The document discusses big data analytics and the DataCanvas platform. It notes that the big data market was worth $16.9 billion in 2015 and areas like government, communication, banking, and manufacturing are seeing heavy investment in big data. It then describes the DataCanvas platform as providing an intuitive way to build workflows and pipelines to enable flexible data analysis, product analysis, and predictive services through drag and drop functionality and module sharing. The platform aims to address issues with unmanageable configurations and lack of reuse in existing solutions by providing collaboration, reusable modules, and solution templates to help users build and operate advanced analytics.

1. Lei Fang@
DataCanvas.io
Empower big data analytics for business

2. • 16.9B USD in 2015
• 40% Big data project
• Hadoop, CAGR 58%,
2.2B 2020

3. • Volume
• Velocity
• Variety
Super hot in
• Government
• Communication
• Media
• Banking
• Manufacturing

4. Technology
Infrastructure
IAAS, SAAS, DAAS,
Application
BI, Social analytics,
visualization…
Domain solution
Finance, Retail,
Insurance
Development
Data scientist,
Devops
Business process
Operation,
Support

5. Make data live
 Data sitting in storage generates no value
Revenue and profit from data
 Application and solution to get insights from data
 Link insights with business
 Don’t stop at visualization or report
Advanced analytics is the engine of business solution
 Fraud detection
 Customer retention

6.  Data analysis
 Example: Estimate customer’s life cycle value
 User: data scientist
 Demanding: flexibility to explore and faster iteration
 Product analysis
 Example: How many female customers visit website home page and
leave within less than 5 clicks?
 User: product manager, data analyst, marketing team
 Demanding: No complex coding, SQL query at most
 Predictive service
 Example: Is this transaction a fraud?
 User: developer and data scientist
 Demanding: pipeline processing

7.  Powering all these scenarios
 Data Analysis: Flexible
 Product Analysis: Intuitive
 Prediction service: Complex processing
 Enable application, solution and business process
DataCanvas

8. Hadoop(HIVE/Pig) RDBMS NOSQL SPARK
Recommendation Anomaly Detection Operation Analytics
Application
Platform to enable application and connect infrastructure
Service
Pipeline
Infrastructure

9. • Big data challenges are across services,
environments and even locations
Storage
Processing
Reporting
Data Generation
• An orchestration platform is required to
manage and connect steps in the pipeline
• Bring Pipeline to the game

10. No more central data store, bring
computation to data, not vice versa!
• Unify resource
• Optimize workload
• Automation

11.  Unmanageable
 Redundancy
 Hard to fast iterate
 Gap between documentation and
actual workflow
Pain points
monster configuration
spaghetti script no reuse No idea what’s actually running

12. • Drag & drop to run data flow
• Public or private cloud
• Intuitive job management
• Module repository
• Built-in library
• Make your own recipe
• Powering advanced analytics
• Business solution template
• Address common applications
• Fully customizable
• Team collaboration
• Flow sharing
• Module sharing
• This is the BEST documentation

13. Workflow
Scheduling
Module
Solution
Template
Operation Developer/Data scientist Business
• Data ETL
• Machine learning
• Module repository
• Business requirement
• Recommendation
• Fraud detection
• Sentiments analysis
• User experience
• Production
quality
• Easy ops

14. • Seamlessly connect to any existing/
upcoming computation infrastructure
• Enabler for module management and
sharing
• Support Lambda: Processing + Serving
+ Visualization
Lambda Architecture

15. AWS DP Oozie AzureML MortarData Azkaban DataCanvas
Workflow + Scheduling
Module management
Solution template
Multiple Env support
Collaboration + Sharing
Cloud service
DataCanvas = ((Workflow + Scheduler) * Drag & drop * Module composition ) ^ Solution @ Cloud
Good
Bad or not support
Not that great

16.  Subscription
 Charge services on tiers, Startup, Premium, Enterprise
Free
• 1 user
• Unlimited
projects
• Limited
workload, good
for evaluation
• Forum support
Startup
• Unlimited users
• Unlimited
projects
• Decent workload,
3-5 jobs in
parallel
• Email support
Premium
• Unlimited users
• Unlimited
projects
• Significant
workload, >20
jobs in parallel
• Email support
Enterprise
• Unlimited users
• Unlimited
projects
• Workload on
scale
• Full support
 Annual Support Package
 For Premier and Enterprise customers
 Forum support, Email support with SLA, Telephone support

17.  Data scientist
 Assembly line to facilitate exploration
 Team collaboration
 Analyst
 Drag and drop to find insights, need any more reason?
 Manager
 Faster iteration
 Shorter time to deliver project
 Easier to maintain

18. Demo upon request (contact@zetdata.com) DataCanvasIO @ GitHub

19. THANK YOU