Building a Data Platform Strata SF 2019

CopyrightThird Nature,Inc.
Architecting a Data
Platform For
Enterprise Use
March 2019
Mark Madsen
Todd Walter

Why are we talking about architecture?

4 © 2017 Gartner, Inc.and/or its affiliates. All rights reserved.
Gartner statement in 2018:
only 15% are reported
to be successful
only
17%of Hadoop deployments
are in production in 2017
Survey Analysis: BI and Analytics
Spending Intentions, 2017
A McKinsey survey this
year asked executives if
their company had
achieved a positive ROI
with their big data projects:
7% answered “yes”
Gartner Finding:
in 2017

DW: Centralize, that solves all problems!
Creates bottlenecks
Causes scale problems
Availability?

The data lake solution: no central authority
wtf, it was fully
operational!

The data lake solution?
There’s a problem: as
the lake is envisioned,
it is still a centralized
data architecture, but
this time there is no
single global model.
Instead it’s files and
not modeled. It can be
operational while
under construction.
It’s still a death star.

Eventually we run into the same problems
Seriously, wtf?
It was agile
and operational
You’re building a centralized model without realizing it.
Maybe we can avoid recreating the same problems.

Copyright Third Nature, Inc.
The solution to our problems isn’t
technology, it’s architecture.

Architecture?
What does the architect do?
What is the architect’s
responsibility and role?
What is the core problem of
data architecture?

Use

Use
Over time

Use
Over time
By people

Bricks are not buildings
We don’t think this is equivalent to this
Architecture is not technology. It’s not a product you can buy.

Blueprints are not architectures

Technical wiring diagrams
Product lists
Pretty pictures
Hand-waving abstract statements and rules
A thing you can purchase
…so what is it?
Architecture is not…

What is this?

Architecture is an abstraction – a pattern that supports a purpose
You need purpose, therefore focus business goals, outcomes, use cases

19
No architecture = accidental architecture
The complexity of most organizations exceeds the capability of one system
Just like the DW, keep piling things on…
Accidental architecture at enterprise scale:
Kowloon full scope (above), and the detail of one
building section(left) – this is a true monolith.
Functional, served a purpose, impossible to add
services to, support, maintain.
Result: avoid, or bulldoze.

20
This focus on monolith is why the
data warehouse is in legacy mode
Everything is tightly
interconnected, such that
nobody wants to change
anything because they might
break something else.
It’s easier to start over
somewhere else.
Then they blame the data
warehouse, or the database,
or the data management
team for what is essentially
an architectural failure.

“The problem is the tools. Standardize on one tech!”
It’s called stack think. Pick your vendor. Cede all architecture.

The problem is methods and process: agile your way
into the Analytics Shantytown

“Start with the platform. The rest will follow.”

CopyrightThird Nature,Inc.CopyrightThird Nature,Inc.
Big data reality: What users seeBig data promise: What you see

HISTORY: HOW DID WE GET HERE?

CopyrightThird Nature,Inc. 4/23/2018
The market shifted from not enough data
in the 90’s to too much data: the problem
has become managing not just size, but
scope and variety

More complex needs drive more complex technology

Market hype and IT workforce skill gaps lead to FOMO
The pressure on IT to “just
buy something” is high. The
usual IT response is based on
procurement, not innovation
or integration. The data
infrastructure solution tends
to be: buy tools, collect data.

CopyrightThird Nature,Inc. 4/23/2018
Developmentaccumulates in a disorderly fashion

The end result in the IT landscape is complexity

31
Sales
SQL
BI: dashboards,
reports, queries
Customers
Inventory
Products
tables
Where we started: the data warehouse and BI
The data warehouse solved a key problem:
access to data from multiple OLTP systems
to provide a unified view of key
information.
It is built on some assumptions:you must
model the data before use, the data must
be cleaned first,the data must be in tables.
The DW is built for repeatable datause, not
for one-time uses of informationor
unknown-value datasets.
© 2018 Teradata

32
Sales
SQL
BIDiscovery
Customers
Inventory
Products
Financials
tabular
?
Analysts Anyone
tables
After a while, user self-service was required
© 2018 Teradata
Eventually we reached maturitywith
the DW, driving an increasingrate of
new data requests by departments
and individuals.
A backlog of smaller data requests
built up around .it We got self-service
tools that actually worked.

