Introduction

In the modern data-driven world, organizations must effectively collect, store, manage, and analyze vast amounts of structured and unstructured data. Implementing a Data Lake is a critical step in enabling businesses to leverage big data, AI, and real-time analytics. This 5-day training course provides a deep dive into designing, implementing, securing, and optimizing a scalable and future-proof Data Lake that meets the needs of evolving business intelligence and analytics frameworks.

Through hands-on labs and real-world case studies, participants will gain practical experience with modern Data Lake architectures, cloud-native solutions, governance strategies, and performance optimization techniques.

Objectives

By the end of this course, participants will be able to:

• Understand the role of Data Lakes in modern data ecosystems.
• Design a scalable and future-ready Data Lake architecture.
• Leverage cloud platforms and modern storage technologies for Data Lakes.
• Implement data ingestion, transformation, and processing pipelines.
• Ensure data governance, security, and compliance in Data Lakes.
• Optimize performance and cost efficiency in large-scale data storage.
• Integrate Data Lakes with Machine Learning (ML), AI, and BI tools.
• Understand emerging trends and the future of Data Lakes.

Who Should Attend?

This course is ideal for:

• Data Engineers who design and build modern data architectures.
• Data Architects seeking best practices for implementing scalable Data Lakes.
• Business Intelligence (BI) Professionals integrating Data Lakes with analytics tools.
• Cloud Engineers & DevOps Teams working with cloud-native data solutions.
• Data Scientists & AI Engineers leveraging Data Lakes for advanced analytics.
• IT Managers & CTOs planning enterprise-wide data strategies.

Day 1: Foundations of Data Lakes

• Introduction to Data Lakes

  • What is a Data Lake?
  • Differences between Data Lakes and Data Warehouses
  • Use cases and business value of Data Lakes

• Core Architecture of Data Lakes

  • Key components: Storage, Metadata, Ingestion, Processing, and Consumption
  • Designing a scalable architecture for structured and unstructured data
  • On-premises vs. cloud-based Data Lakes

• Data Ingestion Strategies

  • Batch vs. real-time data ingestion
  • Streaming data ingestion (Kafka, Apache Flink, Kinesis)
  • Connecting IoT and sensor data sources to Data Lakes

• Hands-on Lab:

  • Setting up a basic Data Lake architecture on AWS, Azure, or Google Cloud

Day 2: Building and Managing a Data Lake

• Storage and Data Organization

  • Choosing the right storage format (Parquet, ORC, Avro, JSON)
  • Partitioning strategies for performance optimization
  • Metadata management and data cataloging (AWS Glue, Apache Hive, Databricks Unity Catalog)

• Data Processing and Transformation

  • ETL vs. ELT: Modern approaches
  • Processing frameworks (Apache Spark, AWS Glue, Azure Data Factory)
  • Data enrichment and cleansing techniques

• Security and Access Control

  • Implementing role-based access control (RBAC)
  • Encryption and data masking for sensitive data
  • Auditing and logging for compliance (GDPR, CCPA)

• Hands-on Lab:

  • Data ingestion and transformation pipeline using Apache Spark

Day 3: Data Governance, Compliance & Performance Optimization

• Data Governance in Data Lakes

  • Data cataloging and lineage tracking
  • Managing schema evolution and versioning
  • Automated data quality checks

• Compliance & Legal Considerations

  • Handling personally identifiable information (PII)
  • Meeting regulatory requirements (GDPR, HIPAA, SOC 2)

• Optimizing Performance in Data Lakes

  • Query acceleration techniques (Apache Iceberg, Delta Lake, Hudi)
  • Cost-optimization strategies for cloud-based Data Lakes
  • Data lifecycle management and archiving strategies

• Hands-on Lab:

  • Implementing data governance and security policies in a cloud-based Data Lake

Day 4: Advanced Analytics, AI & Business Intelligence Integration

• Data Lakehouses: The Next Evolution

  • Converging Data Lakes & Warehouses
  • Best practices for Data Lakehouse implementation

• Integrating Data Lakes with BI & Analytics Tools

  • Connecting Data Lakes to Power BI, Tableau, and Looker
  • Building serverless query engines with AWS Athena, Presto, and BigQuery

• Machine Learning & AI in Data Lakes

  • Preparing data for AI/ML model training
  • AutoML & AI-driven analytics on Data Lakes
  • Deploying AI models directly in a Data Lake environment

• Hands-on Lab:

  • Building a predictive analytics model using data from a Data Lake

Day 5: Future Trends, Best Practices & Final Project

• Emerging Trends in Data Lakes

  • The rise of Data Mesh and decentralized data architectures
  • Data Fabric and AI-driven data management
  • The impact of Quantum Computing on Big Data

• Best Practices for Enterprise Data Lakes

  • Scaling Data Lakes for multi-cloud environments
  • Avoiding Data Swamps: Ensuring high data quality
  • Building a sustainable and maintainable Data Lake strategy

• Final Project: Building a Complete Data Lake Solution

  • Design and implement a full-scale Data Lake based on a real-world business scenario
  • Apply best practices in architecture, governance, and analytics

• Course Wrap-Up & Certification

  • Review of key concepts
  • Q&A and discussions on real-world challenges
  • Certification of completion