Course Introduction
The Serverless Data Analytics and Data Processing Training Course provides a practical foundation for designing, implementing, and optimizing modern analytics solutions using serverless computing and managed data processing technologies. Participants explore how serverless architectures can simplify infrastructure management while providing scalable processing capabilities for analytical workloads.
The course examines the principles of serverless data analytics, including event-driven execution, managed storage, elastic compute, automatic scaling, consumption-based pricing, stateless processing, and service integration. Participants learn how these principles can be applied to batch analytics, interactive querying, data transformation, real-time processing, and automated analytical workflows.
Participants will explore serverless data warehouses, object storage, serverless SQL engines, managed stream processing, event-driven functions, workflow services, and cloud-native orchestration. The training demonstrates how these services can be combined to create flexible data pipelines without requiring organizations to manage traditional server infrastructure.
The course also addresses practical data engineering requirements such as ingestion, transformation, partitioning, schema management, data quality, metadata, orchestration, monitoring, security, and access control. Participants learn how to build serverless processing workflows that remain reliable and maintainable while accommodating changing data volumes and analytical requirements.
Performance and cost optimization are central themes throughout the training. Participants examine query optimization, partition pruning, file formats, workload management, resource consumption, execution patterns, cold starts, data transfer, storage costs, and service selection. These considerations help participants understand how serverless architectures can be designed for both technical efficiency and sustainable operating costs.
Emerging serverless analytics practices are also incorporated, including event-driven data platforms, real-time analytics, lakehouse architectures, serverless AI workloads, data products, automated pipelines, and intelligent data processing. Participants gain a practical understanding of where serverless approaches fit within modern enterprise data architectures and how they can complement other cloud data technologies.
Duration
5 days
Who Should Attend
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Data engineers developing cloud-native and serverless analytical data pipelines.
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Data analysts seeking practical knowledge of serverless data processing and cloud analytics services.
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Cloud engineers responsible for implementing managed and event-driven data solutions.
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Analytics engineers building scalable transformation and analytical workflows.
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Business intelligence developers working with cloud-based analytical platforms.
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Solution architects designing serverless analytics and data processing architectures.
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Enterprise architects evaluating cloud-native approaches for analytical workloads.
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Database professionals transitioning from traditional infrastructure to managed cloud data services.
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Software developers integrating applications, APIs, events, and serverless data processing workflows.
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Technology managers evaluating serverless platforms for analytics modernization initiatives.
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Data platform engineers building scalable data lakes, warehouses, and analytical services.
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Machine learning and AI practitioners requiring scalable serverless data preparation and processing capabilities.
Course Objectives
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Understand serverless computing principles and apply them to scalable data analytics, processing, ingestion, transformation, and analytical application architectures.
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Design serverless data architectures using managed storage, query engines, event-driven functions, orchestration services, and cloud-native processing components.
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Build reliable serverless data pipelines capable of handling batch workloads, incremental processing, event-driven ingestion, and near-real-time analytical requirements.
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Apply serverless SQL and analytical processing techniques to query large datasets efficiently while minimizing infrastructure administration and operational overhead.
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Optimize serverless data workloads through appropriate partitioning, file formats, data organization, query design, caching, and workload-specific processing strategies.
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Implement data quality, schema management, validation, metadata, lineage, monitoring, and observability practices across serverless analytical pipelines.
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Apply cloud security principles including identity and access management, encryption, network controls, secrets protection, and fine-grained access to analytical data.
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Evaluate serverless architectures according to scalability, latency, reliability, operational complexity, vendor dependencies, workload characteristics, and cost considerations.
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Integrate serverless analytics with data lakes, warehouses, streaming services, APIs, event platforms, business intelligence tools, and modern AI data workflows.
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Develop practical strategies for extending serverless data processing toward real-time analytics, lakehouse architectures, machine learning, AI applications, and automated enterprise data platforms.
Comprehensive Course Outline
Module 1: Foundations of Serverless Data Analytics
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Understand serverless computing principles and how managed services change traditional approaches to data processing and analytics.
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Explore automatic scaling, event-driven execution, stateless processing, managed infrastructure, consumption-based pricing, and operational abstraction.
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Examine suitable serverless workloads including analytical queries, transformations, ingestion, scheduled processing, event handling, and data automation.
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Compare serverless architectures with provisioned infrastructure and evaluate practical trade-offs involving control, performance, cost, and operational responsibility.
Module 2: Serverless Data Storage and Analytical Platforms
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Explore object storage, serverless data warehouses, cloud databases, serverless SQL engines, and managed analytical storage technologies.
