Fundamentals of Data Engineering – Data Lakes Foundation

  2Month/20 Hour                                                   Price:85,000

                                                                                      80,000

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.

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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.

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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

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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

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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

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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

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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

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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

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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

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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.

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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).

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Career Path

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

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International Student Fees: USD650$

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Job Interview Preparation  (Soft Skills Questions & Answers)

• Tough Open-Ended Job Interview Questions
• What to Wear for Best Job Interview Attire
• Job Interview Question- What are You Passionate About?
• How to Prepare for a Job Promotion Interview

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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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