Overview
Harness the power of advanced data analytics, AI, and machine learning
Syllabus
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- Introduction to Data Lakes
-- Definition and purpose of a data lake
-- Key differences between data lakes and data warehouses
-- Benefits and challenges of implementing a data lake
- Data Lake Architecture
-- Core components of a data lake
-- Data ingestion and storage layers
-- Processing and analytics layers
- Data Lake Technologies
-- Overview of popular data lake platforms (e.g., AWS Lake Formation, Azure Data Lake, Google Cloud Storage)
-- Comparison of different data storage formats (e.g., Parquet, ORC, Avro)
- Data Ingestion
-- Batch vs. streaming data ingestion
-- Common tools and methods for data ingestion (e.g., Apache Kafka, Apache NiFi)
- Data Governance and Security
-- Importance of data governance in data lakes
-- Security best practices (e.g., access control, encryption)
-- Metadata management
- Data Processing and Analytics
-- Using big data processing frameworks (e.g., Apache Spark, Apache Flink)
-- Real-time analytics and batch analytics use cases
-- Integrating machine learning with data lakes
- Best Practices for Implementing Data Lakes
-- Planning and designing a data lake
-- Common pitfalls and how to avoid them
-- Scalability and performance optimization
- Case Studies and Industry Applications
-- Real-world examples of data lake implementations
-- Lessons learned from industry leaders
- Future Trends and Developments in Data Lakes
-- Emerging technologies and methodologies
-- The evolving role of data lakes in big data analytics and AI
- Conclusion and Next Steps
-- Summary of key takeaways
-- Resources for further learning and exploration
Taught by
Tags
On-Demand
