The world of database management is changing. Cloud adoption is accelerating, offering a path for companies to increase their database capabilities while keeping costs in line. To help IT decision-makers survive and thrive in the cloud era, DBTA hosted this special roundtable webinar.

1. Using Cloud Automation Technologies to Deliver an Enterprise Data Fabric
Sean Martin, CTO
Cambridge Semantics Inc.
DBTA, 21 April 2020

2. • Big data volume
• Ad hoc queries
• Unstructured
• Semi-structured
• Exploratory
• Raw
• Self service
• On-demand
• Clean
• Consistent
• Integrated
• Accessible
• Searchable
• Secure
• Governed
• Privacy (PII)
• Clean
• Consistent
• Integrated
• Accessible
• Searchable
• Secure
• Governed
• Privacy (PII)
• Clean
• Consistent
• Integrated
• Accessible
• Searchable
• Secure
• Governed
• Privacy (PII)
• Big data volume
• Ad hoc queries
• Unstructured
• Semi-structured
• Exploratory
• Raw
• Self service
• On-demand
• Cataloged
• Linked
• Modeled
• Persisted
• Virtualized
• Collaborative
Data Fabrics are the modern successor to warehouses and lakes.
Fully connected and integrated
Data Fabric
Data Lake
Data
Warehouse
Flexibility and scale
Quality and control

3. RDBMS/OLTP Big Data / Hadoop Document Repositories
Traditional BI Cloud
CLAIM
CUSTOMER
PRODUCTS
POLICY
Semantics and graph allow the data fabric to be an overlay spanning
and encompassing the existing data and analytics landscape.

4. • Patients
• Encounters
• Providers
• Medications
• Costs
• Care Plans
• Claims
• Etc.
Providers
Care
Plans
Patients
Costs
Inpatient
Claims
Carrier
Claims
Outpatient
Claims
Prescriptiom
Drug_Events
Beneficiary
Summary
BestPractiseLinks
careprog2
careprog1
Medications
Patient
Encounters
Observations
Conditions
Allergies
Patients
Procedures
Imaging
Studies
Immunizations
Care
Plans
care planscanonicalelectronic medical records claims
How it works: Business Friendly Data models
Semantic Graph data models to capture and navigate data relationships

5. Real World Graphs
Get Big Fast
Vast
Hundreds of sources, representing
thousands of entity types
Siloed
Different technologies, schemas,
formats
Complex
Sprawling disconnected schemas,
wide flat tables, and cryptic names
Unstructured
documents, emails, logs
Valuable
Hidden connections and common
business definitions

6. Graph Data Models & Semantics
Simplifies access to complex data to address
unanticipated questions
Quickly profiles, connects and harmonizes data
from multiple sources, including unstructured
Presents tailored views and experiences
to different personas with conceptual models
Flexibly accommodates new data sources
and use cases on the fly, with minimal impact
Scales horizontally to accommodate enterprise
data fabric scale

7. What it is
● An Enterprise Data Fabric Platform
○ Metadata Hub
○ Data Catalog
○ GraphMarts
○ Data Layers (where graph data blending happens)
○ REST Query Service Endpoints
○ Hi-Res Graph Aware Dash-Boarding Tool
What it does
● Accelerated Data Integration as Services
○ Creates and stores a vast metadata description of the
enterprises data landscape
○ Creates and stores metadata describing the
transformations required to turn all raw data sources into
a well described Enterprise Knowledge Graph
○ Automates the on-demand creation of the portions of the
Knowledge Graph and brokers query access to it

8. What it is
● The first Graph Data Warehouse
○ GOLAP (Graph Online Analytics Processing)
○ In-Memory Massively Parallel Processing (MPP)
○ Linear Scale (Largest cluster 200x64 CPU servers)
○ Like Snowflake or AWS Redshift, but for Graph
○ Enterprise Scale Knowledge Graphs
What it does
● Accelerated Data Integration & Analytics
○ ELT & Data Virtualization (VKG)
○ Knowledge Graph ingested from data sources using > 200
data source connectors
○ Reporting and BI analytics & aggregates
○ Graph Algorithms e.g. Page Rank, Shortest Path
○ Data Science libraries & Feature Engineering
Transformations e.g Matrices, PCA, SVD
○ Labeled property graph (LPG)
○ Inferencing, windowed aggregates & views
Standards
• Supports Open Standards
Supports RDF* and SPARQL
1.1 standards
Where you can deploy
• Fully Automated Deployment
on premises or on cloud with
Kubernetes Operator

9. Automation with Kubernetes

17. Kubernetes / K8s

18. Kubernetes API &
AnzoGraph Operator

19. Kubernetes API &
AnzoGraph Operator
Kubernetes Cluster

20. Kubernetes API &
AnzoGraph Operator
Kubernetes
Container
Kubernetes Cluster

21. Kubernetes API &
AnzoGraph Operator
Kubernetes
Container
Cluster

22. Kubernetes API &
AnzoGraph Operator

23. Kubernetes API &
AnzoGraph Operator

24. Kubernetes API &
AnzoGraph Operator

25. Kubernetes API &
AnzoGraph Operator

26. Kubernetes API &
AnzoGraph Operator

27. Kubernetes API &
AnzoGraph Operator

28. Who supports Kubernetes?

29. The Kubernetes API provides a common automation abstraction across all cloud
providers as well as on-premises implementations which allow us to deliver a hybrid
multi-cloud deployment model for Anzo Enterprise Data Fabric with very low switching
costs.
Because all data transformation mappings, graph linking & blending instructions and all
computing configurations are held as metadata in Anzo, customers can decide both
when and where to deploy their data integration and analytics computing at the most
granular level.
This allows them to take advantage of the best available pricing and to more easily
keep some workloads (and their data) behind their firewalls.

37. Thank You