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Fundamentals of Data Engineering – Data Lakes Foundation


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by fatima
Price: 160,000
2Months/20 Hours
0 Lessons

Fundamentals of Data Engineering – Data Lakes Foundation

This course offers a detailed exploration of Data Lakes, focusing on their architecture, implementation, and management. It is designed to equip participants with practical skills and knowledge to effectively design, build, and maintain Data Lakes.


Course Key Leranings

By the end of this course, participants will:

  1. Gain a comprehensive understanding of Data Lake concepts and architectures.
  2. Learn to design, implement, and manage Data Lakes.
  3. Acquire hands-on experience with key Data Lake tools and technologies.
  4. Understand data ingestion, storage, processing, and retrieval strategies.
  5. Learn about data governance, security, and compliance in the context of Data Lakes.

Course Content:

Module 1: Introduction to Data Lakes

  • Understanding Data Lakes and their role in data management
  • Differences between Data Lakes and Data Warehouses
  • Advantages of Data Lakes for large-scale data storage and processing
  • When and why to use a Data Lake
  • Common challenges in implementing Data Lakes

Module 2: Data Lake Architecture

  • Data ingestion, storage, processing, and retrieval
  • Key technologies and tools involved
  • Design principles for scalable and efficient Data Lakes
  • Traditional vs. modern Data Lake architectures

Module 3: Data Ingestion and Integration

  • Batch processing vs. real-time streaming
  • Tools and technologies for data ingestion
  • Apache Kafka, AWS Kinesis, Azure Event Hubs
  • Setting up and configuring data ingestion pipelines

Module 4: Data Storage and Management

  • Object storage vs. file storage
  • Data Lake storage solutions: AWS S3, Azure Data Lake Storage, Google Cloud Storage
  • Metadata management and data cataloging
  • Structuring data within a Data Lake
  • Implementing data storage solutions and managing data organization

Module 5: Data Processing and Transformation

  • Overview of Apache Spark, Hadoop MapReduce
  • Data Transformation Techniques
  • ETL (Extract, Transform, Load) vs. ELT (Extract, Load, Transform
  • Developing and running data processing workflows

Module 6: Data Governance and Security

  • Data Governance
  • Principles and frameworks for effective data governance
  • Data Security
  • Ensuring data privacy and protection
  • Implementing encryption, access controls, and data masking
  • Compliance and Regulations
  • Understanding GDPR, CCPA, and other data protection regulations

Module 7: Advanced Data Lake Concepts

  • Data Lake Analytics
  • Techniques for querying and analyzing data within a Data Lake
  • Integration with Machine Learning and AI
  • Using Data Lakes to support machine learning and AI applications

Target Audience
  • Data Engineers and Architects seeking to enhance their knowledge of big data solutions.
  • IT Professionals and System Administrators responsible for managing large-scale data storage and processing systems.
  • Business Analysts interested in understanding how data lakes can be used for advanced analytics.
  • Data Scientists looking to leverage data lakes for machine learning and AI projects.

Prerequisites
  • Basic understanding of data management concepts.
  • Familiarity with SQL and relational databases.
  • Basic knowledge of programming (Python or Java is beneficial).
  • Understanding of cloud computing concepts (helpful but not required).

Career Path

  • Data Lake Engineer
  • Big Data Analyst
  • Data Engineer
  • Data Architect
  • Senior Data Engineer

International Student Fees: USD650$


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Flexible Class Options

  • Week End Classes For Professionals  SAT | SUN
  • Corporate Group Training Available
  • Online Classes – Live Virtual Class (L.V.C), Online Training

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