1. Architecting an Edge to Core to Cloud Data Pipeline
Unify Analytics Engines with an In-Place Data Pipeline
Santosh Rao
Senior Technical Director, NetApp
Mar

2. Early Generation Big Data Analytics Platform
Big Data Analytics Software
Designed to Deliver Initial Analytics Solutions
Primary Considerations – Cost & Agility
Scalability, Availability, Governance are after thoughts
Typical Approach – Cloud or Commodity Infrastructure
Leading to Unpredictable RoI as Copies Manifest
3-5 Replicas copied across LoB, Functions
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3. Early Generation Analytics Platform Challenges
Unpredictable performance
Inefficient Storage utilization
File Copies
Storage Demon
File Storage Replication G
Media and node failures
Not enterprise ready
Total cost of ownership
Storage and compute tied
(creates imbalance)

4. Evolving To A Data Pipeline

5. Extending the Data Pipeline from Edge to Core to Cloud
Data Lifecycle Challenges
Edge
Core
Public Cloud
Initial point of data collection and aggregation
Dedicated hardware, private cloud deployments
Hosted as-a-service solutions; long-term data archival
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Multicloud

6. Traditional Data Architecture
Issues with Traditional on-premises Data workflow and Commodity Infrastructure Siloed architecture
Traditional Lambda architecture: Data processing pipeline
Notes
The limits of traditional on-premises workflow and architecture.
Let’s clarify what we mean by “traditional” —we are talking about non-services oriented architectures that may have siloed applications and modern applications built on virtualized infrastructures. In these architectures, data is not always free to move to where it is needed.
Challenges and problems with traditional architectures include:
Inflexible
Fixed ratio of compute to storage
Difficult to change database schemas
Costly
Hardware licensing and underutilization
IT support
Slow
Takes months to upgrade systems and to deploy new applications
Need to determine what is corollary in hybrid cloud web services
Creates data management challenges
Data copies
disk failures
I/O bandwidth
Archival and disaster recovery
Start with shared infrastructure and just have speaker note to ask audience if are on DAS and have statement to speak to that?
Users
Analytics
Services
Reports
Web tier/ App server
Stream analytics
Traditional data warehouse
Data Lake
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IoT Data
Hadoop Data Lake
NoSQL
Inflexible | Months to upgrade systems
Poor utilization | High TCO | Fixed compute to storage ratio
Inability to access the cloud
Data management challenges

7. Next Generation Data Pipeline
Unified Insights across Lines of Business and Functions
Unified Enterprise Data Lake
Federate Data Sources across 2nd and 3rd Platform
In-Place Access to Data Pipeline (Copy Avoidance)
Future Proof to allow shifts in Architecture
Deployment – PoC at LoB, Scale for Production Use
Scale Edge, Core and Cloud as a Single Pipeline
Governance, Data Protection, Security on Data Pipeline
Lowest TCO over life of Solution

8. Meet Diverse Needs across Enterprise Functions
Data Scientists
Real-world Data for App Dev
Data Architects
Future Proof Architecture
Data/IT Admins
Lowest TCO in face of shrinking budgets
We typically sell to the IT Admins on the right side
Seek Extensible Architecture:
Architecture spans Edge, Core and Cloud
Future Proof to allow shifts in deployment
Tiering is the new Scaling
No Data and Compute Sprawl
Architect for overall TCO
Balance Cost & TTM :
Reduce IT/Licensing costs
Automated Data Lifecycle Management
Meet SLAs
Non-Disruptive Operations
Need Agile Infrastructure :
Refreshed access to Production Data
Enable DevOps, Data Scientist
Enable Multi Tenant Data Science
Data, AI/ML Models as Code
API Based
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Balanced Architecture to Deliver for Stakeholders

9. Data Pipeline Requirements
Consider needs for each stage of the typical data pipeline
Edge
Core
Cloud
Data Ingest, Collection, Transport
Massive data (few TB/device/day)
Real-time Edge Analytics/AI
Ultra Low Latency
Network Bandwidth
Smart Data Movement
Edge
Data Ingest, Collection, Transport to the Core
Massive amounts of data growing exponentially
May require intelligence at the edge
Core
Prep in Data Lake, Training Clusters, Deployment, Transport to Cloud and to Edge
Demands raw I/O bandwidth for parallel operations to accelerate training phase
Requires ultra-low latency for access for deployed inference models
As amount of data grows exponentially, cost of storing data also grows exponentially
Cloud
Data Lake, Training, Deployment, GPU as a Service, Archive
Data lake requires performance for data ingest and streaming data into training cluster
Training cluster requires parallel processing
Deployed inference models require ultra-low latency access
Archive demands Cost-effective storage forever
Multi-cloud strategy requires data movement among
Data Lake, AI Model Training, Deployment, Serving
Ultra High I/O bandwidth ( GBps)
Ultra-low latency (micro – nanosec)
Linear Scale (1-128 node AI)
Overall TCO for PB
Data Lake, Training, Deployment, GPU as a Service, Archive
Cloud Analytics, AI/DL/ML
Consume and not Operate
Cloud Vendor vs. On-Prem stack
Cost-effective Archive
Need to avoid Cloud Lock-in