33
Sales
SQL
BIDiscovery
Customers
Inventory
Products
Financials
tabular
?
Analysts Anyone
tables
Why Isn’t the Data in the DW? Rush Jobs and Unknown Data
© 2018 Teradata
The real problem is time: business
analyses are often needed in a week
or less. If the data is not in the DW
then the analyst has to wait – the
industry average for making data
available in a DW is 10 weeks.
They need quick access to new data
rather than reusable, cleaned,
common data.This means they need
another way – and self-service tools
offer an answer other than “wait.”

34
Sales
SQL
BIDiscovery
Customers
Inventory
Products
Financials
tabular
?
Analysts Anyone
tables
But data is rarely used in isolation, DW data is often needed
© 2018 Teradata
New data usually needs to be linked
to existing data.
Users don’t justneed accessto data,
they need a place to work with and
store datatoo.
Ignore this requirement and you have
runaway copies, extracts, andfiles
tied to specific tools,with have no
visibility into what is happening.

35
Sales
SQL
BIDiscovery
Customers
Inventory
Products
Financials
? R
Analysts Anyone
tables Array /
matrix
Warehouse
events
There are limits to what you can do with queries
© 2018 Teradata
tabular
Some questionsare not answerable
with queries. Deeper analysis is
required to answer the question.
The truth is, and always has been,
that tables and a database are not the
only technology in the analytic
ecosystem.

36
Sales
SQL
BIDiscovery Data science
Customers
Inventory
Products
Financials clicks
? R Python
Analysts
Spark
Anyone Data scientists
tables Array /
matrix
Time
series
Warehouse
events
Data Science accelerates, more data, more engines
© 2018 Teradata
tabular
As BI matured,informationneeds
grew more complex. New analytics,
new data,higher volumes drove
creationof new techniquesand new
processingengines.
New techniques,new engines, means
new structuringand positioning of
data is required.

37
Sales
SQL
Customers
Inventory
Products
Financials clicks
? R Python Tensor
Flow
Analysts
Spark
tables Array /
matrix
Time
series
Graph
Warehouse
events
More unique data, more approaches, technologies arrive monthly
© 2018 Teradata
tabular
?
e.g. Emails, images,
more events

38
Sales
SQL
Customers
Inventory
Products
Financials clicks
? R Python Tensor
Flow
Analysts
Spark
tables Array /
matrix
Time
series
Warehouse
events
The end result of years of addition is accidental architecture
© 2018 Teradata
tabular Graph
?
more events

39
Sales
SQL
Customers
Inventory
Products
Financials clicks
? R Python Tensor
Flow
Analysts
Spark
tables Array /
matrix
Time
series
Warehouse
events
Today’s environment has (and still needs) different engines
Engines
© 2018 Teradata
tabular Graph
?
more events

40
Sales
SQL
Customers
Inventory
Products
Financials clicks
? R Python Tensor
Flow
Analysts
Spark
tables Array /
matrix
Time
series
Warehouse
events
Some engines require specific structures and positioning of data
Data storedto
engine needs
Engines
© 2018 Teradata
tabular Graph
?
more events

41
Sales
SQL
Customers
Inventory
Products
Financials clicks
? R Python Tensor
Flow
Analysts
Spark
S3HDFSRDBMSRDBMS
Raw data
tables Array /
matrix
Time
series
Warehouse
events
Distributed data is the norm, stored in multiple types of repositories
© 2018 Teradata
tabular Graph
?
more events

42
Sales
SQL
Customers
Inventory
Products
Financials clicks
? R Python Tensor
Flow
Analysts
Spark
tables Array /
matrix
Time
series
Warehouse
events
How much of the data science problem is tools vs engines?
Data storedto
engine needs
Engines
© 2018 Teradata
tabular Graph
?
more events
The dirty secret of datascience: more
than 80% of the enterprise market
does it on laptops.Mostdoes not
need specialized infrastructure.

