Transitioning from Data Warehousing to Big Data - Meeting Business Challenges of Data Volume, Velocity and Variety
Dates: 9 October 2018 (8.30am - 5.00pm)
Venue: Securities Commission Malaysia
Accreditation: CPE Eligible
In most business organisations, various data and information was generated by various departments such as finance, administration, marketing, production and sales departments. These data were created in a variety of data formats which may sits in numerous departmental silo setups without a central repository or a common data format.
In today’s fast moving business world with increasing data volumes and velocity, Big Data systems offer important economies of scale and scalability. Big Data systems are better at data analyses, more flexible and cost-effective in handling increasing volume and velocity of various data. Big Data ultimately offers much faster and effective way to gather useful insights from data analysis.
This programme examines the business potential and significance of data exploration on Big Data platforms. It explains how business intelligence and analytics on Big Data can be used for the success of digital and business transformation initiatives in capital market business organisations.
Upon completion of this programme, participants will be able to:
- Recognise key aspects of big data and analytics
- Compare types of current data warehousing architectures
- Assess big data alternatives and offerings to business
- Discuss the flexibility and cost-effectiveness of big data systems
Interactive presentations, Discussions and Question-and-Answer (Q&A) sessions
||Functional (Technical Skills) – Digital Technology Application
||Types of Current Data Warehousing Architectures
- Unstructured and structured data sources
- Weaknesses and limitations of current data warehousing structures
||What is the Big Data Alternative? What Can it Offer Businesses?
- How to make big data analytics work for your business
- Example - The Hadoop Ecosystem
||Why Big Data Systems are More Flexible and Cost-Effective?
- Case studies - How big data systems handle volume, velocity, and data variety
||Case Studies - Common Practices for the Migration from Data Warehousing to Big Data Systems
||End of Programme