data modelling

1. PD 1 – Data Integration Architecture
Extract, Transform, Load and More
By: Brian Arnesto Sitorus , S.Kom, MSc
(Bakrie University)

2. What is Data
Integration?
Data integration is the
process of combining data
from multiple sources into
a unified view

3. Data Integration
Reference
Architecture
The architecture typically
consists of several layers,
including
1. extract processes,
2. transform processes,
3. load/publish processes, and
4. data quality processes.

5. Data Integration
Challenges
- dealing with data silos, where data is stored in
different systems and formats that are not easily
accessible
- ensuring data quality, as poor data quality can lead
to inaccurate insights and decisions.

6. Enterprise Application
Integration (EAI)
• Integrating different business application
into one integrated system

7. Data Integration
Tools
• ETL tools are used to extract data
from various sources,
• Transform it into a format that can be
used by the target system, and load it
into the target system
• Data integration platforms provide a
comprehensive solution for managing
data integration processes, including
data mapping, data transformation,
and data quality.

10. Data Integration
Governance
• It ensures that data
integration processes are
aligned with business goals
and objectives, and that data
is being used effectively to
drive business outcomes.

11. How?
• by providing a set of guidelines,
policies, and procedures for managing
data integration processes
• For example, a governance framework
might include guidelines for data
quality, data security, and data privacy,
as well as procedures for monitoring
and maintaining data integration
processes over time.

12. Data Integration
Security
• data encryption, which involves
converting data into a coded language
that can only be accessed with a
decryption key.
• Access controls can also be implemented
to restrict access to data based on user
roles and permissions.
• firewalls, intrusion detection systems,
and regular security audits.

13. Data Integration Performance Optimization
• Data caching, which involves storing frequently
accessed data in memory for faster retrieval. This can
significantly reduce the time required for data
integration processes and improve overall system
performance.
• parallel processing, which involves breaking down
large data integration tasks into smaller, more
manageable pieces that can be processed
simultaneously.

14. Data Integration
Testing
• Ensures that the data being
integrated is accurate, complete,
and consistent with the business
requirements.

15. Data Integration Monitoring and
Maintenance
• checking for errors in data transfer,
• ensuring that data is being properly
transformed and loaded into the target
system, and
• verifying that data is being processed
within specified timeframes.

16. Data Integration Trends and
Future Directions
• cloud-based data integration, which allows
organizations to easily integrate data from
multiple sources in a scalable and cost-
effective way
• artificial intelligence and machine learning to
automate data integration processes and
improve data quality.
• data privacy and compliance

17. Key
Takeaways
Data integration is a complex process that requires careful
planning and execution. By following best practices such as
ensuring data quality, implementing data governance, and
securing data integration processes, organizations can
achieve successful data integration.
However, there are also common challenges to overcome
such as data silos, lack of resources, and data quality
issues. To address these challenges, organizations can use
tools such as ETL tools, data integration platforms, and
data virtualization tools.
Looking towards the future, trends in data integration
include cloud-based data integration, the use of artificial
intelligence and machine learning, and the increasing
importance of data privacy and compliance.

18. Thank You

19. Additional Resources?
Here are some additional resources for those who want to learn more
about data integration:
- Books: 'Data Integration Blueprint and Modeling' by Anthony David Giordano, 'Data Integration Life
Cycle Management with SSIS' by Andy Leonard
- Articles: 'Data Integration Best Practices' by Informatica, 'The Future of Data Integration' by Talend
- Blogs: 'The Data Integration Blog' by Syncsort, 'The Informatica Blog'
- Online courses: 'Data Integration Fundamentals' by Udemy, 'Data Integration and ETL with Talend' by
Pluralsight