About the Course
Organizations invest in data lake management because they need data they can prove is available, governed, and ready for use in analytics, reporting, and machine learning. That means you need to demonstrate data ingestion design, metadata management, schema-on-read discipline, access control, lineage tracking, and cost monitoring, not just storage administration. A workable data lake program typically draws on DAMA-DMBOK, Apache Kafka patterns, and cloud-native governance controls to keep raw, refined, and curated zones aligned with business use.
This data lake management training turns scattered platform knowledge into a structured operating model you can apply in day-to-day work. You will practice lake zone design, ingestion planning, catalog structuring, and performance triage, and you will be introduced to advanced AI-assisted data classification and automated data quality monitoring at an operational awareness level. In plain terms, this course teaches you how to design, govern, and optimize a data lake so you can support analytics and machine learning with better control, clearer lineage, and lower avoidable storage cost.
Many teams face budget constraints, cloud sprawl, duplicate datasets, unclear ownership, and pressure to expose data faster without weakening security. This course is designed for professionals who have to deliver practical results under those constraints, especially when governance, integration, and performance expectations compete with limited time and mixed technical maturity across the organization.
Target Audience
This training is designed for professionals who manage, design, govern, or analyze data lake environments and need practical control over ingestion, storage, cataloging, security, and performance.
- Data Engineer responsible for ingestion pipelines and lake zone organization
- Data Architect designing scalable data lake layouts and access patterns
- Data Governance Analyst tracking metadata, lineage, and ownership
- Analytics Engineer preparing curated datasets for BI and reporting
- BI Developer consuming lake data for dashboards and semantic models
- Cloud Data Platform Administrator managing storage, access, and monitoring
- Information Security Analyst enforcing encryption and access controls
- Data Quality Analyst defining checks for completeness and freshness
- Data Product Owner prioritizing dataset accessibility for business users
- Machine Learning Engineer preparing lake data for feature reuse and experimentation
Course Objectives
This course equips you to design, execute, and measure data lake initiatives that improve usability, strengthen governance, and support analytics at lower operational risk.
- Assess data lake maturity using a lake zone, metadata, and lineage review informed by DAMA-DMBOK.
- Apply schema-on-read and schema-on-write choices to batch and streaming ingestion scenarios.
- Build a governed raw, refined, and curated zone structure for enterprise lake storage.
- Create a data catalog and ownership map using glossary, tags, and lineage conventions.
- Evaluate lake security controls against ISO/IEC 27001:2022 access, encryption, and data handling practices.
- Navigate governance and compliance requirements for sensitive data, retention, and audit readiness.
- Implement storage and query optimization using partitioning, file format, and cost metrics.
- Synthesize findings into a data lake roadmap, KPI dashboard, and executive briefing pack.
Requirements & Prerequisites
Prerequisites required: working knowledge of data concepts, SQL, file formats such as CSV and Parquet, and basic cloud storage terminology. Familiarity with ETL or ELT workflows is helpful, but coding is not required for completion. Advanced implementation topics such as automated cataloging and AI-assisted data quality monitoring are covered at operational awareness and applied design level, not production engineering depth.
Local Application and Business Return in your market
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
Training Methodology
This is a practical, outcome-driven course designed to turn data lake management aspiration into measurable action and credible reporting.
Methodology includes:
- Hands-on calculation using storage cost, query latency, and freshness metrics from a sample lake dataset.
- Scenario simulation on a failed ingestion and delayed BI refresh incident in a cloud lake.
- Assessment using a governance checklist mapped to DAMA-DMBOK and ISO/IEC 27001:2022 controls.
- Stakeholder mapping for data owners, security reviewers, platform admins, and BI consumers.
- Case study analysis from finance, healthcare, retail, and manufacturing lake environments.
- Group workshop to produce a zone design, catalog structure, and governance register.
- Reflection exercise comparing current lake practices with metadata, lineage, and cost benchmarks.
Upcoming Sessions
Next available dates worldwide
Certification
Recognized credentials that advance your career
Participants who complete the Data Lake Management Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Career Advancement
- Master data lake technologies to elevate your career in big data management.
- Unlock senior data roles with cutting-edge skills in managing complex data environments.
- Certification in Data Lake Management increases your marketability to top tech employers.
Expert-Led Instruction
- Learn from industry leaders with over 20 years in data management and analytics.
- Courses designed by experts from leading tech companies, ensuring current industry relevance.
- Gain insider insights with real-world case studies from data management professionals.
Flexible and Practical Learning
- Access course materials anytime, anywhere, to fit learning into your busy schedule.
- Hands-on exercises and interactive content to apply your skills in real-world scenarios.
- Immediate practical takeaways, ready to be implemented in your current projects.
Tools and platforms relevant to this field
Examples local teams may encounter, and that may be featured in training where they support the confirmed course scope.
These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.
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Apache Kafka Apache Software FoundationUsed for streaming ingestion and event-driven pipelines that feed cloud data lakes.
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Amazon S3 Amazon Web ServicesUsed as durable object storage for raw and curated lake zones.
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Databricks Lakehouse Platform DatabricksUsed to manage lakehouse-style ingestion, transformation, and analytics on shared storage.
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Microsoft Fabric MicrosoftUsed to unify data integration, lake storage, and analytics in one platform.
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Snowflake Snowflake Inc.Used for governed data sharing and analytics workloads that often sit alongside data lake architectures.
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Apache Spark Apache Software FoundationUsed for distributed processing, transformation, and performance tuning across large lake datasets.
