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Big Data & Data Engineering

Big Data & Data Engineering

1 Weeks
Professional Level
Certificate Included
Course Content

Available Events

Available Events
CityDatePriceStatus
SingaporeSingapore
October 5, 2026
€4,800
Confirmed date
DubaiUnited Arab Emirates
October 8, 2026
€3,900
Confirmed date
LondonUnited Kingdom
October 15, 2026
£4,600
Confirmed date
GenevaSwitzerland
October 20, 2026
€4,600
Confirmed date
AmsterdamNetherlands
October 25, 2026
€4,600
Confirmed date
LisbonPortugal
October 29, 2026
€4,400
Confirmed date
TunisTunis
November 1, 2026
€3,900
Confirmed date
ParisFrance
November 5, 2026
€4,600
Confirmed date
IstanbulTurkey
November 19, 2026
€3,900
Confirmed date

Course Information

Duration

1 Weeks

Category

Training & Development

Level

Professional Level

Certificate

Included

INTRODUCTION

Big Data and Data Engineering have become essential disciplines for organizations that need to manage growing data volumes, increasing data variety, and faster decision cycles. Modern enterprises rely on well-designed data platforms to convert raw operational information into accurate, accessible, and actionable intelligence. Without effective data engineering, analytics initiatives often suffer from poor data quality, slow processing, fragmented systems, and unreliable reporting. This course provides a practical and strategic foundation for understanding how large-scale data environments are planned, implemented, governed, and improved. Participants will examine the relationship between business goals, data architecture, cloud infrastructure, processing frameworks, and analytics delivery. The program explains how batch processing, real-time streaming, data modeling, metadata management, and workflow orchestration work together in enterprise data ecosystems. It also highlights the importance of security, compliance, documentation, monitoring, and cost control in professional data engineering practice. The course is designed for professionals who need to understand both the technical language and business value of big data solutions. By the end of the program, participants will be prepared to support scalable, reliable, and business-aligned data engineering initiatives.

COURSE OBJECTIVES

Participants will achieve the following objectives by this course:

  • Understand the strategic role of Big Data and Data Engineering in modern organizations.
  • Identify core components of scalable enterprise data platforms and analytics ecosystems.
  • Design reliable data pipelines for ingestion, transformation, storage, and delivery.
  • Compare data lakes, data warehouses, lakehouses, and distributed processing architectures.
  • Apply best practices for data quality, validation, lineage, and metadata management.
  • Evaluate batch processing, stream processing, and real-time analytics use cases.
  • Understand cloud-based data engineering services and modern deployment considerations.
  • Strengthen governance, security, privacy, and compliance across data engineering workflows.
  • Monitor, optimize, and troubleshoot data pipelines for performance and reliability.
  • Align data engineering initiatives with business intelligence, analytics, and artificial intelligence goals.

TARGET AUDIENCE

This program targets a professional audience seeking to improve knowledge and skills:

  • Data engineers, database professionals, business intelligence specialists, analytics managers, information technology leaders, cloud engineers, software developers, data analysts, transformation consultants, project managers, and technical team leaders responsible for data platforms, reporting environments, integration workflows, or digital transformation initiatives.
  • Professionals involved in modernizing legacy data systems, improving data quality, supporting artificial intelligence readiness, managing enterprise analytics, selecting big data technologies, or coordinating data-driven projects across business and technical departments.
  • Executives and managers who need practical understanding of Big Data, Data Engineering, scalable architecture, governance, security, and operational requirements for successful enterprise data programs.

COURSE OUTLINE

Day 1: Foundations of Big Data and Data Engineering

  • Understanding big data characteristics, value, challenges, and enterprise use cases.
  • Exploring data engineering roles, responsibilities, and professional workflow expectations.
  • Reviewing structured, semi-structured, and unstructured data sources.
  • Comparing operational databases, analytical platforms, and distributed data systems.
  • Understanding data pipelines from ingestion to consumption.
  • Identifying key stakeholders across analytics and technology teams.
  • Mapping business requirements to data engineering capabilities.
  • Reviewing common failures in enterprise data initiatives.