43
Sales
SQL
?
Customers
Inventory
Products
Financials clicks e.g. Emails, images,
more events
? R Python Tensor
Flow
Analysts
Spark
tables Array /
matrix
Time
series
Warehouse
events
Managing this complexity is a growing challenge
© 2018 Teradata
tabular Graph
The challenge with use of data
science shifts to operations:how to
deploy and manage a production
environment with so many
technologies and copies of data?

44
Sales
SQL
Customers
Inventory
Products
Financials clicks
? R Python Tensor
Flow
Analysts
Spark
tables Array /
matrix
Time
series
Warehouse
events
Accidental architecture does not lead to agility
© 2018 Teradata
tabular Graph
?
more events
Accidentalarchitectureis
unsustainablein the long run.

45
What’s missing:
loosely integrated
data stored for
provisioning.
Sales
SQL
Customers
Inventory
Products
Financials clicks
? R Python Tensor
Flow
Analysts
Spark
tables Array /
matrix
Time
series
Warehouse
events
© 2018 Teradata
tabular Graph
?
more events
The arrows in architecturediagrams
hide the hard work and make things
seemsimplerthan they are.
The arrows are wherethe integration
costs and labor are buried.

46
Sales
SQL
Customers
Inventory
Products
Financials clicks
? R Python Tensor
Flow
Analysts
Spark
Anyone
Curated data stored
for integration and
provisioning
tables Array /
matrix
Time
series
Warehouse
events
Data architecture is needed to fill the gap© 2018 Teradata
You need a layerto separatethe
messof raw data below from the
distributed,context-specificusesof
data above.
This minimizesthe cost of change,
providesuser control of data via
separationof data managementfrom
data use.
tabular Graph
?
more events
Data scientists

47
Sales
Renovating the accidental architecture requires a transition
path to separate data infrastructure from the uses of data
© 2018 Teradata
SQL
Customers
Inventory
Products
Financials clicks
? R Python Tensor
Flow
Analysts
Spark
Anyone
Data storedto
engine needs
Curateddata stored
for integrationand
provisioning
S3HDFSRDBMSRDBMS
Raw data
tables Array /
matrix
Time
series
Warehouse
events
Engines
tabular Graph
?
more events
Data scientists

We continue to act as if there is one single system
“So much complexity in software
comes from trying to make one
thing do two things.”
— Ryan Singer

Break down the monolithic architecture – not one
building, multiple buildings in a block

Buildingsabove: flexibility,repurposing,
quicker change above, funded separately
Applications
Utilitiesbelow:stability, reuse, slow
predictable change below, funded centrally
Infrastructure
We built things as a single entity that
combined the building and the
infrastructure in one complex monolith
Complexity requires a shift: separate the
application from the infrastructure – we
are focused on the block, no the buildings

© Third Nature Inc.
Speed and agility
You want speed. Speed comes
from burying work in the
infrastructure, which trades
flexibility for repeatability.
Therefore you must be careful
when you draw the line
between what is above ground
and below, the uses of data
and the infrastructure.

"Always design a thing by consideringit in its next larger
context - a chair in a room, a room in a house,a house in an
environment,an environmentin a city plan." – Eliel Saarinen

Think of IT as a city
Where does a building fit?

