The Era of Big Data and Cloud Computing.pdf

This presentation explores the vital role of big data and cloud computing in driving digital transformation. It examines how these technologies enhance organizational efficiency, scalability, and innovation by enabling real-time analytics, data-driven decision-making, and cost-effective infrastructure solutions. The content covers key concepts such as cloud computing models, big data characteristics, and the integration of AI and machine learning for smarter business operations. Through examples and a case study on Google BigQuery, the presentation demonstrates how organizations can leverage cloud-based data analytics to achieve agility, security, and sustainable growth in the digital era.

1. Digital Transformation in
the Era of Big Data and
Cloud Computing
Prepared by
RawanAlharbi
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2. Articles
Authors Titles Published date Sourcing Citations
1
Minghao Xu , Yufei Zhang , Haoran
Sun , Yuanxiu Tang , Junjun Li
How digital transformation enhances corporate innovation
performance: The mediating roles of big data capabilities and
organizational agility
2024
ScienceDirect
15
2
Karwan Jameel Merseedi
, Subhi R. M. Zeebaree
Cloud Architectures for Distributed Multi-Cloud Computing: A Review of Hybridand
Federated Cloud Environment
2024
The
Indonesian
Journal of
Computer
Science
22
3
Wei Tang , Shuili Yang
Enterprise Digital Management Efficiency under Cloud
Computing and Big Data 2023 MDPI 7
4
Blend Berisha, Endrit Mëziu and
Isak Shabani
Big data analytics in Cloud computing:
an overview
2022
Springer 159
2
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3. Abstract
Introduction
The combination
of big data and
cloud computing
Enhances DT
Cloud
Computing
Big Data
Agenda
3
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4. Abstract
4
Digitaltransformation(DT)hasbecomeafundamentalstrategyfor
businessesaimingtoenhanceoperationalefficiency,scalability,and
innovation.Theintegrationofbigdataandcloudcomputingplaysa
importantroleinthistransformation,enablingreal-timedecision-making,
advancedanalytics,andcost-effectiveinfrastructuresolutions.Cloud
computingprovideson-demandresources,flexibleandscalablebusiness
operations,whilebigdataempowersorganizationstoextractvaluable
insightsforinformeddecision-making.Thispresentationexplorestherole
ofcloudcomputingandbigdataindigitaltransformation,highlighting
theirbenefits,real-worldapplications,andbestpracticesfororganizations
seekingtoleveragethesetechnologieseffectively.
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5. 5
Introduction
Therapidadvancementsintechnologyhavemadedigitaltransformation
(DT)anessentialapproachforbusinessesstrivingtoremaincompetitivein
themoderndigitallandscape.AmongthekeydriversofDTarebigdata
andcloudcomputing,whichofferorganizationstheabilitytoprocesslarge
volumesofinformationandimplementscalablesolutionswithout
significantinfrastructureinvestments.Cloudcomputingenablesenterprises
toaccesscomputingresourcesondemand,reducingcostsandimproving
flexibility,whilebigdataanalyticsallowsformoreinformeddecision-
makingthroughtheextractionofmeaningfulpatternsandtrendsfromvast
datasets.Thispresentationdelvesintohowthecombinationofcloud
computingandbigdataacceleratesdigitaltransformationandenhances
businessefficiency.
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6. Digital Transformation
Digitaltransformation(DT)hasbecomeaimportant
strategyforbusinessesseekingtoenhanceefficiency,
flexibility,andinnovation.Theriseofbigdataandcloud
computinghasfurtheracceleratedthistransformationby
enablingreal-timedecision-making,advancedanalytics,
andscalableinfrastructure[4].
6
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7. The combination of big data and cloud computing
Enhances DT
7
Providing Centralized Data Storage
•Cloud platforms store and process massive datasets, making big data analytics
more efficient.
Enabling AI and Machine Learning
•Cloud-based AI models process big data in real-time, supporting automation
and smart decision-making.
Enhancing Cybersecurity
•Cloud providers offer advanced security features, reducing risks associated with
big data storage and processing [3].
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8. Cloud Computing
Cloudcomputingofferson-demand
computing,networking,andstorage
withoutrequiringheavyinfrastructure
investments[2].
8
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9. 9
Cloud Computing Deployment Models
Public Cloud
•Infrastructure owned and operated by a third-party
provider.
Private Cloud •Dedicated cloud infrastructure for a single organization.
Community Cloud
•Shared infrastructure among organizations with similar
requirements.
Hybrid Cloud
•Combines two or more cloud models to balance security,
cost, and scalability.
Multi-Cloud
•Uses multiple cloud providers to distribute workloads and
reduce vendor lock-in [2].
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10. 10
Cloud Computing Services Models
Infrastructure as a
Service (IaaS)
•Virtualized computing resources (servers, storage,
networks).
Platform as a
Service (PaaS)
•Frameworks for developers to build applications without
managing infrastructure.
Software as a
Service (SaaS)
•Software applications delivered over the internet [2].
Analytics as a
Service (AaaS)
•Cloud-based Big Data Analytics [3].
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11. The Role of Cloud Computing in
Digital Transformation
Cloud computing enables enterprises to store, manage, and
analyze vast amounts of data while reducing costs and
improving operational flexibility [1].
11
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12. Cloud-Based Digital Transformation Benefits
12
