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Big data and data security: securing large-scale architectures
Big data architectures must protect information while supporting analytical use. Identify risks and connect security measures to flows, access and processing. Develop a technical approach to integrating data protection into architectural decisions.
- Duration
- 2 days 14 hours
- Code
- DATA011FR Code
Presentation
In today's workplace, using big data provides a major competitive advantage but also greatly expands organisations' attack surfaces. Distributed architectures and vast information volumes require traditional defences to be adapted to these environments. This course equips you to turn big data clusters into resilient infrastructure protecting strategic information assets.
Explore vulnerabilities specific to Hadoop and Spark ecosystems and their potential failure points. Develop skills in encrypting data at rest and in transit, and fine-grained access management across complex clusters. Learning centres on attack simulations and system hardening workshops to ensure processing environments' integrity and confidentiality.
After two intensive days, you will have a rigorous methodology for integrating regulatory compliance and ISO standards into data projects. Diagnose big data architecture vulnerabilities and deploy an effective, comprehensive security plan. Leave with operational best practices for securely managing large-scale infrastructure.
Objectives
By the end of this course, you will be able to:
- understand specific risks and technical challenges in big data environments;
- identify attack vectors and critical vulnerabilities in distributed architectures;
- deploy protection and encryption mechanisms suited to big data flows;
- integrate GDPR requirements and ISO standards into data governance;
- develop a comprehensive security strategy for a big data technology project.
Program
Module 1: Analysing big data risks and challenges
- Specific characteristics and major challenges of large-scale data processing.
- Threat types: data theft, insider compromise and ransomware.
- Analysing a cyberattack's actual impact on big data service continuity.
Hands-on exercises
- Analyse a Hadoop cluster intrusion scenario to identify compromise stages.
Module 2: Auditing architecture and vulnerabilities
- Examining critical ecosystem components: HDFS, YARN and Spark.
- Identifying sensitive areas: storage nodes, processing engines and transmission flows.
- Assessing risks from system interconnection and API use.
Hands-on exercises
- Create a comprehensive vulnerability map for a typical distributed architecture.
Module 3: Implementing technical data protection
- Deploying encryption for data at rest and in transit.
- Centralised encryption key and security certificate management.
- Configuring strong authentication and granular access control tools.
Hands-on exercises
- Implement file encryption directly on HDFS.
Module 4: Managing regulatory compliance and governance
- Applying GDPR obligations to large-scale processing.
- Using ISO 27001 and ISO 27701 to structure security.
- Implementing traceability processes and data governance monitoring.
Case study
- Analyse a GDPR non-compliance incident to identify necessary corrective measures.
Module 5: Securing and monitoring big data infrastructure
- Server hardening and cluster node isolation.
- Active monitoring and detection of behavioural anomalies in flows.
- Organising incident response and developing contingency plans.
Hands-on exercises
- Simulate a cyberattack and follow the incident response plan.
Module 6: Applying best practices and isolation
- Environment segmentation and logical isolation of data projects.
- Defining security policies for user and application access.
- Integrating SIEM solutions for centralised monitoring.
Hands-on exercises
- Design a comprehensive security plan for an institutional big data project.
Audience
This course is intended for technical experts and data leaders, including:
- system and network administrators maintaining production cluster availability and isolation;
- big data engineers integrating security by design into data pipelines;
- CISOs and compliance managers adapting control frameworks to big data constraints;
- IT project managers overseeing big data deployment while ensuring regulatory compliance.
Prerequisites
The following prerequisites apply:
- Technical skills: fundamental networking knowledge and, ideally, initial exposure to Hadoop or Spark to facilitate practical workshops.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Quiz / multiple-choice questions
- Practical exercises
- Case study
Course highlights
- Immersive practical workshops: work directly with key components such as HDFS and Hadoop for immediate operational proficiency.
- Governance and technical perspectives: reconcile GDPR requirements with cluster performance constraints.
- Proactive expertise: develop anomaly detection practices specific to large-scale processing.
- Methodological deliverable: leave with a security plan framework adaptable to your own big data projects.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.