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Quest Training

Big Data Fundamentals & Analytics Training Course

Explains Big Data characteristics, sources, distributed storage and processing architectures and the analytics that turn large datasets into business insight.

BD · Big Data & Modern Data TechnologiesAll Levels#2554ClassroomEnglish , 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 Big Data concepts, technologies, architectures, and analytics methods used to transform large and complex datasets into actionable business and operational insights. The programme focuses on how organizations can manage data at scale and use analytics to improve decision-making. Participants will examine the characteristics of Big Data, data sources, storage and processing architectures, distributed computing concepts, data quality, and the analytics lifecycle. The course emphasizes the relationship between data infrastructure, analytical methods, and business requirements. The programme also develops practical capabilities in descriptive, diagnostic, predictive, and advanced analytics, with emphasis on identifying patterns, trends, relationships, and performance drivers within large datasets. Through practical exercises and applied scenarios, participants will learn how to frame Big Data problems, select appropriate analytical approaches, interpret results, and translate findings into business and operational actions.

Learning Objectives

  • Analyse the fundamental concepts, characteristics, and business value of Big Data.
  • Identify major Big Data sources and understand different data types and structures.
  • Evaluate the key components of Big Data architectures and data processing environments.
  • Understand distributed data storage and processing concepts.
  • Assess data quality, governance, security, and lifecycle considerations in Big Data environments.
  • Apply structured approaches to Big Data collection, preparation, and integration.
  • Select appropriate analytical techniques according to business and operational requirements.
  • Apply descriptive and diagnostic analytics to identify patterns and performance drivers.
  • Explore predictive analytics approaches for forecasting and risk identification.
  • Interpret analytical results and communicate data-driven insights effectively.
  • Evaluate the role of advanced analytics and machine learning within Big Data initiatives.
  • Develop practical Big Data analytics approaches that support organizational decision-making.

Who Should Attend

This course is designed for Data Analysts, Business Analysts, Data Scientists, BI professionals, IT Managers, Data Engineers, Digital Transformation specialists, and professionals involved in data-driven decision-making. It is also suitable for managers and specialists in operations, finance, marketing, risk, quality, supply chain, government, and other functions seeking to understand how Big Data and analytics can support organizational performance. The programme is particularly relevant to organizations developing data-driven strategies, digital transformation initiatives, analytics capabilities, or enterprise data management practices.

Learning Outcomes

  • Explain the fundamental concepts and characteristics of Big Data.
  • Identify and classify structured, semi-structured, and unstructured data sources.
  • Describe the main components of modern Big Data architectures.
  • Evaluate data storage and processing approaches for large-scale datasets.
  • Identify key data quality and governance challenges.
  • Prepare and structure large datasets for analytical use.
  • Select appropriate analytical methods for different business problems.
  • Apply descriptive and diagnostic approaches to uncover meaningful patterns.
  • Interpret predictive analytics outputs and assess their business relevance.
  • Evaluate how advanced analytics and machine learning can enhance Big Data initiatives.
  • Communicate analytical findings to technical and non-technical stakeholders.
  • Develop a practical framework for applying Big Data analytics to an organizational use case.

Course Outline

Day 1

Big Data Fundamentals, Sources & Business Value

  • Definition and evolution of Big Data.
  • The key characteristics of Big Data and their business implications.
  • Structured, semi-structured, and unstructured data.
  • Internal and external Big Data sources.
  • Transactional, sensor, social, machine-generated, and operational data.
  • Data volumes, velocity, variety, quality, and complexity.
  • Big Data use cases across business and public-sector environments.
  • Business challenges and opportunities associated with Big Data.
  • Defining analytical objectives and business questions.
  • Practical Application: Analyse a business scenario, identify relevant Big Data sources, and define the analytical questions that should be addressed.
Day 2

Big Data Architecture, Storage & Processing

  • Components of modern Big Data environments.
  • Centralized versus distributed data architectures.
  • Data lakes, data warehouses, and modern data platforms.
  • Distributed storage and processing principles.
  • Batch processing and real-time processing.
  • Data ingestion and integration approaches.
  • Data pipelines and large-scale data preparation.
  • Scalability, availability, and performance considerations.
  • Security and access considerations in Big Data environments.
  • Practical Application: Design a conceptual Big Data architecture for a selected organizational use case.
Day 3

