The document discusses important technologies in data architecture, focusing on NoSQL databases and real-time streaming platforms as essential for enhancing speed and responsiveness to customer needs. It also highlights the benefits of container technology and orchestration, which facilitate the deployment and management of microservices as well as FaaS for streamlined development. Lastly, it emphasizes the need for effective data governance and shared data assets to drive enterprise performance.

1. Data Architecture
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2. 2
Important
Technologies of
Data Architecture
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3. NoSQL Databases
NoSQL databases have been taking over other databases
because of their speed and ability to scale
In the case of document databases, they offer a far simpler
model from a software engineering perspective
This simpler development model increases speed-to-
market and helps the business respond more quickly to the
needs of customers and internal users
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4. Real-Time Streaming Platforms
Responding to customers in real-time is critical to the customer experience
01
Reason why consumer based industries is facing distress is due to the ability of companies to react to the
user in real time
02
Moving to a real-time model requires event streaming
03
The recent advancement in streaming technologies opens the door to many new ways to optimize a
business
04
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5. 5
Containers hold significant benefits for both developers and operators as
well as for the organization itself
In traditional technology, static partitions made it easier to troubleshoot issues, but at the
significant cost of delivering substantially underutilized hardware
The great benefit of container technology is its ability to create a new type of
isolation
Further, no matter how hard you try, those automation tools cannot create the isolation required to move
workloads freely between disparate infrastructure and hardware setups
Docker and Containers
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6. Container Repositories
A container repository is critical to agility
Mirroring capabilities between on-premises and the cloud might be vastly different than between
your on-premises data centers
With the repository, container images can be launched on any machine configured
to read from that repository
Without a develops process for building container images, and a repo for storing
them, each container would have to be built on every machine in which that
container could run
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7. Container Orchestration
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Instead of static hardware partitions, each container appears to be its
entirely own private operating system
Unlike virtual machines, containers do not require a static partition of compute
and memory
This enables administrators to launch large numbers of containers on servers
without having to worry so much about exact amounts of memory
With container orchestration tools like Kubernetes, it becomes very easy to launch
containers, kill them, move them, and relaunch them elsewhere in an environment.
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8. Microservices
Without these new
approaches to data storage,
event streaming, and
infrastructure orchestration,
large-scale microservices
deployments would not be
possible
The infrastructure
needed to manage the
vast quantities of data,
events, and container
instances would not be
able to scale to the
required levels
A service that is micro in
nature generally consists
of a single function, or a
small group of functions
The smaller and more
focused the functional
unit of the work, the
easier it will be to create,
test, and deploy the
service
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9. 9
Function as a Service
Faas enables the creation of
microservices in such a way that
the code can be wrapped in a
lightweight framework
The automatic invocation of the
functions is what makes Faas truly
valuable
It frees the developer to focus
almost exclusively on the function.
More specifically, it filters out only
certain types of records.
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10. 10
Principles of
Data
Architecture
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11. View Data as a Shared Asset
11
Provide the right Interfaces for users to Consume the Data
In order for people (and systems) to benefit from a shared data asset, you need to provide the interfaces that make it easy for users
to consume that data
Enterprises that start with a vision of data as a shared asset ultimately outperform their competition
Instead of allowing departmental data silos to persist, these enterprises ensure that all stakeholders have
a complete view of the company
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12. Ensure Security and Access Controls
Establish a Common Vocabulary
Product catalogs, fiscal calendar dimensions, provider hierarchies and KPI definitions all need to be common
The emergence of Big Data and Hadoop as a unified
data platform for enterprises has enabled—and
necessitated—the enforcement of data policies and
access controls directly on the raw data, instead of in
a web of downstream data stores and applications
Look to technologies that allow you to architect for
security, and deliver broad self-service access, without
compromising control
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13. 13
3GL
Application
4GL
Application
End – User
tool
Network
View
Relational
View
Flat Files
View
UDS Control Integrated Recovery
SFS Flat Files
DMS 1100
Database
RDMS 1100
Database
Repository
Audit Trail
User/ Application
Universal Data System
Physical Data
Data Architecture
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14. Big Data Architecture
StackComplexity
Value of Data
Hadoop Framework
Physical Hardware
Data Pipeline
Processing
Ingest
Staging
Access
Data & Workflow
Management
InsightData
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15. Data Architecture Icons Slide
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16. 16
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17. Our Mission
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Vision
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Mission
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Goal
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18. Meet Our Team
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01
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19. Timeline
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20. Puzzle
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Our Target

22. Venn
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23. Bulb or Idea
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24. 24
Charts & Graphs

25. Pie Chart
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Product 01
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Product 03
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Product 04
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Product 02
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Product 01
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Product 02
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Financial Years
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Bar Graph

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FY' 18FY' 17FY' 16FY' 15FY' 14FY' 14FY' 13FY' 12
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Product 01
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Product 02
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