In today's complex data landscape, mastering Data Lake Architecture and Implementation is a foundational skill for building scalable, flexible, and cost-effective data solutions that can store and process vast quantities of structured, semi-structured, and unstructured data, thereby breaking down data silos and enabling advanced analytics, machine learning, and business intelligence. A well-designed data lake serves as a centralized repository, providing a unified view of an organization's data assets and empowering data-driven innovation. This comprehensive training course is meticulously designed to equip data architects, data engineers, cloud architects, and analytics professionals with the advanced knowledge and practical strategies required to plan, design, and implement a robust data lake, from selecting the right cloud technologies to establishing a strong governance framework. Without a solid understanding of Data Lake Architecture and Implementation, organizations risk creating fragmented data ecosystems, facing high costs, and struggling to leverage their data for competitive advantage, underscoring the vital need for specialized expertise in this critical domain.

Duration: 10 Days

Target Audience

• Data Architects and Designers
• Data Engineers and ETL Developers
• Cloud Architects and DevOps Engineers
• Big Data and Analytics Professionals
• Data Scientists and Machine Learning Engineers
• IT Managers and Technical Leaders
• Business Intelligence (BI) Developers
• Database Administrators (DBAs)
• Anyone responsible for designing, building, or managing modern data platforms.

Objectives

• Understand the core concepts of data lakes and their role in a data ecosystem.
• Learn to differentiate between a data lake, data warehouse, and data mart.
• Acquire skills in designing a multi-layered data lake architecture (e.g., Bronze, Silver, Gold).
• Comprehend techniques for ingesting data from various sources into a data lake.
• Explore strategies for processing and transforming data within a data lake.
• Understand the importance of data cataloging, metadata management, and data discovery.
• Gain insights into implementing robust security and access control for data lakes.
• Develop a practical understanding of data governance, quality, and lineage in a data lake environment.
• Master the use of key cloud-native services for data lake implementation.
• Acquire skills in optimizing data lake storage and compute costs.
• Learn to apply best practices for building scalable and reliable data pipelines.
• Comprehend techniques for handling streaming and real-time data ingestion.
• Explore strategies for integrating a data lake with existing data warehouses and BI tools.
• Understand the importance of monitoring, logging, and performance tuning for a data lake.
• Develop the ability to lead and implement a successful Data Lake Architecture and Implementation project.

Course Content

Module 1: Introduction to Data Lakes

• What is a data lake and its key characteristics?
• The evolution from data warehouses to data lakes and lakehouses.
• Key components of a data lake: storage, compute, and metadata.
• Benefits and challenges of implementing a data lake.
• Business drivers for adopting a data lake architecture.

Module 2: Data Lake Architecture Patterns

• The multi-layered approach: Raw, Staging, Curated, and Consumption layers.
• The medallion architecture (Bronze, Silver, Gold) and its purpose.
• Choosing the right file formats for data lake storage (e.g., Parquet, Avro, ORC).
• Understanding schema-on-read vs. schema-on-write.
• Designing for scalability and flexibility.

Module 3: Cloud Data Lake Storage Services

• Introduction to Amazon S3, Azure Data Lake Storage (ADLS), and Google Cloud Storage (GCS).
• Managing storage: buckets, containers, and data organization.
• Tiered storage strategies and lifecycle management.
• Cost management for object storage.
• Best practices for file and folder naming conventions.

Module 4: Data Ingestion Strategies

• Batch vs. Streaming data ingestion.
• Tools for data ingestion: AWS Glue, Azure Data Factory, Google Cloud Dataflow.
• Ingesting structured data from databases (CDC, bulk loading).
• Ingesting semi-structured data (JSON, XML) and unstructured data.
• Data ingestion security and pipeline monitoring.

Module 5: Data Processing and Transformation

• The role of Spark, Databricks, and other processing engines.
• Using SQL for data transformation (e.g., AWS Athena, Azure Synapse SQL, Google BigQuery).
• Building ETL/ELT pipelines for data cleansing and enrichment.
• The importance of idempotency and fault tolerance in processing jobs.
• Orchestrating data pipelines with tools like Airflow or cloud-native services.

Module 6: Data Cataloging and Discovery

• What is a data catalog and why is it essential for data lakes?
• Metadata management: business, technical, and operational metadata.
• Tools for data cataloging: AWS Glue Data Catalog, Azure Purview, Google Data Catalog.
• Data governance and ownership with a data catalog.
• Enabling self-service analytics and data discovery.

Module 7: Data Lake Security and Access Control

• Implementing a secure perimeter for the data lake.
• Identity and Access Management (IAM) for data resources.
• Data encryption at rest and in transit.
• Role-based access control (RBAC) and attribute-based access control (ABAC).
• Auditing, logging, and monitoring access to data.

Module 8: Data Governance and Quality

• Establishing a data governance framework for a data lake.
• Data quality rules and validation at each layer.
• Data lineage and tracking data flow.
• Managing data lifecycle and retention policies.
• Creating a single source of truth within the data lake.

Module 9: Data Lake Implementation on AWS

• Deep dive into AWS S3, AWS Glue, and AWS Lake Formation.
• Using AWS Athena and Redshift Spectrum for querying the data lake.
• Building a real-world data lake on AWS.
• Best practices for performance and cost optimization on AWS.
• Case studies of AWS data lake implementations.

Module 10: Data Lake Implementation on Azure

• Deep dive into Azure Data Lake Storage (ADLS) and Azure Synapse Analytics.
• Using Azure Data Factory and Databricks for data processing.
• Implementing a data lake with Azure Purview for governance.
• Best practices for security and cost management on Azure.
• Case studies of Azure data lake implementations.

Module 11: Data Lake Implementation on GCP

• Deep dive into Google Cloud Storage (GCS) and Google Cloud Dataproc.
• Using BigQuery for querying and analytics on the data lake.
• Implementing data ingestion with Google Cloud Dataflow and Pub/Sub.
• Best practices for performance and cost optimization on GCP.
• Case studies of GCP data lake implementations.

Module 12: Building a Modern Lakehouse

• Introduction to the lakehouse architecture and its advantages.
• The role of Delta Lake in creating a lakehouse.
• Using Databricks on any cloud to build a lakehouse.
• Unifying data warehousing and data lakes.
• Migrating from a traditional data warehouse to a lakehouse.

Module 13: Streaming Data into the Data Lake

• Architecting for real-time data ingestion.
• Using stream processing engines (e.g., Apache Flink, Spark Streaming).
• Cloud services for streaming: Kinesis, Event Hubs, Pub/Sub.
• Processing and storing real-time data in a data lake.
• Building a lambda or kappa architecture for hybrid data.

Module 14: Data Lake Operations and Maintenance

• Monitoring data pipelines and infrastructure.
• Troubleshooting common data lake issues.
• Automation of tasks and CI/CD for data lake projects.
• Performance tuning: compaction, partitioning, and indexing.
• Cost management and resource allocation strategies.

Module 15: Practical Workshop: Designing an End-to-End Data Lake

• Participants work in teams on a business case to design a data lake.
• Exercise: Define the architecture, choose cloud services, and design the data flow.
• Create a data ingestion plan and a data quality framework.
• Present the final design and justify key decisions.
• Discussion on real-world challenges and solutions.

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: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute 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 Skills for Africa Training Institute 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: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.