Data Preparation, Quality & Analytics Foundations

  • Data collection and integration strategies.
  • Data cleaning and preparation for large datasets.
  • Handling missing, inconsistent, duplicated, and anomalous data.
  • Data transformation and feature preparation.
  • Data quality dimensions and validation.
  • Exploratory data analysis.
  • Descriptive statistics and data visualization.
  • Identifying patterns, trends, relationships, and anomalies.
  • Selecting analytical methods according to business objectives.
  • Practical Application: Prepare and explore a large dataset to identify quality issues, significant patterns, and potential analytical opportunities.
Day 4

Big Data Analytics, Predictive Methods & Advanced Insights

  • Descriptive, diagnostic, predictive, and prescriptive analytics.
  • Identifying drivers of business and operational performance.
  • Correlation and relationship analysis.
  • Predictive modelling concepts.
  • Forecasting and trend analysis.
  • Classification and clustering concepts.
  • Introduction to machine learning within Big Data environments.
  • Evaluating analytical model results.
  • Translating analytical findings into business decisions.
  • Practical Application: Analyse a selected dataset, identify key drivers, and develop predictive or advanced analytical insights.
Day 5

Big Data Analytics Strategy, Governance & Decision-Making

  • Building an organizational Big Data analytics framework.
  • Aligning analytics initiatives with business strategy.
  • Data governance and responsible data management.
  • Data security, privacy, and access considerations.
  • Defining analytical performance indicators.
  • Communicating Big Data insights to decision-makers.
  • Managing analytical projects and implementation challenges.
  • Identifying opportunities for automation and advanced analytics.
  • Establishing continuous improvement for data and analytics capabilities.
  • Final Workshop: Develop an integrated Big Data analytics roadmap for a selected organizational use case, covering business objectives, data sources, architecture, analytical approach, governance, expected insights, and implementation priorities.

Upcoming Dates

Selected date

Vienna · 8 – 12 Mar 2027 · Classroom

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CityDatesPrice per ParticipantEarly Bird
BangkokScheduled
2 – 6 Nov 2026€6,100—
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FrankfurtScheduled
9 – 13 Nov 2026€5,800—
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GenevaScheduled
16 – 20 Nov 2026€6,300—
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BeirutScheduled
22 – 26 Nov 2026€4,000€3,600until 2 Nov
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IstanbulScheduled
23 – 27 Nov 2026€4,700€4,230until 2 Nov
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Kuala LumpurScheduled
30 Nov – 4 Dec 2026€4,400€3,960until 9 Nov
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LisbonScheduled
30 Nov – 4 Dec 2026€5,400€4,860until 9 Nov
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CairoScheduled
6 – 10 Dec 2026€3,300€2,970until 16 Nov
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LondonScheduled
7 – 11 Dec 2026€5,600€5,040until 16 Nov
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MadinahScheduled
14 – 18 Dec 2026€4,200€3,780until 23 Nov
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MadridScheduled
21 – 25 Dec 2026€5,400€4,860until 30 Nov
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Cape TownScheduled
28 Dec 2026 – 1 Jan 2027€5,100€4,590until 7 Dec
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MakkahScheduled
28 Dec 2026 – 1 Jan 2027€4,200€3,780until 7 Dec
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ManamaScheduled
4 – 8 Jan 2027€4,700€4,230until 14 Dec
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MarrakeshScheduled
11 – 15 Jan 2027€4,300€3,870until 21 Dec
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MarbellaScheduled
11 – 15 Jan 2027€5,400€4,860until 21 Dec
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MilanScheduled
18 – 22 Jan 2027€6,000€5,400until 28 Dec
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CasablancaScheduled
18 – 22 Jan 2027€4,300€3,870until 28 Dec
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MunichScheduled
25 – 29 Jan 2027€5,400€4,860until 4 Jan
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MuscatScheduled
1 – 5 Feb 2027€5,300€4,770until 11 Jan
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OnlineScheduled
7 – 11 Feb 2027€1,700€1,530until 18 Jan
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DammamScheduled
7 – 11 Feb 2027€4,200€3,780until 18 Jan
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ParisScheduled
15 – 19 Feb 2027€5,700€5,130until 25 Jan
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DohaScheduled
21 – 25 Feb 2027€4,400€3,960until 1 Feb
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RiyadhScheduled
22 – 26 Feb 2027€4,200€3,780until 1 Feb
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RomeScheduled
1 – 5 Mar 2027€5,800€5,220until 8 Feb
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SalalahScheduled
1 – 5 Mar 2027€5,300€4,770until 8 Feb
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ViennaScheduled
8 – 12 Mar 2027€5,700€5,130until 15 Feb
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ErbilScheduled
14 – 18 Mar 2027€3,100€2,790until 22 Feb
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AmmanScheduled
14 – 18 Mar 2027€3,100€2,790until 22 Feb
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