Big data prototyping in AWS cloud

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This document discusses using AWS cloud for rapid prototyping of big data minimum viable products (MVPs). Key benefits of cloud computing include no upfront costs, ability to scale quickly, and pay only for resources used. Typical big data use cases involve retrospective analysis, real-time processing, and machine learning. The document demonstrates building a basic Hadoop cluster on AWS in 15 minutes for word count analysis. It recommends measuring MVP outcomes and considering ongoing cloud use based on needs for 24/7 access, internal resources, and security requirements.

Technology

Big Data
Prototyping in
AWS Cloud
Samuel Yee
AWS Certified Solutions Architect

Why Cloud Computing?
• Rapid prototyping for Minimum Viable Product (MVP)
• No need to worry about what-to-buy, what-if-buy-wrong etc.
• Scale up and out if demands pick up
• Tear down at minimal cost if no demand or project failure
• Deploy anytime and everywhere
• Worldwide infrastructure
• Flexible, elastic, agile
• No high upfront costs
• Pay-as-you-use model
• Can start instantly without much cost justifications

Typical Big Data Use Cases
Retrospective
Analysis and reporting
Here-and-Now
Real-time processing
and dashboards
Predictions
Machine learning and
smart applications

Typical Big Data Pipeline
Ingest Process Store
Analyse
(BI)
Data Answer

Word Count Example
Ingest Process Store
Analyse
(BI)
Data Answer

Demo
• Spin your new Hadoop cluster in AWS within 15 minutes
• Terminate your Hadoop cluster instantly when you don’t need it

Data Types Ingest Process Store Analyse (BI)
Flat Files
Database
Data
Streaming
Data
Hadoop-as-a-Service
File Objects
Database Objects
NoSQL Data Store
Desktop Apps
Enterprise Apps
Open Source

What’s Next?
• Measure the outcomes of your data projects
• No need – Change yourself or educate your users/bosses
• Fail – Try to understand the needs of your users/bosses better. Deliver faster!
• Success – Congratulation!
• Should I continue in AWS cloud?
• Do I need it 24x7, anywhere?
• Can I move my system and network admins to host my Big Data apps?
• Do I have the resource, time & expertise to deal with the intricate details of Hadoop
cluster configurations and troubleshooting?
• How do I secure my big data infrastructure?
• If answer is still no, go back to the Big Data Landscape chart. But at least you
can keep your users/bosses happy and engaged with your MVP in cloud.

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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management. The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM). Speakers: Bob Boule Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle. Gopinath Rebala Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.

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Big data prototyping in AWS cloud

1. Big Data Prototyping in AWS Cloud Samuel Yee AWS Certified Solutions Architect

2. Minimum Viable Product (MVP)

3. Why Cloud Computing? • Rapid prototyping for Minimum Viable Product (MVP) • No need to worry about what-to-buy, what-if-buy-wrong etc. • Scale up and out if demands pick up • Tear down at minimal cost if no demand or project failure • Deploy anytime and everywhere • Worldwide infrastructure • Flexible, elastic, agile • No high upfront costs • Pay-as-you-use model • Can start instantly without much cost justifications

4. Typical Big Data Use Cases Retrospective Analysis and reporting Here-and-Now Real-time processing and dashboards Predictions Machine learning and smart applications

5. Typical Big Data Pipeline Ingest Process Store Analyse (BI) Data Answer

6. Word Count Example Ingest Process Store Analyse (BI) Data Answer

7. Whichonetobuy?

8. Demo • Spin your new Hadoop cluster in AWS within 15 minutes • Terminate your Hadoop cluster instantly when you don’t need it

9. Data Types Ingest Process Store Analyse (BI) Flat Files Database Data Streaming Data Hadoop-as-a-Service File Objects Database Objects NoSQL Data Store Desktop Apps Enterprise Apps Open Source

10. What’s Next? • Measure the outcomes of your data projects • No need – Change yourself or educate your users/bosses • Fail – Try to understand the needs of your users/bosses better. Deliver faster! • Success – Congratulation! • Should I continue in AWS cloud? • Do I need it 24x7, anywhere? • Can I move my system and network admins to host my Big Data apps? • Do I have the resource, time & expertise to deal with the intricate details of Hadoop cluster configurations and troubleshooting? • How do I secure my big data infrastructure? • If answer is still no, go back to the Big Data Landscape chart. But at least you can keep your users/bosses happy and engaged with your MVP in cloud.

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