Logical Data Fabric and Data Mesh – Driving Business Outcomes

Data Mesh
Data Lakehouse
Data Lake
Data Fabric
Cloud Data Warehouse

Monolithic Architecture
• Monolithic: Physically centralize data in a single location
(e.g. data lake/house)
Challenges (one size never fits all)
• Takes time & effort: intensive data replication for each new
data need
• Difficult to maintain: changes require modifying pipelines
and datasets
• Incompatible: Existing analytical systems cannot be
reused; need to ingest all data into a new system
…for Data Integration and Management
Stop collecting.
“Inherent in the LDW architecture is the recognition that a
single data persistence tier and type of processing is
inadequate to meet the full scope of modern data and
analytics demands.”
– Gartner: The Practical Logical Data Warehouse, Dec 2020, by
Henry Cook, Rick Greenwald, and Adam Ronthal

• Logical: Consumers access data through semantic
models, decoupled from data location and physical
schemas
•
•
•

SAS Macros
▪ Too many query points
▪ Heterogenous technologies
▪ Complex source systems
▪ Scattered business rules
▪ Semantic layers in BI
▪ Business logics in DB views
▪ Many points of access control
▪ Audit points all over the place
▪ Each system has its own access control
KPI DB Source DBs New DWH Old DWH Markets DB
Views
WebI
Lumira
Crystal
reports
Live Office
Views
Views
Views
General Reporting
KPI
SAS EG SAS VA
VA Server
Risk Reporting
Monitoring / Audit Business security
Business rules
Board
Other DBs
BO Semantic Layer (Universes / DF)
Data
Sources
Semantic
Layer

▪ Unique point of query
▪ “Need data? LDW has the answer!”
▪ For reporting, analytics, APIs, …
▪ Unique point of truth
▪ Business logic repository
▪ Lineage available
▪ Unique point of access control
▪ Unified access to the data
▪ Unique point of auditing
KPI DB Source DBs New DWH Old DWH Markets DB
WebI
Lumira
Live Office
General Reporting
KPI SAS EG SAS VA
VA Server
Risk Reporting
Board
Other DBs
Data
Sources
Logical Data Warehouse w/ Denodo
Business rules
Crystal
reports

▪ Addition of data consumers
▪ Tableau
▪ REST / Restful APIs
▪ Addition of more data sources
▪ Where ETL is not required
▪ When history is provided in source
▪ Logical data pipelines
▪ Reduces the number of ETL jobs
▪ EDW gets data from LDW
WebI Tableau
Denodo to
Excel
General Reporting
KPI SAS EG SAS VA
VA Server
Risk Reporting
Board
Data
Sources
Logical Data Warehouse w/ Denodo
KPI DB
Source
DBs
New
DWH
Old
DWH
Markets
DB
Other
DBs
Flat files
Excel
SaaS
REST
SOAP
WWW
Customers
Domains
Operational
systems
Business rules
Customer
statements

▪ A simplified process
1. Domains provided with a development space
2. LDW developers combine views
3. Domains publish data
4. Operational systems access the data
▪ Top-down modelling
▪ Using interface views (data contracts)
Source
system
Base
Data Mesh
Domain A
developer
Business
systems
LDW
developer
LDW
Requests
(interface contracts)
Shares Combines
LDW
Source
DBs
Operational
systems
Domains
Domain B
developer
Requests
(interface contracts)
Publication
Data Mesh
Publishes
LDW

▪ Delegate the ownership of data to the domains
▪ Data is in the hands of its creator
▪ Give better overview of the pipeline
▪ Views lifecycle managed by the source developer
▪ Reduce data pipelines
▪ Fewer ETL jobs when available
LDW
Source
DBs
Operational
systems
Domains
Savings
domain
Loans
domain
Cards
domain
Claims
domain
EDW
domain
LDW
developer
CRM Loan Online bank

“Denodo is giving us a lot of
advantages over an ETL process,
especially when people need
real-time data made available to
them very quickly. Time-wise
and project-wise, Denodo has
helped speed up many, many
projects and enabled my team
to do more.”
-IT Manager, Manufacturing
Customer Experience
SUMMARY
Denodo helps reduce the time it
takes for data workers to fulfill
dataset deliveries over legacy ETL
processes. Denodo automatically
integrates disparate data sources,
optimizes query requests, and
builds in a centralized governance
architecture so that organizations
can access the data they need
faster.
KEY VALUE CAPTURE
65% improvement in
delivery times over ETL
with Denodo
THREE-YEAR
FINANCIAL IMPACT
$1.7M

“With Denodo, our data
scientists no longer spend
30% of their time on data
wrangling and data curation.
They can now spend that
time on modeling since we
can logically model our data
within Denodo.”
-VP of Data & Analytics, Real Estate
Customer Experience
SUMMARY
THREE-YEAR
FINANCIAL IMPACT
$698K
Denodo automatically integrates
data across disparate sources,
allowing data scientists to quickly
and intuitively conduct the queries
they need to perform modeling.
Denodo’s software also ensures
that the data being analyzed for
modeling is consistent, quality, and
secure through its governance and
security features.
KEY VALUE CAPTURE
67% reduction in
data preparation
effort

17
Benefit: Reduced Legacy Integration Costs
“With Denodo, we’re probably
saving $400,000 a year. The flip
side of that is Denodo has
allowed us to do a lot more so
now, we’ve grown that footprint
and we’ve replaced that
$400,000 with a whole bunch of
new things we can never do in
the old world.”
-VP of Data & Analytics, Real Estate
Customer Experience
SUMMARY
THREE-YEAR
FINANCIAL IMPACT
$1.5M
KEY VALUE CAPTURE
$1.2M in reduced ETL costs
within three years of
deployment
+$300K per year in
reduced legacy data
integration tools
Denodo can gradually replace
existing ETL processes with a
faster, more reliable data
virtualization layer, without forcing
an organization to retire these
processes all at once or affecting
end-user experience. Denodo’s
features can also allow
organizations to retire now-
redundant legacy software
systems, saving on licensing fees
and support costs.

18
Next Steps
Access Denodo Platform in the Cloud.
Start your Free Trial today!
G E T S T A R T E D T O D A Y
www.denodo.com/free-trials
TDWI Checklist Report:
Six Popular Use Cases Enabled by a Logical Data Fabric
DOWNLOAD CHECKLIST REPORT

Logical Data Fabric and Data Mesh – Driving Business Outcomes

Logical Data Fabric and Data Mesh – Driving Business Outcomes

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