage Architecture

• Designing cloud object storage architectures as scalable foundations for serverless data lakes and analytical data platforms.

• Applying open data formats, partitioning strategies, schema evolution, lifecycle policies, metadata, and storage optimization techniques.

• Building governed data lake architectures that support structured, semi-structured, unstructured, and analytical datasets.

• Integrating serverless storage with query engines, data catalogues, transformation services, analytics applications, and AI workloads.

Module 6: Serverless Workflow Orchestration and Data Pipelines

• Designing managed workflow architectures for scheduling, dependency management, event-driven execution, retries, and pipeline coordination.

• Applying stateful workflow patterns to coordinate serverless functions, processing jobs, validation stages, and analytical operations.

• Engineering reliable pipelines using checkpoints, compensation strategies, timeout controls, failure recovery, and idempotent processing.

• Integrating orchestration services with monitoring, metadata, data quality, security, deployment automation, and operational management.

Module 7: Serverless Analytics and Query Engineering

• Designing serverless analytical environments for interactive queries, business intelligence, operational analytics, and large-scale data exploration.

• Applying serverless query engines and analytical services to process cloud-based data without dedicated infrastructure management.

• Optimizing query performance through partition pruning, columnar formats, data layout, caching, workload management, and query design.

• Establishing analytical access patterns that balance user concurrency, responsiveness, scalability, governance, and consumption-based pricing.

Module 8: Serverless Streaming and Real-Time Analytics

• Designing serverless streaming architectures for continuous ingestion, event processing, real-time enrichment, and analytical data delivery.

• Applying managed streaming, event processing, serverless functions, and real-time query services to low-latency workloads.

• Engineering real-time pipelines for operational intelligence, monitoring, personalization, anomaly detection, and automated decision support.

• Managing ordering, delivery guarantees, replay, state, schema evolution, backpressure, fault tolerance, and streaming observability.

Module 9: Serverless Data Quality, Testing and Reliability

• Establishing automated data quality controls for accuracy, completeness, consistency, validity, uniqueness, freshness, and reconciliation.

• Designing testing strategies for serverless functions, workflows, transformations, event flows, data products, and analytical queries.

• Implementing quality gates, anomaly detection, validation rules, automated reconciliation, and event-triggered remediation processes.

• Applying reliability engineering principles to ephemeral workloads, distributed dependencies, asynchronous execution, and managed cloud services.

Module 10: Serverless Observability and Operational Intelligence

• Designing comprehensive observability architectures for serverless functions, workflows, events, pipelines, queries, and analytical services.

• Applying centralized logging, distributed tracing, metrics, alerts, execution telemetry, and dependency monitoring across serverless environments.

• Detecting performance degradation, execution failures, unusual consumption, data freshness issues, and downstream analytical impacts.

• Establishing service-level objectives, operational dashboards, incident workflows, automated diagnostics, and continuous reliability improvement.

Module 11: Serverless Security, Governance and Privacy

• Designing identity, authentication, authorization, encryption, secrets management, and network controls for serverless data platforms.

• Implementing data classification, masking, tokenization, retention, access policies, and privacy safeguards across distributed serverless services.

• Applying policy-as-code and automated governance to enforce security, compliance, data usage, and operational standards consistently.

• Establishing audit trails, lineage, compliance evidence, risk controls, and governance processes for enterprise serverless data environments.

Module 12: Serverless DataOps, DevOps and Infrastructure Automation

• Applying infrastructure-as-code to provision, configure, version, and manage serverless data services and supporting cloud resources.

• Designing CI/CD pipelines for functions, workflows, schemas, transformations, infrastructure, data quality rules, and analytical assets.

• Implementing automated testing, deployment promotion, rollback, release controls, environment isolation, and configuration management.

• Establishing DataOps practices that improve development velocity, reliability, collaboration, governance, and operational consistency.

Module 13: Serverless Data APIs and Intelligent Data Services

• Designing serverless APIs that expose governed enterprise data and analytical capabilities to applications, users, and intelligent services.

• Applying API gateways, functions, managed databases, authentication, authorization, throttling, and request-level observability.

• Building reusable data services that provide analytical results, metadata, search, transformation, and real-time data access.

• Integrating serverless data services with machine learning, generative AI, semantic retrieval, vector platforms, and intelligent applications.

Module 14: Serverless Performance, Scalability and FinOps

• Engineering serverless workloads for concurrency, throughput, latency, cold-start behavior, execution limits, and workload variability.

• Applying partitioning, parallelism, caching, batching, asynchronous processing, and workload decomposition to improve scalability and efficiency.

• Establishing FinOps practices for monitoring invocation costs, data processing consumption, storage usage, query expenditure, and service utilization.

• Balancing performance, resilience, scalability, operational simplicity, service limits, and consumption-based economics across serverless platforms.

Module 15: Advanced Serverless Data Platforms and AI-Ready Architectures

• Designing serverless architectures for machine learning, generative AI, retrieval-augmented generation, intelligent search, and advanced analytics.

• Integrating serverless data processing with vector stores, knowledge graphs, semantic services, feature platforms, and AI application architectures.

• Applying event-driven automation and managed services to support intelligent data pipelines and continuously updated AI context.

• Evaluating emerging serverless capabilities, managed data services, platform abstractions, and architectural patterns for future-ready enterprises.

Module 16: Enterprise Serverless Data Strategy and Implementation

• Developing enterprise strategies for adopting serverless data engineering across new platforms, modernized workloads, and legacy environments.

• Creating target-state architectures, workload assessment frameworks, migration roadmaps, implementation priorities, and operating models.

• Establishing organizational responsibilities across cloud engineering, data engineering, analytics, security, governance, architecture, and business teams.

• Designing continuous optimization frameworks covering reliability, performance, cost, security, technology evolution, and enterprise data value.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.