Course Introduction
The Modern ETL and ELT Data Pipeline Engineering Training Course provides a comprehensive foundation for designing, developing, deploying, monitoring, and optimizing reliable data pipelines for modern analytical environments. Participants explore how traditional ETL approaches have evolved alongside cloud platforms, distributed processing, data lakes, lakehouses, cloud data warehouses, and increasingly complex enterprise data ecosystems.
This course examines the principles and engineering practices behind effective extract, transform, and load workflows, as well as modern extract, load, and transform architectures. Participants learn how to evaluate when ETL, ELT, streaming, event-driven, or hybrid pipeline patterns are appropriate and how to design data movement architectures that balance performance, scalability, reliability, maintainability, governance, and cost.
The Modern ETL and ELT Data Pipeline Engineering Training Course covers the complete lifecycle of data pipelines, from source discovery and ingestion through transformation, validation, orchestration, delivery, monitoring, and operational support. Participants explore batch and incremental processing, change data capture, schema evolution, data quality controls, dependency management, error handling, retries, and recovery strategies for enterprise data workloads.
Participants also learn how modern pipeline engineering integrates with cloud data warehouses, data lakes, lakehouses, open table formats, APIs, SaaS applications, databases, files, event streams, and external data services. The course emphasizes reusable architecture patterns and engineering practices that help organizations build scalable pipelines capable of supporting business intelligence, advanced analytics, machine learning, and AI applications.
Emerging topics are incorporated throughout the course, including real-time data pipelines, data contracts, pipeline observability, infrastructure as code, DataOps, metadata-driven pipelines, AI-assisted engineering, streaming analytics, and orchestration across distributed data platforms. Participants examine how these capabilities influence pipeline architecture, operational reliability, development productivity, and enterprise data management.
By completing the Modern ETL and ELT Data Pipeline Engineering Training Course, participants will be able to design and manage modern data pipelines using structured engineering principles and practical architectural approaches. The course equips professionals to improve data movement reliability, accelerate analytical data delivery, optimize pipeline performance, and establish sustainable practices for modern enterprise data engineering environments.
Duration
5 days
Who Should Attend
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Data engineers responsible for designing, developing, maintaining, and optimizing enterprise data pipelines.
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Analytics engineers building transformation workflows and analytical datasets for business intelligence and reporting.
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Cloud engineers supporting data ingestion, processing, orchestration, storage, and analytical infrastructure.
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Data architects designing enterprise ETL, ELT, streaming, and modern data integration architectures.
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Database administrators managing data movement between operational databases and analytical platforms.
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Data platform engineers responsible for pipeline infrastructure, orchestration, monitoring, reliability, and automation.
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Business intelligence professionals seeking deeper understanding of the engineering processes behind analytical data delivery.
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DevOps and DataOps professionals supporting deployment, automation, observability, and operational management of data pipelines.
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Machine learning and AI professionals requiring reliable and timely data pipelines for model development and production workloads.
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Data governance professionals involved in data quality, lineage, metadata, security, and compliance across pipeline environments.
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Technology managers overseeing data engineering modernization and enterprise analytics transformation initiatives.
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Consultants and solution architects designing scalable data integration and pipeline strategies for organizations.
Course Objectives
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Understand modern ETL and ELT principles and determine how different data pipeline patterns support analytical, operational, streaming, and AI-oriented workloads.
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Design scalable data pipeline architectures that integrate databases, APIs, files, cloud services, applications, event streams, and heterogeneous enterprise data sources.
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Apply practical techniques for data extraction, ingestion, transformation, loading, validation, enrichment, and delivery across modern analytical platforms.
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Build reliable batch and incremental pipelines using appropriate processing strategies, dependency management, scheduling, retries, checkpointing, and recovery mechanisms.
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Understand change data capture, schema evolution, data contracts, and incremental processing approaches for maintaining continuously evolving analytical datasets.
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Implement data quality controls that identify completeness, accuracy, consistency, timeliness, uniqueness, validity, and integrity issues throughout pipeline workflows.
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Apply orchestration and workflow management principles to coordinate complex pipelines, manage dependencies, automate execution, and improve operational reliability.
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Optimize pipeline performance, scalability, resource consumption, storage utilization, processing efficiency, and cloud costs across growing data engineering environments.
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Establish observability, monitoring, logging, alerting, lineage, and incident management practices that enable proactive detection and resolution of pipeline failures.
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Design modern DataOps-ready pipelines that support continuous delivery, automation, governance, real-time processing, AI workloads, and sustainable enterprise data engineering practices.
Comprehensive Course Outline
Module 1: Modern Data Pipeline Engineering Fundamentals
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Evolution of ETL and ELT architectures and their changing role in cloud-native and modern analytical data platforms.
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Core data pipeline components including sources, ingestion layers, transformation engines, storage platforms, orchestration, and consumption systems.
