To transform this raw information into actionable insights, enterprises must develop robust storage strategies that combine the scalability of data lakes with the analytical power of data warehouses. This course provides an in-depth exploration of enterprise data lakes and warehousing, equipping participants with the skills to design, implement, and manage modern data storage ecosystems.
Participants will learn how to architect hybrid solutions that leverage the strengths of both data lakes and data warehouses. The course examines the evolution of data storage, from traditional on-premise warehouses to cloud-native platforms, highlighting how enterprises can optimize performance, reduce costs, and improve agility.
Through hands-on labs and real-world case studies, learners will gain practical experience working with leading platforms such as Amazon Redshift, Google BigQuery, Azure Synapse, Snowflake, and Hadoop-based data lakes. The program emphasizes building solutions that support diverse workloads including advanced analytics, machine learning, and real-time business intelligence.
A core focus of the training is governance, security, and compliance, ensuring that participants not only build scalable solutions but also understand how to enforce policies, protect sensitive information, and comply with global regulations such as GDPR and HIPAA.
The course also addresses emerging trends such as data mesh, data fabric, and lakehouse architectures that unify the benefits of lakes and warehouses. Participants will explore how enterprises are adopting these cutting-edge approaches to remain competitive in a rapidly evolving digital landscape.
By the end of the training, learners will be able to design comprehensive enterprise storage strategies that balance scalability, performance, governance, and innovation. This makes the course ideal for professionals tasked with building resilient data foundations that drive digital transformation.
Who Should Attend
- Data Engineers and Architects designing enterprise data storage solutions.
- Cloud Engineers managing large-scale data ecosystems.
- Business Intelligence professionals building analytics platforms.
- Database Administrators transitioning to cloud-based storage systems.
- IT Managers overseeing enterprise data strategies.
- Data Scientists requiring high-quality, scalable data sources.
- DevOps and MLOps Engineers integrating storage with ML pipelines.
- Compliance Officers ensuring regulatory data management.
- Enterprise Architects designing hybrid and multi-cloud solutions.
- Professionals preparing for certifications in cloud data warehousing.
Duration
10 days
Course Objectives
By the end of this training, participants will be able to:
- Understand the evolution and principles of data lakes and warehouses in enterprises.
- Design and implement hybrid architectures that combine both approaches.
- Build scalable data lakes for handling unstructured and big data.
- Architect optimized warehouses for high-performance analytics.
- Integrate data ingestion, ETL, and ELT workflows into storage systems.
- Apply governance and compliance frameworks to enterprise storage.
- Implement cost-optimized and performance-tuned storage strategies.
- Leverage modern lakehouse architectures for unified data management.
- Manage real-time and batch data processing in data storage ecosystems.
- Deploy data storage solutions using AWS, Azure, GCP, and Snowflake.
- Monitor, secure, and optimize enterprise storage environments.
- Prepare for certifications in cloud data engineering and warehousing.
Comprehensive Course Outline
Module 1: Introduction to Enterprise Data Storage
- Evolution from traditional warehouses to modern storage.
- Role of data lakes and warehouses in enterprise ecosystems.
- Key challenges in large-scale data management.
- Overview of cloud-native data storage solutions.
Module 2: Fundamentals of Data Lakes
- Architecture and components of data lakes.
- Building lakes with Hadoop, AWS S3, Azure Data Lake.
- Ingesting structured and unstructured data.
- Metadata, catalogs, and data lake governance.
Module 3: Fundamentals of Data Warehousing
- Designing schemas and optimized warehouse structures.
- Platforms: Amazon Redshift, BigQuery, Azure Synapse.
- Query optimization and performance tuning.
- Integrating BI and analytics tools with warehouses.
Module 4: Hybrid Data Architectures
- Combining lakes and warehouses in enterprise solutions.
- Data lakehouse architectures: Delta Lake, Snowflake.
- When to choose lake vs. warehouse.
- Case studies of hybrid implementations.
Module 5: Data Ingestion and Integration
- ETL vs. ELT strategies in modern storage.
- Batch ingestion vs. real-time streaming ingestion.
- Tools: Apache NiFi, Kafka, and cloud-native services.
- Best practices for reliable integration.
Module 6: Data Governance and Security
- Implementing governance frameworks (GDPR, HIPAA).
- Access controls, encryption, and identity management.
- Metadata management with catalogs.
- Building secure, auditable storage ecosystems.
Module 7: Performance Optimization
- Partitioning, indexing, and clustering in warehouses.
- Caching strategies for query acceleration.
- Cost-performance trade-offs in cloud platforms.
- Monitoring and performance tuning tools.
Module 8: Cloud-Native Storage Solutions
- Amazon S3 and Redshift for enterprise storage.
- Azure Synapse and Data Lake integration.
- Google BigQuery as a serverless warehouse.
- Snowflake as a multi-cloud data solution.
Module 9: Real-Time Data Storage Strategies
- Streaming ingestion pipelines with Kafka and Flink.
- Real-time analytics integration with warehouses.
- Low-latency pipelines for business intelligence.
- Case study: IoT and financial services.
Module 10: Machine Learning and AI Integration
- Preparing data for ML and DL pipelines.
- Feature stores and AI-ready data warehouses.
- Using storage systems with ML platforms (SageMaker, Azure ML).
- Case studies: AI-driven analytics.
Module 11: Multi-Cloud and Hybrid Deployments
- Designing cross-cloud storage strategies.
- Data replication and synchronization.
- Vendor-agnostic data solutions.
- Challenges in multi-cloud data governance.
Module 12: Emerging Architectures
- Data mesh for decentralized data management.
- Data fabric for unified access and governance.
- Evolution of the data lakehouse concept.
- Future trends in enterprise storage.
Module 13: Migration and Modernization
- Migrating from legacy warehouses to cloud-native platforms.
- Refactoring ETL processes for cloud environments.
- Modernizing on-premise storage with hybrid solutions.
- Case study: enterprise-wide storage modernization.
Module 14: Monitoring and Observability
- Logging, metrics, and alerting for storage systems.
- Cloud-native monitoring tools (CloudWatch, Azure Monitor).
- Detecting bottlenecks and optimization opportunities.
- Proactive troubleshooting approaches.
Module 15: Industry Applications
- Healthcare data management and compliance.
- Financial services and real-time analytics.
- Retail analytics with lakehouse architectures.
- Manufacturing and IoT storage solutions.
Module 16: Project and Certification Preparation
- Designing an enterprise data lake + warehouse strategy.
- Building a hybrid solution with cloud-native tools.
- Peer reviews and collaborative evaluation.
- Certification exam preparation and practice.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.
Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment
Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