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Understand how storage architecture, file organization, partitioning, compression, and metadata influence serverless query performance and cost.
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Design analytical storage layers that organize raw, processed, curated, and consumption-ready data for different business and analytical requirements.
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Evaluate storage options based on data volume, access patterns, latency requirements, durability, scalability, governance, and workload characteristics.
Module 3: Serverless Data Ingestion and Integration
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Design event-driven and scheduled ingestion workflows that collect information from databases, files, APIs, applications, and external data services.
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Explore serverless functions, managed connectors, message queues, event buses, notifications, and integration services for data movement.
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Implement incremental ingestion, change data capture, deduplication, checkpointing, validation, and failure recovery techniques within serverless pipelines.
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Address ingestion challenges involving schema evolution, source system failures, late-arriving records, duplicate events, and inconsistent source data.
Module 4: Serverless Data Transformation and Processing
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Apply serverless ETL and ELT approaches to transform raw information into validated and reusable analytical datasets.
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Explore distributed data processing, serverless SQL transformations, filtering, joins, aggregations, enrichment, and business rule implementation.
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Design event-driven processing workflows that automatically transform data when new files, records, events, or business triggers become available.
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Compare processing approaches based on data size, transformation complexity, execution duration, latency requirements, and operational characteristics.
Module 5: Serverless Workflow Orchestration
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Understand serverless workflow orchestration concepts including triggers, dependencies, scheduling, retries, state management, and failure handling.
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Design automated workflows that coordinate ingestion, transformation, validation, publishing, notification, and downstream analytical activities.
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Explore event-driven orchestration and scheduled execution patterns for recurring and continuously triggered data processing requirements.
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Implement workflow monitoring, logging, alerting, recovery, and operational controls that improve reliability and maintainability.
Module 6: Serverless Analytics and Query Optimization
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Develop efficient serverless analytical queries using appropriate filtering, joins, aggregations, partition pruning, and data access strategies.
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Explore columnar storage, compression, file formats, partitioning, clustering, and data layout techniques for reducing analytical processing overhead.
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Analyze query execution patterns and identify inefficient scans, unnecessary data movement, expensive transformations, and poorly structured analytical workloads.
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Apply optimization techniques that improve query performance while controlling compute consumption and serverless analytics costs.
Module 7: Security, Governance, and Data Quality
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Implement identity and access management principles that provide appropriate permissions to serverless functions, services, users, and analytical resources.
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Explore encryption, secrets management, secure service communication, network controls, data classification, and access policies for serverless data environments.
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Establish data quality controls covering completeness, accuracy, consistency, uniqueness, validity, freshness, and business-critical analytical requirements.
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Integrate metadata, lineage, auditing, retention, governance, and compliance practices into serverless analytics architectures and workflows.
Module 8: Real-Time and Event-Driven Analytics
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Explore serverless architectures for real-time and near-real-time analytics using events, streams, functions, managed processing, and analytical storage.
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Design pipelines that combine streaming events with historical data to support continuously updated dashboards, monitoring, and operational intelligence.
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Address event ordering, duplicate events, replay, checkpointing, latency, fault tolerance, and schema evolution in serverless streaming workflows.
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Examine practical use cases including application monitoring, customer behavior analysis, IoT analytics, fraud detection, and real-time business intelligence.
Module 9: Cost, Scalability, and Operational Optimization
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Understand the major cost drivers of serverless analytics including query execution, storage, data transfer, function invocations, processing duration, and managed services.
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Design workloads that take advantage of automatic scaling while avoiding unnecessary invocations, excessive data scans, inefficient transformations, and uncontrolled resource consumption.
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Establish monitoring and cost governance practices that provide visibility into serverless workload performance, utilization, spending, and operational trends.
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Balance scalability, reliability, latency, performance, operational simplicity, and cost when selecting serverless technologies for different analytical requirements.
Module 10: Emerging Serverless Analytics and Practical Implementation
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Explore serverless lakehouse architectures, event-driven data platforms, data products, automated analytical pipelines, and modern cloud-native processing patterns.
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Examine serverless approaches for machine learning, AI data preparation, retrieval workflows, vector processing, and other emerging analytical workloads.
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Investigate architectural considerations involving multi-cloud environments, portability, vendor dependencies, observability, service integration, and platform evolution.
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Develop practical implementation roadmaps for introducing serverless analytics into enterprise environments while managing migration, governance, skills, and operational change.
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.
