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    Data Warehousing Free Online Course with Certificate

    Data Warehousing Free Online Course with Certificate

    Learn Data Warehouse and master practical implementation with industry insights!

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    Prerequisites

    Data Warehousing
    • Prior knowledge of relational databases is crucial for a solid foundation.
    • Understanding database design concepts is advantageous for effective learning.
    Course Prerequisites
    • 450,000 +
      Professionals trained
    • 250+
      Workshops every month
    • 100+
      Countries and counting

    Navigating the World of Data Warehousing  

    Navigating the World of Data Warehousing  

    19+ Hours of Self-Paced Learning Content

    Test Your Learning with Recall Quizzes

    Unlock Knowledge with Interactive eBooks

    Accelerate Progress with Auto-Graded Assessments

    Dive into the transformative world of data warehousing through our comprehensive free Data Warehousing course online. Uncover data warehousing architecture, strategies, and analytical power, equipping you to master data management in today's dynamic landscape.

    The curriculum of this free Data Warehousing course is carefully curated to give you the best learning experience possible. Begin with a foundational understanding of data warehousing, exploring its purpose and differentiating it from OLTP databases. Shift to practical implementation, navigating strategies like ODS, Data Lakes, and Data Marts. Grasp data integration via ETL and ELT, and delve into modeling techniques, managing historical data with precision.

    Learn to apply data warehousing across industries like telecommunications, banking, healthcare, and taxation for domain-specific insights. Acquire ability to extract meaningful insights, guiding way for well-informed decision-making. By the end of this Data Warehousing free online course, you will emerge as a skilled practitioner ready to make a substantial difference in the world of data.

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    What You Will Learn
    1
    Data Analysis for Decision Making

    Learn all about organizing and analyzing large sets of data for well-informed decision making.

    2
    Implementing Data Warehouse

    Understand the various methods used for implementing a data warehouse.

    3
    Data Integration Techniques

    Understand the importance and techniques of data integration in data warehousing.

    4
    Data Modelling Components

    Understand the essential components and techniques of data modelling for data warehousing.

    WHY KNOWLEDGEHUT?

    The KnowledgeHut Edge

    Superior Outcomes

    Focus on skilled-based outcomes with advanced insights from our state-of-the art learning platform.

    Immersive Learning

    Go beyond just videos and learn hands-on with guided exercises, projects, assignments and more.

    Continual Support

    Learn better with support along the way. Get 24/7 help, stay unblocked and ramp up your skills.

    World-Class Instructors

    Course instructors and designers from top businesses including Google, Amazon, Twitter, and IBM.

    Real-World Learning

    Get an intimate, insider look at leading companies in the field through real-world case studies.

    Industry-Vetted Curriculum

    Six months of post-training mentor guidance to overcome challenges in your web development career.

    Concept of a Data Warehouse

    Curriculum

    1. Concept of a Data Warehouse

    Learning Objective:

    Understand the fundamentals of data warehousing, differentiating it from OLTP databases, and grasp the high-level architecture.

    Topics

    • Overview of Data Warehousing
    • What is a Data Warehouse?
    • Why Do We Need a Data Warehouse?
    • Data Warehouse vs OLTP Database
    • High Level Data Warehouse Architecture

    2. The Different Implementation Methods of the Data Warehouse

    Learning Objective:

    Explore varied data warehouse implementation approaches, including ODS, staging, data lakes, marts, cubes, and EDWs, while identifying key decision points and challenges

    Topics

    • Operational Data Store (ODS)
    • Staging
    • Data Lake
    • Data Mart
    • Cube
    • Enterprise Data Warehouse
    • Data Virtualization
    • Decision Points
    • Data Warehousing Vs Data Mart
    • Data Warehousing Challenges

    3. Data Integration

    Learning Objective:

    Differentiate ETL vs ELT, grasp data standardization and cleansing techniques, explore loading and transformation methods, and delve into event-driven integration.

    Topics

    • ETL vs ELT
    • Data Standardization
    • Data Cleaning
    • Data Warehouse Loading Methods
    • Data Transformation Methods
    • Data Aggregation
    • Frequency of Data Loading and Transformation
    • Event-Driven Integration

    4. Data model for a Data Warehouse  

    Learning Objective:

    Discover data modeling techniques, normalized and dimensional models, facts, and dimensions, and learn to manage relationships and data aggregation.

    Topics

    • Types of Data Models
    • Data Modeling Techniques
    • Normalized Data Model
    • Facts and Dimensions
    • Relationships Between Tables
    • Data Aggregation

    Data Warehousing and Algorithms in Java

    Data Warehousing and Algorithms in Java

    1. What is Data Warehousing?

    Data Warehousing involves gathering, organizing, and centralizing diverse data for efficient querying, analysis, and reporting. It offers a structured framework for integration, transformation, and historical storage, empowering informed decisions through comprehensive insights. Data Warehousing enhances quality, accessibility, and consistency, providing a holistic view for business intelligence and analytics.

    2. What does a Data Warehouse engineer do?

    A data warehouse engineer designs, constructs, and maintains data warehousing systems, facilitating efficient data storage, retrieval, and analysis. They collaborate with stakeholders to understand data requirements, implement ETL processes for data extraction, transformation, and loading, and optimize data models for performance. Data warehouse engineers ensure data quality, security, and integration from various sources, enabling informed decision-making. They possess expertise in database technologies, ETL tools, and programming languages, contributing to streamlined data management and empowering organizations to extract valuable insights for strategic business decisions.