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Quest Training

Data Lake & Lakehouse Architecture Training Course

Compares data lakes, warehouses and lakehouses and shows how to design scalable platforms for ingesting, storing, governing and analysing diverse data.

BD · Big Data & Modern Data TechnologiesAll Levels#2555ClassroomEnglish , 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:Big Data & Modern Data Technologies

Course Overview

This advanced practical course provides a structured understanding of Data Lake and Lakehouse architectures and their role in modern enterprise data platforms. It focuses on designing scalable environments capable of storing, processing, governing, and analysing large volumes of structured, semi-structured, and unstructured data. Participants will examine the architectural principles behind Data Lakes and Lakehouses, including data ingestion, storage layers, metadata management, data processing, security, governance, data quality, and analytical access. The programme emphasizes architectural decisions based on organizational requirements rather than technology selection alone. The course also explores how Lakehouse architectures combine the scalability and flexibility of Data Lakes with the reliability, governance, and analytical capabilities traditionally associated with data warehouses. Participants will evaluate different architectural patterns and understand how to support batch, streaming, business intelligence, data science, and machine learning workloads. Through practical scenarios, participants will design conceptual architectures, define data flows, identify governance requirements, address security and performance considerations, and develop implementation approaches for enterprise Data Lake and Lakehouse environments.

Learning Objectives

  • Analyse the principles and business value of Data Lake and Lakehouse architectures.
  • Distinguish between Data Lakes, Data Warehouses, and Lakehouse platforms.
  • Evaluate architectural components required for scalable data platforms.
  • Design data ingestion and integration approaches for diverse data sources.
  • Develop appropriate storage and data organization strategies.
  • Apply concepts for batch and streaming data processing.
  • Evaluate metadata, cataloguing, lineage, and data discovery requirements.
  • Design security, access control, and governance mechanisms.
  • Address data quality, lifecycle management, and operational challenges.
  • Evaluate performance, scalability, reliability, and cost considerations.
  • Integrate Data Lake and Lakehouse architectures with analytics, business intelligence, and machine learning workloads.
  • Develop practical architecture and implementation roadmaps aligned with organizational requirements.

Who Should Attend

This course is designed for Data Architects, Data Engineers, Solution Architects, Data Platform Engineers, Cloud Architects, Analytics Managers, Data Scientists, and IT professionals involved in enterprise data platform design and implementation. It is also suitable for managers and specialists responsible for data governance, business intelligence, digital transformation, analytics, cloud infrastructure, data management, and enterprise technology strategy. The programme is particularly relevant to organizations modernizing their data infrastructure, consolidating diverse data sources, developing advanced analytics capabilities, or moving from traditional warehouse-centric environments toward scalable modern data architectures.

Learning Outcomes

  • Explain the architecture and purpose of modern Data Lake and Lakehouse platforms.
  • Compare Data Lake, Data Warehouse, and Lakehouse approaches for different use cases.
  • Identify the major architectural components of an enterprise data platform.
  • Design scalable data ingestion and integration patterns.
  • Structure data storage according to analytical and operational requirements.
  • Define suitable approaches for batch and streaming data processing.
  • Establish metadata, cataloguing, lineage, and data discovery practices.
  • Design appropriate security, access, and governance controls.
  • Identify and address data quality and lifecycle management challenges.
  • Evaluate architectural options based on performance, scalability, reliability, and cost.
  • Integrate Lakehouse environments with analytics, business intelligence, data science, and machine learning.
  • Develop a practical target architecture and implementation roadmap for a selected organizational use case.

Course Outline

Day 1

Data Lake & Lakehouse Foundations and Architectural Principles

  • Evolution of enterprise data architectures.
  • Data Lakes, Data Warehouses, and Lakehouse concepts.
  • Architectural differences, strengths, and limitations.
  • Structured, semi-structured, and unstructured data.
  • Core components of modern data platforms.
  • Data zones and logical data organization.
  • Data ingestion and storage principles.
  • Analytical and operational workload requirements.
  • Selecting architectural patterns based on business needs.
  • Practical Application: Assess an existing data environment and develop initial requirements for a Data Lake or Lakehouse architecture.
Day 2

Data Ingestion, Storage & Processing Architecture

  • Data ingestion patterns and source integration.
  • Batch and real-time data ingestion.
  • Streaming data architecture concepts.
  • Data storage organization and partitioning.
  • File formats and data organization principles.
  • Data processing and transformation layers.
  • Distributed processing concepts.
  • Data pipelines and orchestration.
  • Scalability and performance considerations.
  • Practical Application: Design an end-to-end data ingestion and processing flow for a selected organizational use case.
Day 3

