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Quest Training

ETL & Data Pipeline Development Training Course

Covers designing ETL and ELT workflows that extract data from databases, files and APIs, then clean, transform and load it reliably for analytics and reporting.

DWH · Data Warehousing & Data EngineeringAll Levels#2735ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Data, Analytics & Digital TransformationCategories:Data Warehousing & Data Engineering

Course Overview

This course develops practical skills in building reliable data pipelines that extract data from multiple sources, transform it into usable formats, and load it into data warehouses, databases, and analytical platforms. Participants learn how to design ETL and ELT workflows, automate data movement, manage dependencies, and deliver consistent datasets for reporting, business intelligence, and analytics. The programme covers data extraction methods, transformation logic, pipeline orchestration, incremental loading, data quality validation, error handling, and performance optimization. Participants also explore scheduling, monitoring, logging, and recovery techniques to improve pipeline reliability and maintainability. Through practical exercises, they develop end-to-end data pipelines that support recurring business reporting, data integration, and scalable analytical operations.

Learning Objectives

  • By the end of the course, participants will be able to:
  • Explain ETL and ELT architectures and their applications in modern data environments.
  • Identify suitable extraction methods for databases, files, APIs, and other data sources.
  • Design transformation workflows to clean, standardize, validate, and prepare data.
  • Develop pipelines that load data into target databases and analytical platforms.
  • Implement full and incremental loading strategies.
  • Automate pipeline execution using scheduling and orchestration techniques.
  • Apply data quality checks and reconciliation controls throughout pipeline workflows.
  • Implement error handling, logging, retry mechanisms, and recovery procedures.
  • Monitor pipeline performance and identify bottlenecks and recurring failures.
  • Design maintainable and scalable pipelines that support reliable analytical reporting.

Who Should Attend

This course is designed for data engineers, ETL developers, database administrators, data warehouse developers, BI developers, and data analysts responsible for data integration, transformation, and delivery. It is also suitable for IT professionals, analytics specialists, application integration teams, and technical consultants supporting enterprise reporting and data platforms. Professionals working in government entities, banks, financial institutions, oil and gas companies, industrial organizations, and large corporations will benefit from learning how to build dependable pipelines for operational, financial, and management data. Familiarity with SQL and relational databases is recommended.

Learning Outcomes

  • Upon completing the course, participants will be able to:
  • Differentiate between ETL and ELT workflows and select an appropriate approach for a given environment.
  • Map data from source systems to target structures and define transformation requirements.
  • Build extraction and loading workflows for structured and semi-structured data.
  • Implement data cleansing, standardization, type conversion, and validation rules.
  • Design incremental pipelines that process new or changed records efficiently.
  • Orchestrate dependent pipeline tasks and manage execution schedules.
  • Detect duplicate records, missing values, schema changes, and reconciliation discrepancies.
  • Configure logging, error handling, retries, and recovery procedures.
  • Evaluate pipeline execution metrics and identify performance improvement opportunities.
  • Document pipeline logic, dependencies, data quality rules, and operational procedures.

Course Outline

Day 1

ETL Architecture and Data Extraction Fundamentals

  • Understanding ETL and ELT concepts and their roles in data integration.
  • Comparing batch processing and streaming data pipelines.
  • Identifying data sources, including relational databases, files, APIs, and enterprise applications.
  • Understanding source schemas, data types, keys, and relationships.
  • Designing extraction workflows and defining source-to-target mappings.
  • Working with structured and semi-structured data formats such as CSV, JSON, and XML.
  • Understanding full extraction, incremental extraction, and change data capture concepts.
  • Identifying source-system constraints, access permissions, and data availability requirements.
  • Practical Exercise: Design a data extraction workflow that collects information from multiple sources and prepares it for downstream processing.
Day 2

Data Transformation, Cleansing, and Quality Controls

  • Designing transformation logic to standardize data across different sources.
  • Applying filtering, sorting, aggregation, joins, and calculated fields.
  • Handling missing values, duplicates, inconsistent formats, and invalid records.
  • Managing data types, date formats, reference values, and business rules.
  • Applying validation rules and defining acceptable data quality thresholds.
  • Implementing source-to-target reconciliation and record-count checks.
  • Managing schema differences and unexpected changes in source data.
  • Separating transformation logic into reusable and maintainable components.
  • Practical Exercise: Build a transformation workflow that cleans and combines datasets while applying validation and reconciliation controls.
Day 3

Pipeline Development, Loading Strategies, and Orchestration

  • Designing end-to-end data pipelines from extraction through transformation and loading.
  • Selecting suitable target environments, including databases, data warehouses, and analytical platforms.
  • Implementing full loads and incremental loading strategies.
  • Managing watermarks, change tracking, and processing checkpoints.
  • Understanding idempotency and preventing duplicate processing during reruns.
  • Organizing pipeline tasks and defining execution dependencies.
  • Applying scheduling and orchestration principles to coordinate workflow execution.
  • Managing configuration parameters, credentials, and environment-specific settings.
  • Practical Exercise: Develop a multi-stage pipeline that extracts source data, transforms it, loads the target system, and manages task dependencies.
Day 4

