Overview
Explore modern data management technologies, Among the topics to be analysed are the definition of basic data management techniques and technologies, the background of such technologies, and the future trends of Data Technology and Management (DTM).
This programme will also explain the differences between various Data Management approaches, environments, and tools, as well as the advantages and disadvantages associated with their diverse applications. Emerging trends in the field of DTM will be reviewed as well.
Key Takeaways
At the end of this programme, you will be able to:
- Understand the basic terms of DTM, along with the characteristics and applications of different DTM techniques
- Articulate the future trends of DTM
- Compare the advantages and disadvantages of the new DTM platforms and tools in different applications
- Recommend a set of tools for a given problem
Who Should Attend
- Please refer to the job roles section.
- Suitable for IT Engineers, IT Managers, IT Executives and IT Consultants.
Prerequisites
Minimum two years of working experience in the field of IT.
Programme Structure
The programme covers:
- Programme Introduction, description of the goals, contents, and scheme
- Data management technology: Definitions and basic terms
- Big Data and Big Data Technology: Definitions and basic terms
- A review of the DBMS/RDBMS technology and trend
- Technical details of Big data, Cloud and Edge techniques
- Hardware and software requirements
- Differences between ordinary DBMS/RDBMS and Big data
- Skills and resources needed for big data management
- Data quality for big data solutions: data quality assurance for big data systems
- Data quality for big data solutions: new methodologies
- Cloud and edge technologies introduction
- Moving to big data: architecture, infrastructure, application, metadata
- Future trends in big data technology
- Tools review: Airflow, Kubernetes, Docker, ML Flow, Google Cloud, Mongo DB, Graph Databases, No SQL, and Server-less technologies
- Tools Comparison: Airflow, Kubernetes, Docker, ML Flow, Google Cloud, Mongo DB, Graph Databases, No SQL, and Server-less technologies
- Security considerations in modern DM
- Versatility considerations: Technical and implementation
- Volume and Velocity considerations: Technical and implementation
- IoT and multi-resources considerations: technical and implementation
- Big data implementation: requirements, roadmaps, strategies
- Big data implementation: challenges, integration, and validation
- Big data analysis challenges and methodologies
- Big data scaling challenges, necessities, and the application side
- Data warehousing for big data and its challenges
- Describing future DTM trends
Fees
| Full Fee |
Full programme fee | S$1900 |
9% GST on nett programme fee | S$171 |
Total nett programme fee payable, incl. GST | S$2071 |
With effect from 1 Jan 2024
NOTE
Funding is available for this programme. Please visit the
Learning Parner's website to find out about the updated programme fee funding breakdown, eligibility, terms and conditions.
| Step 1
| Apply through your organisation's training request system. |
| Step 2
| Your organisation's training request system (or relevant HR staff) confirms your organisation's approval for you to take the programme.
Your organisation will send registration information to the academy. Organisation's HR L&D or equivalent staff can click to register through Learning Partner's registration portal. The HR L&D will need to generate a URL link and send it to the participant to register for the programme under Corporate-Sponsored. The participant must first log in to L3AP using Singpass before clicking on the URL link to complete their registration and declaration. Failure to do so will result in registration under Self-Sponsored. Please refer to NUS Lifelong Learning Portal (L3AP) guide here.
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| Step 3
| The Learning Partner will inform you whether you have been successful in enrolment.
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