Data Lakes and Business Intelligence Integration Training Course: Harnessing Unified Data for Actionable Insights

Introduction

Data lakes have emerged as a transformative technology for managing large volumes of structured, semi-structured, and unstructured data. When integrated with Business Intelligence (BI) systems, data lakes enable organizations to store raw data at scale, transform it into actionable insights, and support advanced analytics and decision-making processes. Leveraging the flexibility and scalability of data lakes allows BI professionals to handle diverse data sources efficiently, reduce latency in reporting, and improve the quality of analytics outcomes.

This training course equips participants with the knowledge and practical skills needed to integrate data lakes into BI workflows. Through hands-on exercises, case studies, and real-world examples, learners will explore data lake architecture, ingestion methods, processing frameworks, and BI integration strategies. By the end of the course, participants will be able to design, implement, and optimize data lake solutions that enhance BI capabilities, support data-driven decision-making, and enable predictive and prescriptive analytics.

Duration: 10 Days

Target Audience

  • Business Intelligence professionals and data analysts
  • Data engineers and architects
  • IT managers overseeing data and analytics platforms
  • Professionals working with large-scale or multi-source data
  • Decision-makers seeking advanced analytics solutions

10 Objectives

  1. Understand the fundamentals of data lakes and their architecture
  2. Explore the benefits of integrating data lakes with BI systems
  3. Learn methods for ingesting, storing, and managing diverse data types
  4. Apply ETL and ELT processes for BI-ready data
  5. Implement real-time and batch processing in data lakes
  6. Ensure data quality, governance, and security in lake environments
  7. Explore advanced analytics and AI integration with data lakes
  8. Optimize BI workflows and reporting using unified data repositories
  9. Examine industry best practices and case studies
  10. Design and implement a complete data lake and BI integration project

15 Course Modules

Module 1: Introduction to Data Lakes and BI

  • Defining data lakes and their role in modern analytics
  • Differences between data lakes and traditional data warehouses
  • Benefits for Business Intelligence and decision-making
  • Key components of a data lake ecosystem
  • Use cases for BI integration

Module 2: Data Lake Architecture

  • Core layers: ingestion, storage, processing, and access
  • Scalability and fault tolerance
  • Cloud-based vs on-premises data lakes
  • Data cataloging and metadata management
  • Architectural best practices

Module 3: Data Ingestion and Integration

  • Batch and streaming ingestion techniques
  • Handling structured, semi-structured, and unstructured data
  • ETL and ELT pipelines for BI readiness
  • Data validation and cleansing
  • Automation and orchestration strategies

Module 4: Storage and Management in Data Lakes

  • File formats (Parquet, ORC, Avro, JSON)
  • Data partitioning and compression
  • Storage optimization strategies
  • Managing data lifecycle and retention
  • Security and access control

Module 5: Processing Frameworks for Data Lakes

  • Apache Spark and Hadoop for large-scale processing
  • In-memory vs disk-based processing
  • Real-time vs batch processing considerations
  • Data transformation and enrichment
  • Performance tuning for analytics workloads

Module 6: Data Governance and Quality

  • Ensuring data consistency and accuracy
  • Data lineage tracking and metadata management
  • Governance frameworks for data lakes
  • Compliance and regulatory considerations
  • Best practices for quality assurance

Module 7: BI Tools Integration with Data Lakes

  • Connecting BI platforms to data lakes
  • Querying large datasets efficiently
  • Designing dashboards and visualizations
  • Optimizing BI performance with lake data
  • Case examples of BI integration

Module 8: Advanced Analytics on Data Lakes

  • Predictive analytics using lake-stored data
  • Prescriptive analytics for decision support
  • Machine learning integration
  • Data mining techniques for insights
  • Industry-specific analytics use cases

Module 9: Real-Time Analytics and Streaming Data

  • Stream processing frameworks (Kafka, Spark Streaming)
  • Event-driven architectures for BI
  • Monitoring and alerting in real-time
  • Low-latency analytics workflows
  • BI applications of streaming data

Module 10: Security and Compliance in Data Lakes

  • Access control and identity management
  • Data encryption and secure storage
  • Auditing and monitoring
  • Ensuring regulatory compliance
  • Risk management strategies

Module 11: Data Lake Performance Optimization

  • Query optimization techniques
  • Resource allocation and cluster management
  • Storage and partitioning strategies
  • Reducing latency for BI queries
  • Monitoring and troubleshooting

Module 12: Cloud-Based Data Lakes for BI

  • Cloud storage solutions and platforms
  • Scalability and cost considerations
  • Integration with cloud BI tools
  • Hybrid and multi-cloud strategies
  • Case studies in enterprise cloud BI

Module 13: Industry Applications of Data Lakes in BI

  • Finance and risk analytics
  • Retail and customer behavior insights
  • Healthcare and life sciences data analysis
  • Supply chain and operations optimization
  • Public sector and government analytics

Module 14: Emerging Trends in Data Lakes and BI

  • Lakehouse architectures and unified analytics
  • AI-driven analytics and cognitive BI
  • Real-time decision-making advancements
  • Data democratization for business users
  • Preparing for future data-driven challenges

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Data Lakes And Business Intelligence Integration Training Course: Harnessing Unified Data For Actionable Insights in Israel
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