•Reduces infrastructure expenses by shifting
from CapExto OpEx.Cost Efficiency
•Adapts to changing business demands in real-
time.
Scalability &
Flexibility
•Ensures data protection and minimizes
downtime.
Business Continuity
& Disaster Recovery
•Enables remote work and real-time data
sharing.
Enhanced
Collaboration
•Implements AI-driven cybersecurity measures
and regulatory frameworks (GDPR) [1].
Security &
Compliance
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13. 13
Cloud Computing
Examples
❖E-commerce
•Amazon uses AWS cloud for scalability, data analytics,
and AI-driven recommendations.
❖Banking & Finance
•Banks use cloud-based AI for fraud detection and risk
assessment.
❖Healthcare
•TelemedicineplatformsusecloudAItoanalyzepatient
datainreal-time[1].
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14. Big Data
BigDataconsistsoflarge,complexdatasets
thatcannotbeprocessedusingtraditional
databasemanagementtools[3].
14
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15. 15
Big Data characteristics (5Vs)
Volume •Massive amounts of structured and unstructured data.
Velocity •Real-time data processing and analysis.
Variety •Different data formats (text, images, videos, IoT data).
Veracity •Ensuring data accuracy and reliability.
Value •Extracting meaningful business insights[3].
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16. 16
Big Data Processing Models
Batch Processing
•Used for large data sets that do not require real-time
analysis (Hadoop MapReduce).
Stream Processing
•Handles real-time data (fraud detection, recommendation
systems) [3].
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17. The Role of Big Data in Digital
Transformation
Big data enables enterprises to extract valuable insights from
large datasets for smarter decision-making [1].
17
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18. Big Data Benefits
18
•AI-powered predictive analytics improve
business strategies.
Data-Driven Decision
Making
•Enhances recommendation engines for
targeted marketing.
Customer Insights &
Personalization
•IoT sensors in smart factories improve supply
chain efficiency.
Operational
Optimization
•Banks use AI-driven fraud detection models.Fraud Detection &
Risk Management
•Manufacturing firms use IoT data to prevent
equipment failures [1].
Predictive
Maintenance
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19. 19
Big Data
Examples
❖Retail
•Walmart uses big data for dynamic pricing and demand
forecasting.
❖Healthcare
•AI-powered big data analytics improve early disease
detection.
❖Smart Cities
•Governments use big data for traffic optimization and
urban planning [1].
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20. Big Data Analytics Cycle
Data Collection
Cloud-based landing
zones or distributed file
systems.
Processing &
Transformation
Machine learning and
statistical models extract
actionable insights [3].
Storage
Organizing data for
analytics.
Analysis &
Insights
Gathering data from
multiple sources (web,
IoT, social media).
20
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21. 21
Shift from ETL to ELT Paradigm
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22. Advantages of Big Data Analytics
❖Cost Optimization
•Reduces storage and processing costs.
❖Efficiency Improvement
•Helps understand consumer behavior.
❖Innovation
•Enables predictive analytics and data-driven decision-making [3].
22
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23. Case Study:
Google BigQuery
23
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24. Google BigQuery
24
✓A serverless, fully-managed data warehouse by Google Cloud Platform (GCP).
✓Uses SQL for querying petabytes of data.
✓Machine Learning (ML) integration.
❖BigQueryInterface
✓Supports web UI, APIs, and command-line interactions.
✓Users can import CSV, Google Drive files, or Cloud Storage data.
✓Executes queries efficiently using a distributed computing model [3].
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25. Google BigQuery
25
❖Key finding
✓Larger datasets benefit more from BigQuery due to parallel
computing.
✓Built-in caching improves execution speed.
✓Pay-as-you-go pricing model helps control costs [3].
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26. 26
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27. 27
Recommendations
Invest in Advanced Analytics and AI
Integration
Optimize IT Infrastructure for Big Data
Processing
Enhance Data Governance and Security
Encourage Cloud-Based Collaboration
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28. Conclusion
28
Cloudcomputingandbigdataplayimportantrolesindigital
transformationbyprovidingscalableinfrastructureanddata-
driveninsightsthatenableorganizationstoinnovateand
optimizetheiroperations.
@2025 RawanAlharbi. All Rights Reserved.

29. References
29
[1]Tang,W.;Yang,S.EnterpriseDigitalManagementEfficiencyunderCloudComputing
andBigData.Sustainability2023,15,13063.https://doi.org/10.3390/su151713063
[2]Merseedi,K.J.andZeebaree,Dr.S.(2024)‘CloudArchitecturesfordistributedMulti-
CloudComputing:AReviewofHybridandFederatedCloudEnvironment’,Indonesian
JournalofComputerScience,13(2).doi:10.33022/ijcs.v13i2.3811
[3]Berisha,B.,Mëziu,E.,&Shabani,I.(2022).BigdataanalyticsinCloudcomputing:an
overview.JournalofCloudComputingAdvancesSystemsandApplications,11(1).
https://doi.org/10.1186/s13677-022-00301-w
[4]Xu,M.,Zhang,Y.,Sun,H.,Tang,Y.,&Li,J.(2024).Howdigitaltransformation
enhancescorporateinnovationperformance:Themediatingrolesofbigdatacapabilities
and organizational agility. Heliyon, 10(14), e34905.
https://doi.org/10.1016/j.heliyon.2024.e34905
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30. Thank you
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