10. AI Infrastructure Guiding Principals: 5 Factors
Smoothing the flow of the Data Pipeline
Choice of Filesystem (Lustre, HDFS, GPFS, NFS)
Ability to federate diverse data sources both structured and un-structured
Smart Data Movement
Data as a Service
Leading Edge Performance (NVMe, NVMeoF, NVDIMM, 3DXPoint)

11. Tech Topics:10 Dimensions of AI Infrastructure
Smoothing the flow of the Data Pipeline
Both Random and Sequential IO
Ultra Low Latency for Inference
Ultra High Bandwidth/Parallelism for Training
Linear Scale Single Namespace & Metadata
Client side Caching for Iterative Scans
Copy Avoidance
In-Place Access to Data (HPC, Analytics, AI/DL)
Availability of Smart Data Movement
Ease of management at Scale
Service Levels for Model Serving
Need to uplevel the story

12. Our Solution: NetApp Data Pipeline
Extends from Edge to Core to Cloud. Federates Data Sources, Compute Engines and Clouds.
Notes
Workflow diagram
Begins with data collection (metadata); messages generated and transmitted to NetApp
Data ingested
Data processed and aggregated (prepared)
Data archived and secured
Data analytics performed (machine learning)
Data services delivered
Data management: Copy management, data storage efficiencies, data movement, data availability, data security (this is unique to NetApp)
Details
AutoSupport v2 on-prem analytics architecture
Replaced HDFS with AFF for real-time cluster
Reduced our storage usage from 12PB on DAS to 1.3PB on AFF
Reduced compute nodes from 120 to 40
Additional Info
Not only are we using our own data to drive a digital transformation through predictive analytics, we are delivering it through the NetApp Data Fabric.
We have over 20 years of experience in managing IoT data through our history of AutoSupport.
We have used the innovation through the Data Fabric solutions to securely take advantage of the scale of the public cloud to deliver Machine Learning and AI analytics through the NFSaaS.
BD Cluster
AI/DL Cluster
IoT Data
HDInsight
HDFS-NFS
In-Place Analytics
In-Place AI/DL
Databricks/EMR
NetApp HDFS-NFS Connector
NetApp HDFS-NFS Connector
HDFS/NFS
ExpressRoute
Archive
Direct Connect
Data Lake
Unified Data Lake
Data Fabric
NetApp Cloud Volumes
Secure Data

13. Data Pipeline for Artificial Intelligence / Deep Learning
Edge
Core
Cloud
Data Aggregation Data Lake High Performance DevOps Cloud AI/DL
Data Fabric
Ingest
Data Prep
Training Cluster
Deployment
Training Set 3
Training Set 2
Test Set
Training Set 1
Repo
Data Lake
IM1
Public or Private Cloud
Archive
IM2
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IM3
Data collection
Edge-level AI (TensorRT)
Aggregation
Normalization
Raw I/O bandwidth
Parallel streams
Ultra-low latency
Model Serving

14. Hybrid Cloud Data Pipeline
Ingest
Edge
Near the Cloud
Cloud
Deployment
Small Footprint
Smart Data Movement
Compute & Data Separation
Data as a Service, Near Cloud Data, Data Tiering, Cloud AI, GPU as a Service
Public or Private Cloud
Archive
Data lake
Training Set 3
Training Set 2
Test Set
Training Set 1
Repo
IM1
IM2
IM3
Training
Express Route
Direct Connect
AI / Deep learning in the cloud
Cold data, Backup, Clone
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15. Key Takeaways Edge Cloud Core Future Proof Accelerate DL
NetApp Data Pipeline : Building Blocks for your entire data flow—From Edge to Core to Cloud
Ultra-High Bandwidth
Edge
Cloud
Ultra-Low Latency
Data Fabric for AI
ONTAP Select
ONTAP Cloud
Core
ONTAP 9 AFF
Smart Data Mover
Future Proof
Accelerate DL
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16. Thank You
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17. Backup

18. Solution Architecture for the Data Pipeline
Edge
Core
Cloud
On-Premises Deep Learning Pipeline
AFF Flexpod for DGIX/Plexistor
Data Prep
Data Lake
Training Cluster 1 2 3
Training Sets
Deployment
IM3
IM2
IM1
Repo
ONTAP Select
© 2018 NetApp, Inc. All rights reserved NETAPP CONFIDENTIAL ---
Cloud-Based Deep Learning Pipeline
ONTAP Select

19. IDC Study : NetApp vs. Commodity TCO