Lakehouse Data Management, Metadata & Data Quality

  • Lakehouse data organization and management.
  • Metadata management and data cataloguing.
  • Data lineage and data discovery.
  • Data quality management within modern data platforms.
  • Data validation and monitoring.
  • Schema management and controlled data evolution.
  • Data lifecycle and retention strategies.
  • Managing raw, refined, and analytical data layers.
  • Supporting trusted and reusable analytical datasets.
  • Practical Application: Develop a data organization, metadata, and quality framework for a conceptual Lakehouse environment.
Day 4

Security, Governance, Performance & Reliability

  • Data security architecture for Data Lakes and Lakehouses.
  • Identity and access management.
  • Role-based and policy-based access controls.
  • Data protection and privacy considerations.
  • Governance frameworks and accountability.
  • Monitoring and operational management.
  • Performance optimization and workload management.
  • Scalability, availability, and resilience.
  • Cost management and resource optimization.
  • Practical Application: Develop security, governance, performance, and reliability controls for a selected Lakehouse architecture.
Day 5

Analytics Integration, Architecture Strategy & Implementation

  • Integrating Lakehouse platforms with business intelligence.
  • Supporting data science and machine learning workloads.
  • Analytical data access and consumption patterns.
  • Building reusable and trusted data products.
  • Architectural integration with enterprise systems.
  • Evaluating migration and modernization strategies.
  • Defining target-state architecture.
  • Developing implementation phases and priorities.
  • Managing architectural risks and adoption challenges.
  • Final Workshop: Design and present a complete Data Lake or Lakehouse target architecture covering data sources, ingestion, storage, processing, governance, security, analytics integration, performance requirements, and implementation roadmap.

Upcoming Dates

Selected date

Kuala Lumpur · 23 – 27 Nov 2026 · Classroom

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CityDatesPrice per ParticipantEarly Bird
FrankfurtScheduled
2 – 6 Nov 2026€5,800—
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GenevaScheduled
9 – 13 Nov 2026€6,300—
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Cape TownScheduled
16 – 20 Nov 2026€5,100—
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IstanbulScheduled
16 – 20 Nov 2026€4,700—
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Kuala LumpurScheduled
23 – 27 Nov 2026€4,400€3,960until 2 Nov
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LisbonScheduled
30 Nov – 4 Dec 2026€5,400€4,860until 9 Nov
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LondonScheduled
30 Nov – 4 Dec 2026€5,600€5,040until 9 Nov
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CasablancaScheduled
7 – 11 Dec 2026€4,300€3,870until 16 Nov
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MadinahScheduled
7 – 11 Dec 2026€4,200€3,780until 16 Nov
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MadridScheduled
14 – 18 Dec 2026€5,400€4,860until 23 Nov
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MakkahScheduled
21 – 25 Dec 2026€4,200€3,780until 30 Nov
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DammamScheduled
27 – 31 Dec 2026€4,200€3,780until 7 Dec
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ManamaScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
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MarbellaScheduled
4 – 8 Jan 2027€5,400€4,860until 14 Dec
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MilanScheduled
11 – 15 Jan 2027€6,000€5,400until 21 Dec
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MarrakeshScheduled
11 – 15 Jan 2027€4,300€3,870until 21 Dec
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DohaScheduled
17 – 21 Jan 2027€4,400€3,960until 28 Dec
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MunichScheduled
18 – 22 Jan 2027€5,400€4,860until 28 Dec
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MuscatScheduled
25 – 29 Jan 2027€5,300€4,770until 4 Jan
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OnlineScheduled
31 Jan – 4 Feb 2027€1,700€1,530until 11 Jan
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DubaiScheduled
7 – 11 Feb 2027€4,400€3,960until 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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ErbilScheduled
21 – 25 Feb 2027€3,100€2,790until 1 Feb
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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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ViennaScheduled
1 – 5 Mar 2027€5,700€5,130until 8 Feb
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AmmanScheduled
7 – 11 Mar 2027€3,100€2,790until 15 Feb
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JeddahScheduled
14 – 18 Mar 2027€4,200€3,780until 22 Feb
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AmsterdamScheduled
15 – 19 Mar 2027€5,400€4,860until 22 Feb
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Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam

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