Day 2: Data Architecture, Storage, and Platform Design

  • Exploring data lakes, warehouses, lakehouses, and hybrid architectures.
  • Understanding distributed storage concepts and scalability principles.
  • Designing data zones for raw, curated, and trusted information.
  • Comparing relational, NoSQL, columnar, and object storage models.
  • Reviewing cloud data platform design and service selection.
  • Understanding schema design, partitioning, and data modeling foundations.
  • Managing metadata, catalogs, lineage, and discoverability.
  • Aligning architecture decisions with cost, governance, and performance.

Day 3: Data Pipelines, Processing, and Transformation

  • Designing ingestion workflows for batch and near-real-time data.
  • Understanding extraction, transformation, and loading patterns.
  • Building reusable, modular, and maintainable data pipeline logic.
  • Applying data cleansing, validation, enrichment, and standardization techniques.
  • Exploring distributed processing frameworks and scalable transformation approaches.
  • Managing dependencies, scheduling, orchestration, and workflow automation.
  • Handling errors, retries, exceptions, and pipeline recovery.
  • Documenting pipeline logic for transparency and operational continuity.

Day 4: Streaming, Governance, Security, and Quality

  • Understanding streaming data concepts and real-time analytics scenarios.
  • Comparing event-driven architectures with traditional batch processing.
  • Applying data quality rules, monitoring, profiling, and anomaly detection.
  • Implementing access control, encryption, masking, and privacy safeguards.
  • Managing compliance requirements across regulated data environments.
  • Establishing governance policies for ownership, stewardship, and accountability.
  • Tracking lineage and audit trails across data workflows.
  • Reducing operational risk through secure engineering practices.

Day 5: Optimization, Operations, and Enterprise Implementation

  • Monitoring pipeline performance, reliability, availability, and service levels.
  • Optimizing storage, compute, query performance, and processing costs.
  • Applying testing strategies for data pipelines and transformation logic.
  • Managing deployment, version control, environments, and release practices.
  • Supporting analytics, reporting, machine learning, and artificial intelligence use cases.
  • Building operational dashboards for data platform health.
  • Planning roadmap priorities for data engineering maturity.
  • Developing action plans for enterprise implementation and continuous improvement.

COURSE DURATION

Duration: 1 Weeks

The recommended duration for this Big Data and Data Engineering course is five intensive training days, delivered through classroom, online, or blended learning formats depending on organizational needs. The program can be adapted for executive awareness, technical practitioner development, or cross-functional data transformation teams. Each day combines conceptual explanation, practical discussion, enterprise examples, architecture review, guided exercises, and applied planning activities to ensure participants understand both strategic value and technical implementation requirements. Organizations may extend the course with hands-on labs, case studies, platform-specific demonstrations, assessment activities, or project-based assignments according to participant background and business objectives.

INSTRUCTOR INFORMATION

The training will be delivered by a team of experts specialized in data engineering, big data architecture, cloud data platforms, analytics enablement, governance, and enterprise technology transformation. Instructors bring practical experience in designing scalable data pipelines, modernizing data infrastructure, implementing data quality frameworks, supporting business intelligence environments, and aligning technical solutions with executive priorities. The delivery approach combines professional instruction, real-world examples, interactive discussions, technical interpretation, and business-focused guidance to help participants understand how Big Data and Data Engineering practices create measurable organizational value.

FREQUENTLY ASKED QUESTIONS

Yes, it explains technical concepts in a business-oriented way while still covering professional engineering principles.

CONCLUSION

This Big Data and Data Engineering training course provides a strong professional foundation for building scalable, secure, and reliable data ecosystems. Participants gain practical understanding of modern data platforms, pipelines, storage models, governance controls, and operational excellence. The program helps organizations strengthen analytics capability, improve data quality, and support artificial intelligence and digital transformation initiatives. By connecting technical implementation with business priorities, the course prepares professionals to contribute effectively to enterprise data modernization. It is a valuable investment for teams seeking long-term data maturity and competitive advantage.

Big Data & Data Engineering

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