Data and Application Modernization in the Age of the Cloud

Data and Application Modernization in
the Age of the Cloud
James Kobielus
Senior Director Of Research
for Data Management
TDWI WEBINAR
Copyright 2023, TDWI

JAMES KOBIELUS
Senior Research
Director,
Data Management
TDWI

Enterprises are modernizing their cloud data and
application infrastructures to sustain innovation,
differentiation, and efficiencies.

Digital transformation requires fast insights across cloud
• Data and analytics for accelerating
business insights
• Data and analytics that make
processes more agile, automated,
predictive, and contextual
• Data and analytics for running
processes efficiently in the cloud
• Data and analytics for orchestrating
processes 24x7
• Data and analytics for enforcing
strong governance and control of
complex processes
• Data and analytics for cross-domain
sharing of process intelligence
Source: TDWI Best Practices Report “Maximizing
Business Value with Data Platforms, Data
Integration, and Data Management,” Q3 2022

Data-driven applications serve every decision maker
Finance
How can we manage our company’s cash flow to
meet current operational requirements while
servicing our debts ?
Marketing
How can we improve
campaign performance
and better target
offerings to target the
right customer segments
at the right time?
HR
What factors
contribute to
superior employee
retention and
productivity?
Manufacturing
How much can
we reduce
downtime by
automating
machines’
operations?
Sales
• t
How can we
restructure our sales
channels and
engagement model
in order to boost
retention and
upsell?
Engineering
How reliable have
engineering teams
been at achieving
project goals on
schedule, and within
budget?
Procurement
How do our suppliers compare in
cost, quality, responsiveness, and
diversity?

Why migrate
your data
analytics
assets to the
cloud?
unified
consumable
accessible
consolidated
efficient
open
scalable

Every data analytics migration has some “lift and shift”
• Copy data to the new platform
• Revise data schemas
• Rewrite stored procedures
• Tweak query plans
• Develop complex joins
• Build new transformations
• Change database drivers
• Implement new source connectors
• Move processing jobs and data flows
• Migrate data integration, data protection, security, identity
and access management, and governance tooling
• Move and tweak legacy analytic applications and machine-
learning model libraries to work with the target cloud data
platform and topology
migration
legacy
database #1
legacy
database #2
legacy
database #…
legacy
database #N
target
cloud
database

Replatforming
Move data sets, analytic apps, server objects, and other assets
to cloud platform
Rearchitecting
Rebalance assets across distributed storage, compute, and
other hardware platforms
Consolidating
Converge data sets, stored procedures, and other analytic
assets from two or more legacy databases to a unified target
platform
Migrations may have several objectives

Migrations may involve cloud-native app/service refactoring
migration
Refactor data analytic
app and platform
functions as reusable
microservices (e.g.,
machine learning,
predictive analysis,
natural language
processing)
Containerize data
analytic
microservices
using standard
formats
Migrate containerized data
analytics microservices to
target cloud for orchestration
on standard platform (e.g.,
Kubernetes)
Modernizing
Converting all data analytic application, pipeline, and platform
functions to realize advantages of cloud-native computing

Migrating data analytics pipelines to cloud-native environments
∙ Unify DataOps and MLOps pipelines for more efficient CI/CD on AI, deep learning, and
machine learning applications.
∙ Leverage elastic cloud resources to parallelize containerized pipeline workloads
∙ Optimize containerized flows and jobs across zones
∙ Enable low-latency containerized stream processing
∙ Leverage AIops to automate all orchestrated functions
Data
ingestion
Data
staging
ETL DW/DL Data
access
and query
…..
Data prep Model
devel
Model
training
Model
serving
Model
eval
…..
DataOps
MLOps

Unifying management of data analytics assets
∙ Data and model governance: versioning of migrated
data and machine learning models
∙ Source-control repository: visibility into all migrated
models, code, APIs, containers, virtual machines,
business rules and other multicloud app pipeline artifacts
∙ Metadata hub: intelligent management of migrated
context, tags, glossaries, ontologies, feature vectors, and
other metadata
∙ Observability console: single-pane visibility into all
migrated models, code, feature vectors, APIs, containers,
virtual machines, business rules, and other multicloud
app pipeline artifacts

John de Saint Phalle
Senior Product Manager
Precisely

of data practitioners
completely trust
their data
of data practitioners
strongly believe
their actions are
driven by data
analysis
30%
27%
Data teams don’t trust their data
Can’t get it fast enough
Don’t understand it
Can’t trust it
Don’t have the context to use it
Don’t know when it’s going to break
Source: IDC

For trusted data,
you need data integrity
Data integrity is data with maximum
accuracy, consistency, and context for
confident business decision-making
Data
Integrity

Same data problems exist in new data clouds Precisely’s unique vision to solve those problems
Data owned by IT
Business &
IT collaboration
Slow, batch
ETL processes
Streaming
data pipelines
Data brought to
the solution
Data integrity processes
run where the data is
Separate business &
IT metadata
Shared catalog of technical
& business metadata
Massive, loosely integrated
products
Flexible, modular,
interoperable solution
Rules-based data
management
AI-driven rules, alerts,
and enrichment
Differentiated vision for modern data integrity

Mainframe Integration with AWS and Precisely
Two most common use cases
Mainframe Application
Modernization to AWS
Artificial Intelligence (AI)
& Analytics on AWS

Data Integration differentiators
Real-time data streaming gives you fast access to fresh data when and where you
need it
Business-friendly user interface allows first-time users to create data pipelines
without coding
Build once, deploy anywhere principle allows you to build data pipelines in the
Precisely Cloud and deploy them wherever your data lives
50+ years of domain expertise in mainframe and IBM i systems is built into the
Data Integration module to handle your complex data sources
Integration with the Data Integrity Suite Foundational enables Data Integration to
share metadata with other modules - exponentially building value and spurring
innovation

Positioning IT Teams for Success
Systems are current
and accurate
Data is democratized
Maximizing value of
current architecture
Setting foundation
for AI use cases

Fireside chat
20
Copyright © 2023 TDWI

When should enterprises consider
migrating and replicating all of their data
assets to modern public clouds vs. retaining
some on-premises in hybrid deployments?

How should enterprises modernize
their legacy data and application
infrastructures to unlock
innovation and value in the age of
cloud computing?

What are the key investments that
enterprises should make to modernize
their data pipelines to deliver better
AI/ML applications in the cloud?

What is the optimal data engineering
workflow for building, testing, and
operationalizing high-quality modern AI/ML
applications in the cloud?

What value does real-time replication play in
migrating data and applications to modern
cloud data architectures?

What challenges do enterprises face in
ensuring and maintaining the integrity,
fitness, and quality of the data that they
migrate to modern clouds?

What tools and methodologies should
enterprise application developers use to
transform legacy data applications that are
migrated to modern clouds?

CONTACT INFORMATION
If you have further questions or comments:
James Kobielus, TDWI
jkobielus@tdwi.org
John de Saint Phalle, Precisely
jdesaintphalle@precisely.com
tdwi.or

Thank you for attending!

Data and Application Modernization in the Age of the Cloud

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