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Comparing ETL, ELT, reverse ETL, streaming, event-driven, and hybrid pipeline architectures for different enterprise workloads.
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Key principles for designing reliable, scalable, maintainable, observable, secure, and cost-efficient data pipeline environments.
Module 2: Data Sources, Extraction, and Ingestion
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Extracting data from relational databases, NoSQL platforms, APIs, SaaS applications, files, cloud services, and enterprise applications.
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Designing ingestion patterns for structured, semi-structured, unstructured, batch, incremental, and continuously generated data.
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Managing source connectivity, authentication, throttling, extraction windows, incremental loads, and reliable data transfer mechanisms.
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Addressing source-system limitations, network failures, duplicate records, late-arriving data, and inconsistent source structures.
Module 3: ETL and ELT Transformation Engineering
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Comparing transformation-before-loading and transformation-after-loading approaches across warehouses, lakes, lakehouses, and hybrid platforms.
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Designing modular transformation workflows using SQL, distributed processing, reusable logic, parameterization, and metadata-driven approaches.
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Applying cleansing, standardization, enrichment, aggregation, filtering, joining, deduplication, and business-rule transformations.
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Managing transformation dependencies, reusable components, testing requirements, version control, and changing business logic.
Module 4: Batch, Incremental, and Change Data Capture Pipelines
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Designing full-load and incremental ingestion strategies according to data volume, source capabilities, freshness requirements, and workload characteristics.
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Understanding change data capture techniques for identifying inserts, updates, deletes, and changes within operational source systems.
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Implementing watermarking, checkpoints, high-water marks, timestamps, sequence identifiers, and other incremental processing approaches.
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Managing late-arriving records, out-of-order events, duplicates, replay scenarios, historical corrections, and data reconciliation.
Module 5: Data Quality and Pipeline Reliability
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Establishing automated data quality checks for completeness, validity, accuracy, consistency, uniqueness, timeliness, and referential integrity.
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Designing validation frameworks that identify malformed records, unexpected values, missing data, schema changes, and business-rule violations.
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Implementing quarantine, rejection, remediation, retry, and recovery mechanisms for unreliable or invalid pipeline data.
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Building reconciliation and control processes to verify that source, pipeline, storage, and analytical outputs remain consistent.
Module 6: Workflow Orchestration and Pipeline Automation
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Understanding workflow orchestration concepts including scheduling, dependencies, task management, parallelism, retries, and conditional execution.
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Designing pipeline workflows that coordinate extraction, transformation, validation, loading, testing, and downstream analytical processes.
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Applying infrastructure automation, configuration management, version control, continuous integration, and continuous deployment practices.
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Managing environment promotion, parameterization, secrets, credentials, deployment dependencies, and operational consistency across platforms.
Module 7: Streaming and Real-Time Data Pipelines
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Understanding event-driven architecture, message brokers, streaming ingestion, stream processing, and continuously updated analytical datasets.
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Designing real-time and near-real-time pipelines for operational analytics, monitoring, personalization, fraud detection, and AI-enabled applications.
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Managing event ordering, delivery guarantees, checkpoints, replay, late events, duplicate events, and fault recovery in streaming environments.
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Integrating streaming pipelines with cloud warehouses, lakehouses, open table formats, analytical engines, and downstream applications.
Module 8: Pipeline Performance, Scalability, and Cost Optimization
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Identifying pipeline bottlenecks across extraction, network transfer, transformation, storage, orchestration, compute, and downstream analytical workloads.
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Applying partitioning, parallelism, batching, caching, incremental processing, file optimization, and workload management techniques.
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Designing elastic pipeline architectures that scale according to data volume, processing demand, freshness requirements, and workload variability.
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Establishing cost optimization practices covering compute consumption, storage utilization, data movement, processing frequency, and resource allocation.
Module 9: Data Pipeline Observability, Governance, and Security
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Designing pipeline observability using metrics, logs, traces, alerts, dashboards, lineage, and operational health indicators.
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Establishing data governance practices covering ownership, metadata, lineage, classification, retention, privacy, and lifecycle management.
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Implementing security controls for authentication, authorization, secrets management, encryption, sensitive data handling, and secure data movement.
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Developing incident management and operational response practices for detecting, diagnosing, resolving, documenting, and preventing pipeline failures.
Module 10: Emerging Data Pipeline Engineering Practices
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Exploring DataOps, data contracts, metadata-driven engineering, reusable pipeline frameworks, and automated data quality management.
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Understanding AI-assisted data engineering, intelligent pipeline monitoring, automated transformation development, and emerging engineering workflows.
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Designing pipelines that support machine learning, generative AI, feature engineering, vector data, unstructured content, and AI-ready analytical datasets.
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Developing continuous improvement strategies for modern pipeline platforms covering reliability, automation, governance, scalability, and evolving data workloads.
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.
