The document discusses data mesh vs data fabric architectures. It defines data mesh as a decentralized data processing architecture with microservices and event-driven integration of enterprise data assets across multi-cloud environments. The key aspects of data mesh are that it is decentralized, processes data at the edge, uses immutable event logs and streams for integration, and can move all types of data reliably. The document then provides an overview of how data mesh architectures have evolved from hub-and-spoke models to more distributed designs using techniques like kappa architecture and describes some use cases for event streaming and complex event processing.

1. Data Fabric or Data Mesh?
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2. Data Fabric or Data Mesh?
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3. 3Copyright © 2020 Oracle and/or its affiliates.

4. What is a Data Mesh?
4
Microservice
Patterns
Log-based
Integrations
Polyglot Data
Movement
Data Mesh is a data-tier architecture to integrate and
govern enterprise data assets across distributed multi-cloud
environments – two defining characteristics are:
(1) De-centralized data processing; no ETL/Hubs/Lake monoliths
(2) Event-driven; real-time where possible, batch only when necessary
Microservices-centric:
• For the administration, deployment and monitoring of the core
frameworks of data movement and governance
• “Sidecar Proxy” style pattern for Events and Data; Aligns with
Service Mesh frameworks (Kubernetes, Istio, etc)
Immutable event-logs for data integrations:
• Messaging and data store events are globally accessible via
immutable event logs
• Logs may be used to drive Streaming or Batch integrations
Distributed data movement of all types of data
• A data mesh moves data: Relational, NoSQL, JSON, Graph…
• Relational data consistency (ACID) during data movement
• Must work reliably with enterprise OLTP data sets
https://en.wikipedia.org/wiki/ACID
Data
Mesh
Event
Streaming
Immutable
Logs
Data
Replication
Polyglot
Persistence
Edge / 5G
Frameworks
Domain
Driven
Design
Service Mesh
“Sidecars”
Data
Mesh

5. Evolution towards Real-Time Data Mesh
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Industry 3.0: Hub and Spoke Transitional: Kappa Hub Mature: Distributed Kappa
This data pattern, popularized by Ralph
Kimball and Bill Inmon, has been the
foundation for enterprise data
management since 1993.
It is transaction consistent, can scale up
nicely for most use cases, and is based on
SQL, lingua-franca for most tools.
By 2010, the Lambda (big data) pattern
was common. In 2014, Jay Kreps (of
LinkedIn) questioned the Lambda
Architecture and spawned Kappa.
The Kappa principles consider batch
processing as a special case of stream
processing. Use a historized event log to
process both real-time as well as batch
processing.
By 2020, IT infrastructure has
dramatically changed – networking,
containers, cloud, compute, IoT etc have
all pushed data to the edge.
A mature Kappa architecture is not a
single instance “hub” but rather a
distributed mesh of data logs, stream
data processing, change events, and time
series data.
Kappa: https://www.oreilly.com/radar/questioning-the-lambda-architecture/
https://en.wikipedia.org/wiki/Dimensional_modeling
mesh & microservice controls
5
ETL
ETL
ETL
ETL
Lambda: http://nathanmarz.com/blog/how-to-beat-the-cap-theorem.html
Monoliths Distributed

6. Data Mesh Conceptual View – Data Domains
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Enterprise Data
Producers:
ERP Apps, DBs,
Middleware etc.
Data Domain
Consumers
People owners of “Data
Products”, collections of
data sets in various
stages of curation
IoT Data
Producers:
Devices & Things
Raw Data
Prepared Data
Canonical Data Data
Domain A
Data
Domain B
f(x)
f(x)
Data
Domain C
Data Mesh
(distributed Kappa, microservices, cloud agnostic)
Domain-Specific Views of Data
Raw Event Consumers
Automated Devices,
Edge Nodes (5G), Scheduled
Routines (eg; ETL etc)
Data Product-Specific
Storage Choices:
• RDBMS
• Data Lake
• Object Store
• Graph, etc.

7. Raw data, Time Series & Alerting events are pushed
Direct to Database (high fidelity transaction semantics fully preserved)
Consumer-Driven, Event-Centric Data Mesh
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Enterprise Data
Producers
Detect
Event
Logical
Change
Records
(LCRs)
App
DB
committed!
CDC Replication
Data Domain
Consumers
Data
Objects
Table
Data
Raw Data
/ Alerts
SQL
Consumers
Raw
Data
Prepared
Data
Canonical
Data
Raw Data (LCR)
Schema Events
(DDL)
Prepared
Data Topics
“Master”
Data Topics
JSON, XML,
Avro, Parquet,
CSV
Prepared data events are pushed
Canonical data events
Speed &
Fidelity
Trusted
Views
Ease of
Consumption
LCR/TFs
Applications,
Data Services
Biz Consumers
Analytics &
Data Marts
Data Science
& Streaming
Applications
DBAs for HA,
DR and OLTP
Data Mesh puts the consumer
needs first – they require data
at different latency, fidelity,
trust levels and views
Data Model
Object Model
System
Of Record
(SoR)
User
Action
App APIs and
system log events
7

