Creating new data lakes or migrating existing ones on the cloud has been a de-facto trend. With reduced costs and agile infrastructure, it is easier to derive business value on the cloud. While there are so many choices for architecture, tech stack, and solutions available on various platforms to fit your use case, it is imperative to consider the key mantras and lifeline principles that will help you succeed in your cloud data lake journey. In this webinar, we share the best practices and the key considerations that will help you build a robust data lake architecture on the cloud. To view the webinar - visit https://bit.ly/2sG8BAp

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2. Best Practices to Build a Sustainable
Data Lake on Cloud
IMP

3. Our mission
Enabling a unified, clear, and present view of
your business

4. Agenda
Data Lake Essentials
The Cloud Advantage
Cloud Adoption Strategy
Keys to Building a Robust Data Lake on Cloud
Q&A

5. Poll

6. Speakers
Mita Baxi
Solutions Architect
Chetan Kalanki
Sr. Director
Big Data and
Cloud Engineering

7. The Cloud Opportunity

8. -Gartner
The worldwide public cloud service market will grow to $331.2 B in 2022

9. -MarketWatch
Data Lakes Market will touch an aggregate of $12.01 B by 2024

10. -Gartner
If you have not developed a cloud-first strategy yet, you are likely falling behind your competitors

11. What’s in it for me?
Key influencers in building a data lake on cloud
Best practices for building a sustainable data lake on cloud

12. What is a Data Lake?
Data Lake
Database
Logs
XML Data
Spreadsheet
Text

13. Data lake: Key functional aspects
Ingest Store Process Analyze Consume

14. Data Privacy, Security and Access Management
Data Management
Data lake: Governance essentials
Ingress
Data Discovery and Curation
Profiling | Classification | Lineage | Prepare
Metadata Catalog | Catalog | MDM | Archive
Quality
Physical | Encryption | Access | Audit
Data Discovery, Reporting and Visualization

15. End-to-end big data lake capability view
Metadata / Governance Layer
Streaming
Structured (ODS) /
Unstructured
Geospatial /
Machine /
Time Series
External Social
RawLayer
ProcessedLayer
Landing/
Staging
Active Archive
Common Data
Model
ODL – Rest /
Motion
Master
Reference Data
API Data
Mart
Stewardship/
Policies
QuickSight
Athena
SLA-BasedDataVendingLayer
Sandbox
Stores
Post Analytics
Store
Search
ODL-End
User
Lineage
Service
Catalog
Audit /
Monitoring
Workflow
/DQ
Workflow
/DQ

16. Why cloud?
Agility
Scalability
Security
Economics
Advanced analytics

17. Success criteria
Pace of adoption
Footprint
Time-to-market
Innovation
ROI and TCO

18. Cloud adoption cycle
Excitement
Realization of Complexity, Effort
and Impact on Individual
Transformation
Deployment
Chaos
Performance
Time
Integration

19. Key influencers
Process Technology
People

20. Critical concerns
Knowledge
Funding and cost
Purchasing
Resistance
Plan

21. Poll Results

22. Best Practices

23. Build a solution from an enterprise view
Goal alignment
Budgeting
Chargebacks
ROI assessment
Assess reskill needs

24. Focus on value delivery
Adopt cloud first commitment
Have flexibility to change
Use accelerators

25. Build a foundation first
Security should be your top priority
Address key challenges first
Reusable templates
Logging and monitoring
Financial controls

26. Select the right tooling
Data integration
Data catalog
Data quality
Data governance

27. Fail fast
Start small
Perform proof of concept
Extend and enhance
Fail fast, recover fast
Measure

28. Assess lock-ins
Which layer do you want to lock-in?
- Tool
- Framework
- Platform
- Cloud
- Solution

29. Build for expansion
Business expansion
Geographical compliances
Integrations

30. Build for speed of change
DevOps
Automate
Cloud-native tools
Engage experts

31. Know and optimize expenditure – Always
Hard cost
Soft cost

32. Measure everything you can
Usage data
Security metrics
Cost parameter
Skills level

33. Recap

34. Advisory and Consulting
Cloud Enablement and Migration, Big Data Enablement and Migration, Data Lake,
DevOps, Usability, Mobility etc.
Architecture, Design and
Engineering
Technology Evaluation and Benchmarking, Solution Design and Architecture,
Engineering, Quality Assurance, NFRs and Performance Engineering etc.
Enterprise Data Management
Data Lake, Data Modelling, Data Migration, Data Visualization, Data Democratization
and Governance etc.
DevOps and Productionization
Capacity Planning, Infra as Code, Infra Provisioning, Automation and Administration,
Ops Support etc.
Data Science and Analytics
NLP and NLG, AI and Deep Learning, Descriptive-Prescriptive-Predictive Analytics,
Sentiment Analysis etc.
User Experience
UX Design and Architecture, Rich Media Design, Mobile App Design, Responsive
Design, UX Lab Assessment etc.
Our Services

35. Impetus cloud practice competencies
Amazon Web Services
Microsoft Azure
Google Cloud
Salesforce
Advisory, strategy,
TCO
Architecture evaluation
Cloud infrastructure
realization
Cloud cost optimization
Workload assessment
and transformation
Capacity planning
Automation and
orchestration
Security and
governance
DevOps
Maintenance
and administration

36. Thank you. Questions?
Visit www.impetus.com or get in touch with us at inquiry@impetus.com