Thuật ngữ Big Data (dữ liệu lớn) được sử dụng từ những năm 1990 và thực sự bùng nổ trong khoảng 10 năm trở lại đây. Big Data hiện nay đã được ứng dụng rộng rãi trong nhiều lĩnh vực như: Bán lẻ, ngân hàng, dịch vụ chăm sóc sức khỏe, viễn thông, giải trí, bảo hiểm, giao thông, giáo dục,… Trong bài này, mời các bạn cùng tìm hiểu về Big Data là gì? các phương thức người ta dùng để khai thác nó và nó giúp ích như thế nào cho cuộc sống của chúng ta.

Course Outline:

  • Module 1, “Introduction to Data Warehousing"
  • Module 2, “Planning Data Warehouse Infrastructure"
  • Module 3, “Designing and Implementing a Data Warehouse "
  • Module 4, “Column store Indexes"
  • Module 5, “Implementing an Azure SQL Data Warehouse"
  • Module 6, “Creating an ETL Solution"
  • Module 7, “Implementing Control Flow in an SSIS Package"
  • Module 8, “Debugging and Troubleshooting SSIS Packages"
  • Module 9, “Implementing a Data Extraction Solution"
  • Module 10, “Enforcing Data Quality"
  • Module 11, “Master Data Services"
  • Module 12, “Extending SQL Server Integration Services"
  • Module 13, “Deploying and Configuring SSIS Packages"
  • Module 14, “Consuming Data in a Data Warehouse"

Data governance technologies are evolving fast to meet the increasing demand of digital businesses today. This has forced organizations to adopt an all-inclusive data intelligence platform that integrate data governance, data quality and analytics capabilities for data-driven initiatives. Such platform enhances the ability to control data, empowering users to self-service their data needs without relying on just the IT department to solve business problems.

With a proper data governance strategy in place, data will be more secured and increases the audibility. Uniformity of data will make analysing, reporting and decision making easier.

Data governance can allow businesses to successfully take advantage of their data to gain actionable, data-driven insights that are timely, relevant and accurate.

Course Information

  • Duration: 5 Day / 40 Hours
  • Certification: Participants will receive a Certificate of Completion upon successfully completed the course
  • Who Should Attend: IT, Data, Data Management (policy, quality), Data Analytics, Statistic, Business Owners and anyone seeking to acquire knowledge on data governance

Pre-Requisite

It is preferred that participants have foundation knowledge in data governance or attended the Data Governance Foundation Training with CASUGOL

Module 1 Introduction to Data Quality

  • Introduction
  • Drivers
  • Goals and Principles
  • Essential Concepts
  • Data Quality Activities
  • Tools
  • Techniques
  • Data Quality & Data Governance
  • Module 2 What is Data Security

  • Introduction
  • Drivers
  • Goals and Principles
  • Data Security Activites
  • Tools
  • Techniques
  • Data security governance
  • Module 3 Metadata management

  • Introduction
  • Drivers
  • Metadata Management Activities
  • Tools
  • Techniques
  • Implementation guidelines
  • Metadata Governance
  • Module 4 Reference and Master data management

  • Introduction
  • Activities
  • Tools & Techniques
  • Implementation guidelines
  • Organizational and cultural change
  • Reference and master data governance
  • Module 5 Data warehouse and business intelligence

  • Introduction
  • Drivers, goals and principles, essential concepts
  • Activities
  • Tools
  • Implementation guidelines
  • DW/BI governance
  • Module 6 Data Storage and operations

  • Introduction
  • Business Drivers, goals and principles, essential concepts
  • Activities
  • Tools
  • Techniques
  • Implementation guidelines
  • Data storage and operations governance
  • Module 7 Data integration and Sharing

  • Introduction
  • Business Drivers, goals and principles, essential concepts
  • Activities
  • Tools
  • Implementation Guidelines
  • Date integration governance
  • Module 8 Document and Content Management

  • Introduction
  • Business Drivers, goals and principles, essential concepts
  • Activities
  • Tools
  • Techniques
  • Implementation guidelines
  • Document and content governance
  • Module 9 Big Data and Data Science

  • Introduction
  • Business Drivers, goals and principles, essential concepts
  • Activities
  • Tools
  • Techniques
  • Implementation guidelines
  • Big data and data science governanace

  • Data governance technologies are evolving fast to meet the increasing demand of digital businesses today. This has forced organizations to adopt an all-inclusive data intelligence platform that integrate data governance, data quality and analytics capabilities for data-driven initiatives. Such platform enhances the ability to control data, empowering users to self-service their data needs without relying on just the IT department to solve business problems.

    With a proper data governance strategy in place, data will be more secured and increases the audibility. Uniformity of data will make analysing, reporting and decision making easier.

    Data governance can allow businesses to successfully take advantage of their data to gain actionable, data-driven insights that are timely, relevant and accurate.

    Course Information

    • Duration: 4 Day / 32 Hours
    • Certification: Participants will receive a Certificate of Completion upon successfully completed the course
    • Who Should Attend: IT, Data, Data Management (policy, quality), Data Analytics, Statistic, Business Owners and anyone seeking to acquire knowledge on data governance

    Module 1 Data Management

  • Introduction to Data Management
  • Essential concepts
  • Data Management Frameworks
  • Module 2 Data Handling Ethics

  • Introduction
  • Drivers
  • Essential Concepts
  • Module 3 Data Governance

  • Introduction
  • Business Drivers
  • Goals and Principles
  • Data Governance Activities
  • Implement data governance practice
  • Tools & Techniques
  • Implementation guidelines
  • Metrices for value, effectiveness, sustainability
  • Module 4 Data Management Organization and Roles Expectations

  • Introduction, Understanding existing organization and cultural norms
  • Data management organizational Models
  • Critical success factors
  • Build the data management organization
  • Interaction between the DMO
  • Data Management roles
  • Module 5 International, Regional Regulatory Framework

  • BASEL Guidelines
  • General Data protection Act
  • MAS (Monetary Authority of Singapore) guidelines
  • BNM (Bank Negara Malaysia) guidelines