8. Direct to Database (high fidelity transaction semantics fully preserved)
Distributed by Design, Microservices Based
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Data Domain
Producers
Detect
Event
Logical
Change
Records
(LCRs)
App
DB
committed!
Data Domain
Consumers
Data
Objects
Table
Data
Raw Data
/ Alerts
SQL
Consumers
Data Model
Object Model
System
Of Record
(SoR)
User
Action
CDC Replication
Microservices
Edge Compute
or Cloud for
Raw Data
Events
Prepare
Technical Data
Views
LCRs
Business
Data Views
Raw data, Time Series & Alerting events are pushed
Prepared data events are pushed
Canonical data
Events
(ephemeral or persisted)
Stream
Process
Events
(persisted)
Stream
Process
Events
(persisted)
Applications,
Data Services
Biz Consumers
Analytics &
Data Marts
Data Science
& Streaming
Applications
DBAs for HA,
DR and OLTP
8

9. Single Pane of Glass for Real-Time Data Mesh
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connect
DB2/z
Data
Objects
Table
Data
Raw Data
/ Alerts
SQL
Consumers
Applications,
Data Services
Biz Consumers
Analytics &
Data Marts
Data Science
& Streaming
Applications
DBAs for HA,
DR and OLTP
Real-Time Stream
Data Processing
Raw
Data
DBAs &
Data Engineers
Data Owners &
Data Products
9
Data Consumer DrivenEvent Centric Pipelines
Deploys in a Mesh
Across Containers, Public Clouds and 5G Edge Devices

10. Oracle Focus on Operational Data
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DATA
DATA
Oracle data mesh/fabric solution strength in Operational and Analytic use cases
Oracle is only DI vendor that customers trust for 99.99999% up-time SLAs
Business
Applications
Systems of Record
Data Stores
Analytic
Services
Analytic
Data Stores
OLTP Replication, Migrations,
High Availability, Recovery
Data Warehouse, Data Mart,
Data Lake, NoSQL, etc.

11. Stream Processing/CEP for Event Driven Architectures
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There has been a widespread
awakening to the benefits of Event
Drive Architecture (EDA) for
increasing the scalability and agility of
business systems. […] Stream
analytics is based on the mathematics
of complex-event processing (CEP).
CEP is a computing technique in
which incoming data about what is
happening (event data) is processed
as it arrives (data in motion or
recently in motion) to generate
higher level, more useful, summary
information (complex events).
W. Roy Schulte (of Gartner), March 2020:
EDA is Suddenly Popular Will Stream Analytics be Next?
Event Stream Analytics (& CEP)
Data & Microservice Events
Event/Data
Pipelines
Time-Series
Analysis
Geospatial
Analysis
Real-time
AI/ML
Continious
ETL
Use Cases:

12. How it Works Today: GoldenGate for Big Data
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Data Domain
Consumers
Data
Objects
Table
Data
Raw Data
/ Alerts
SQL
Consumers
Applications,
Data Services
Biz Consumers
Analytics &
Data Marts
Data Science
& Streaming
Applications
DBAs for HA,
DR and OLTP
BYOS (Bring Your Own Spark)
* distributed, may run on any combination of containers and clouds
12
Data Engineer Data AnalystDBA/GG Ops
Capture Pipeline Analyze DeliverIngest
GoldenGate Microservices Applications Stream Analytics Application
BYOM
(Bring Your
Own Messaging)All Data Events
& Transactions

13. DEMO
SCENARIO

14. Today’s Demo: Retail / Inventory Analysis
Training
Data
Customer
Data
Merchandising
Data
Orders
Data
Data Preparation
Data Science
Data
Flow
Obj
Store
Prepared
Bulk Data
Prepared
Event Data
Autonomous Data Warehouse
Real Time
Analytics
Mobile / SMS
Alerts
Data / Micro
Services
Data
Visualization
ML
Model
Data Catalog
Weather
Data
Analytics Cloud
Real-time Inventory Alerts, Data
Integration, and Predictive Stocking
Self-Service Data Preparation, Data
Integration and Data Visualization
Data Governance, Search and Access

15. Today’s Demo: Retail / Inventory Analysis
Training
Data
Customer
Data
Merchandising
Data
Orders
Data
Data Preparation
Data Science
Data
Flow
Obj
Store
Prepared
Bulk Data
Prepared
Event Data
Autonomous Data Warehouse
Real Time
Analytics
Mobile / SMS
Alerts
Data / Micro
Services
Data
Visualization
ML
Model
OCI Data Catalog
Weather
Data
Analytics Cloud

16. DEMO

18. Copyright © 2020, Oracle and/or its affiliates. All rights reserved. |
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