Pipeline Reliability, Monitoring, and Performance Optimization

  • Designing logging and monitoring mechanisms for pipeline execution.
  • Implementing exception handling, retries, failure notifications, and recovery procedures.
  • Identifying bottlenecks caused by inefficient transformations, data movement, and resource constraints.
  • Optimizing database queries and reducing unnecessary data processing.
  • Applying partitioning and parallel processing where appropriate.
  • Managing late-arriving data, partial failures, and interrupted loads.
  • Monitoring execution duration, throughput, failure rates, and data quality indicators.
  • Maintaining audit trails and operational documentation.
  • Applying access controls and protecting sensitive information during data movement.
  • Practical Exercise: Troubleshoot a pipeline with execution failures and performance issues, then implement corrective measures and monitoring checks.
Day 5

Production-Ready Pipelines and Integrated Implementation

  • Reviewing pipeline architecture for scalability, maintainability, and operational reliability.
  • Integrating extraction, transformation, validation, loading, and orchestration components.
  • Implementing reusable pipeline patterns for recurring reporting requirements.
  • Designing recovery procedures and safe rerun mechanisms.
  • Establishing operational standards for deployment, configuration, monitoring, and maintenance.
  • Validating end-to-end data completeness, accuracy, and consistency.
  • Preparing transformed datasets for reporting tools such as Power BI and Tableau.
  • Documenting pipeline dependencies, business rules, failure scenarios, and support procedures.
  • Final Workshop: Build and present an end-to-end data pipeline that integrates multiple sources, applies transformation and validation rules, loads a target analytical environment, and includes orchestration, monitoring, error handling, and recovery procedures.

Upcoming Dates

CityDatesPrice per ParticipantEarly Bird
GenevaScheduled
2 – 6 Nov 2026€6,300—
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HamburgScheduled
9 – 13 Nov 2026€5,800—
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IstanbulScheduled
16 – 20 Nov 2026€4,700—
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JeddahScheduled
22 – 26 Nov 2026€4,200€3,780until 2 Nov
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KigaliScheduled
30 Nov – 4 Dec 2026€5,100€4,590until 9 Nov
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Kuala LumpurScheduled
7 – 11 Dec 2026€4,400€3,960until 16 Nov
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LondonScheduled
14 – 18 Dec 2026€5,600€5,040until 23 Nov
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LisbonScheduled
14 – 18 Dec 2026€5,400€4,860until 23 Nov
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MadinahScheduled
21 – 25 Dec 2026€4,200€3,780until 30 Nov
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MadridScheduled
28 Dec 2026 – 1 Jan 2027€5,400€4,860until 7 Dec
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ManamaScheduled
4 – 8 Jan 2027€4,700€4,230until 14 Dec
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MarbellaScheduled
11 – 15 Jan 2027€5,400€4,860until 21 Dec
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MarrakeshScheduled
18 – 22 Jan 2027€4,300€3,870until 28 Dec
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MuscatScheduled
25 – 29 Jan 2027€5,300€4,770until 4 Jan
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MilanScheduled
25 – 29 Jan 2027€6,000€5,400until 4 Jan
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OnlineScheduled
7 – 11 Feb 2027€1,700€1,530until 18 Jan
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ParisScheduled
8 – 12 Feb 2027€5,700€5,130until 18 Jan
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RiyadhScheduled
15 – 19 Feb 2027€4,200€3,780until 25 Jan
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RomeScheduled
22 – 26 Feb 2027€5,800€5,220until 1 Feb
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SalalahScheduled
1 – 5 Mar 2027€5,300€4,770until 8 Feb
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Sharm El-SheikhScheduled
8 – 12 Mar 2027€4,300€3,870until 15 Feb
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ViennaScheduled
15 – 19 Mar 2027€5,700€5,130until 22 Feb
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AbujaScheduled
15 – 19 Mar 2027€5,100€4,590until 22 Feb
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AccraScheduled
22 – 26 Mar 2027€5,100€4,590until 1 Mar
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AmsterdamScheduled
29 Mar – 2 Apr 2027€5,400€4,860until 8 Mar
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BarcelonaScheduled
5 – 9 Apr 2027€5,400€4,860until 15 Mar
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CairoScheduled
12 – 16 Apr 2027€3,300€2,970until 22 Mar
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CasablancaScheduled
19 – 23 Apr 2027€4,300€3,870until 29 Mar
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DohaScheduled
26 – 30 Apr 2027€4,400€3,960until 5 Apr
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DubaiScheduled
2 – 6 May 2027€4,400€3,960until 12 Apr
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Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam

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