Data Lake Architecture, Management & Governance Training Workshop

Course overview

Data Lake Architecture, Management & Governance Training is a practical programme designed to equip professionals with the knowledge and skills required to design, implement, manage, secure and govern modern enterprise data lakes. The training covers the complete data-lake lifecycle, from architecture and cloud storage design to data ingestion, organisation, metadata management, data quality, security, access control, lineage, lifecycle management, performance optimisation and integration with analytics, business intelligence, machine learning and AI.

The workshop further addresses data governance operating models, metadata and cataloguing, data ownership, stewardship, privacy, security, data quality, regulatory compliance, data lifecycle management and responsible use of data for AI.

Course objectives

Design modern enterprise data lake architectures capable of supporting structured, semi-structured and unstructured data at scale.
Implement effective data ingestion, organisation and storage strategies using cloud object storage, open data formats, partitioning and modern lakehouse technologies.
Develop data governance frameworks covering ownership, stewardship, metadata, data cataloguing, lineage, access control, quality, privacy and compliance.
Manage and optimise enterprise data lakes through lifecycle management, monitoring, performance optimisation, security controls and cloud cost-management practices.
Develop trusted, analytics-ready and AI-ready data environments that support business intelligence, advanced analytics, machine learning and generative AI applications.

Who should attend

Data Engineers, Data Architects, Data Platform Engineers, Cloud Data Engineers, Big Data Engineers, Data Management Professionals, Data Governance Officers, Data Stewards, Database Administrators, Database Developers, Data Analysts, Business Intelligence Professionals, Data Scientists, Machine Learning Engineers, MLOps Professionals, Cloud Architects, Enterprise Architects, Solutions Architects, DevOps Engineers, Data Quality Professionals, Metadata and Data Catalog Managers, Information Security Professionals, IT Managers, ICT Managers, Chief Data Officers, Digital Transformation Professionals, Compliance Professionals, Records and Information Management Professionals, and IT professionals responsible for enterprise data platforms.

Course content

Module 1: Foundations of Modern Data Lakes Data lake concepts, evolution of enterprise data platforms, data warehouses versus data lakes, data lake use cases, characteristics of modern data lakes, structured data, semi-structured data, unstructured data,
Module 2: Enterprise Data Lake Architecture & Design Data lake architecture principles, architecture layers, ingestion layer, storage layer, processing layer, metadata layer, governance layer, consumption layer, centralized data lakes, federated data lak
Module 3: Cloud Data Lake Storage Cloud object storage, Amazon S3, Azure Data Lake Storage, Google Cloud Storage, Microsoft OneLake, storage accounts and buckets, containers, folders and namespaces, storage tiers, scalable storage, storage security, encr
Module 4: Data Ingestion & Integration Database ingestion, file ingestion, API ingestion, application data, SaaS data, IoT data, batch ingestion, incremental ingestion, streaming ingestion, Change Data Capture, ETL and ELT integration, ingestion pipeline
Module 5: Data Organisation & Lake Structure Raw data zones, landing zones, staging zones, curated zones, analytical zones, Bronze layer, Silver layer, Gold layer, folder structures, naming conventions, partitioning, file organisation, data domains, data
Module 6: Modern Lakehouse Architecture Data lakes versus lakehouses, lakehouse principles, medallion architecture, Delta Lake, Apache Iceberg, open table formats, ACID transactions, schema enforcement, schema evolution, time travel, table versioning, la
Module 7: Metadata Management & Data Cataloguing Technical metadata, business metadata, operational metadata, metadata repositories, data catalogs, data discovery, dataset descriptions, business glossaries, data ownership, data stewardship, metadata stan
Module 8: Data Lineage, Provenance & Traceability Data lineage concepts, source-to-target lineage, technical lineage, business lineage, column-level lineage, data provenance, transformation tracking, impact analysis, dependency management, lineage visual
Module 9: Data Quality & Trust Management Data quality dimensions, accuracy, completeness, consistency, validity, uniqueness, timeliness, data profiling, quality rules, automated validation, anomaly detection, duplicate detection, schema validation, data
Module 10: Data Governance Frameworks & Operating Models Data governance principles, governance policies, data ownership, data stewardship, governance committees, roles and responsibilities, centralized governance, decentralized governance, federated gov
Module 11: Data Security, Access Control & Privacy Identity and access management, authentication, authorization, role-based access control, least-privilege access, data classification, sensitive data, Personally Identifiable Information, encryption at r
Module 12: Data Lifecycle Management & Retention Data lifecycle stages, data retention policies, archival, cold storage, data deletion, storage tiering, lifecycle automation, retention schedules, legal holds, backup, recovery, historical data management,
Module 13: Data Lake Processing & Transformation Data transformation, SQL processing, Python processing, Apache Spark, PySpark, distributed processing, batch processing, streaming processing, data cleansing, enrichment, aggregation, transformation pipeli
Module 14: Data Lake Performance & Cost Optimisation Partitioning strategies, file-size optimisation, compression, columnar formats, Parquet, clustering, indexing, caching, query optimisation, compute optimisation, storage optimisation, workload manageme
Module 15: Data Lake Operations, Monitoring & Observability Data lake monitoring, storage monitoring, pipeline monitoring, processing monitoring, data-quality monitoring, logging, metrics, alerts, anomaly detection, operational dashboards, data freshness
Module 17: Data Lake Integration with BI, Analytics & Data Science Power BI integration, SQL analytics, analytical datasets, semantic models, data marts, dashboards, advanced analytics, machine-learning datasets, feature engineering, data science workflo
Module 18: Data Lakes for AI, Machine Learning & Generative AI AI-ready data architecture, ML datasets, feature pipelines, training data, model data lineage, unstructured data, document repositories, text processing, embeddings, vector databases, Retriev
Module 19: Modern Data Lake Platforms & Governance Technologies Microsoft Fabric and OneLake, Azure Data Lake Storage, Azure Databricks, Amazon S3, AWS Glue, Google Cloud Storage, BigQuery, Databricks Lakehouse, Delta Lake, Apache Iceberg, Unity Catalog,
Module 20: Enterprise Data Lake Implementation & Governance Capstone Business requirements analysis, data-lake architecture design, source-system assessment, storage architecture, ingestion design, data organisation, metadata strategy, governance framewo

General notes

Participants explore modern data-lake architectures across cloud and hybrid environments, including Amazon S3, Azure Data Lake Storage, Google Cloud Storage, Microsoft OneLake, Delta Lake, Apache Iceberg, lakehouse architectures and medallion architectures. The programme also examines how modern governance platforms and catalogs can provide centralised discovery, access control, auditing and lineage. Databricks, for example, uses Unity Catalog as a unified governance layer for data and AI assets, while Microsoft Fabric provides governance capabilities through its OneLake catalog and broader governance framework.
Recommended Technology & Tools Microsoft Fabric, OneLake, Azure Data Lake Storage, Azure Databricks, Amazon S3, AWS Glue, Google Cloud Storage, BigQuery, Databricks, Delta Lake, Apache Iceberg, Apache Spark, PySpark, Microsoft Purview, Unity Catalog, Apache Airflow, dbt, Python, SQL, Parquet, Power BI, REST APIs, cloud IAM, data catalogs, data-quality tools and data-observability platforms.

Frequently asked questions

1. 1. Who should attend the Data Lake Architecture, Management & Governance Training?

The training is suitable for data engineers, data architects, cloud engineers, data platform engineers, data governance professionals, data stewards, database administrators, data analysts, data scientists, BI professionals, IT managers, ICT managers, enterprise architects, information security professionals, compliance officers and technology professionals responsible for managing organisational data platforms.

2. 2. What will participants learn during the training?

Participants will learn how to design, implement, manage, secure and govern modern data lakes. The programme covers data-lake architecture, cloud storage, data ingestion, data organisation, metadata, data cataloguing, data quality, lineage, security, privacy, lifecycle management, monitoring, performance optimisation and integration with analytics, machine learning and AI.

3. 3. What is the difference between a data lake and a data warehouse?

A data lake is designed to store large volumes of structured, semi-structured and unstructured data in its native or minimally transformed form, while a data warehouse generally focuses on structured, curated data optimised for analytical querying and reporting. The training explains how data lakes, data warehouses and modern lakehouse architectures can work together within an enterprise data platform.

4. 4. Does the course cover modern lakehouse architecture?

Yes. Participants explore lakehouse architecture, Medallion Architecture, Bronze/Silver/Gold layers, Delta Lake, Apache Iceberg, ACID transactions, schema evolution, data versioning and analytics-ready datasets. The course also examines how lakehouse architectures support BI, data science, machine learning and generative AI.

5. 5. Which cloud platforms and technologies are covered?

The programme can include Microsoft Fabric and OneLake, Azure Data Lake Storage, Azure Databricks, Amazon S3, AWS Glue, Google Cloud Storage, BigQuery, Databricks, Delta Lake, Apache Iceberg, Apache Spark, PySpark, Microsoft Purview and Unity Catalog, together with Python, SQL and cloud data-integration technologies.

6. 6. Does the training cover data governance?

Yes. Data governance is a major component of the programme. Participants explore data ownership, data stewardship, governance policies, data standards, data classification, metadata, data cataloguing, data lineage, access controls, data-quality management, privacy, compliance and governance operating models.

Schedule & fees

Dates	Location	Duration	Fee (USD)
05/04/2027 - 16/04/2027	Nairobi,kenya or Virtual	10 Days	$3,000
19/04/2027 - 23/04/2027	Naivasha,Kenya	10 Days	$2,000
26/04/2027 - 07/05/2027	Arusha,Tanzania	10 Days	$3,000