Streaming analytics
Data science
Data collection
Self-service
/ Discovery
BI (QRD)
Self-service Data

56
The bigger picture of what we create
The ecosystem is like the city and all of
its extended neighborhoods.
The different applications or types of
projects are like the neighborhoods
with specific types of buildings.
Each individual building (application)
has its own unique blueprint.
Underlying all the applications is
shared data infrastructure, like city
services. The shared infrastructure is
the foundation of the analytics
architecture for a company.
City
Neighborhoods
Buildings and
shared infra

“Begin with the end in mind”
The starting point can’t be with
technology. That’s like starting
with bricks when designing a
house. You may get lucky but…
The goals and specific uses are
the place to start
▪ Use dictates need
▪ Need dictates capabilities
▪ Capabilities are solved with
technology
This is how you avoid spending $2M on a Hadoop andspark cluster in order to serve
data to analysts whose primary requirements aremet with laptops.

Persisting data
is not the end
of the line.
If you stop
here you win
the battle and
lose the war

We don’t have a data science problem, just like we
didn’t have a BI problem
The origin of analytics as “business
intelligence” was stated well in 1958:
…the ability to apprehend the
interrelationships of presented facts in
such a way as to guide action towards a
desired goal. ~ H. P. Luhn
“A Business Intelligence System”, http://altaplana.com/ibmrd0204H.pdf
”
“
Our goal is analytics as a capability, not a technology

The old problem was access, the new problem is analysis

Three constituencies
Stakeholder Analyst Builder
aka the recipient aka the data scientist aka the engineer

Starting points for analytics strategy
Many organizations choose to start with
the analysts. Create a data science team.
Turn them loose to find a problem.
Many more start with builders: technology
solutions looking for problems, e.g. 65% of
the IT driven Hadoop and Spark projects
over the last five years.
The right place to start? Stakeholders.The
goal to achieve,the problem to solve.

WHAT IS THE CONTEXT OF USE?

There is an extensive list of requirements to support
Primary requirementsneededby constituents S D E
Data catalogand ability to search it for datasets X X
Self-service accessto curateddata X
Self-service accessto uncurated(unknown, new) data X X
Temporary storagefor working with data X
Data integration,cleaning,transformation, preparation toolsandenvironment X X
Persistentstoragefor source dataused by productionmodels X X
Persistentstoragefor training,testing,production data used by models X X
Storageand management of models X X
Deployment, monitoring, decommissioning models X
Lineage, traceabilityof changes made for data used by models X X
Lineage, traceabilityfor model changes X X X
Managingbaseline data / metrics for comparing model performance X X X
Managingongoing data / metrics for trackingongoing model performance X X X
S = stakeholder, user, D = data scientist, analyst, E = engineer, developer

How did we get to this state
with BI & analytics?
There’s a difference
between having no past
and actively rejecting it.

67
100
1,000
10,000
1 2 3 4 5 6 7 8 9 10
Customer Data Plateaus
Add users,
accumulate
history, add
attributes, add
dimensions
Entirely new data
set enabled by
new technology at
new price point –
value has to
exceed cost (ROI).
Earliest adopters
have vision ahead
of ROI

68
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
1 2 3 4 5 6 7 8 9 10 11 12 13
User Adoption of New Data
Customer Data
Users - Data Set 1
Users - Data Set 2
Users of Integrated Data
User adoption of new data sets
starts over. Very small number of
experts growing to wider
audience, sophisticated users
moving to business analysts, then
business users, B2B customers and
even to consumers
Greater value is derived when
data sets are linked – see bigger
picture (eg who buys what,
when). Comes after initial
extractionof easy value from
standalonedata

69
• <Store, Item, Week>
• <Store, Item, Day>
– Simple aggregations
• Market Basket
– Affinity
– Link to person, demographics, HR
• Inventoryby SKU by store
– Temporal, time series, forecasting
– Link to product, marketing, market basket
Retail Plateaus
2B records
total for 9
quarters
2B records
per day,
keep 9
quarters

70
• Web Logs and traffic
– Behavioral patterns– eg path linked to person,offers,other channels
– Operationsof the web site
• Supply chain sensors – sampled at major event
– Activity Based Costing
– Link to customer, product,HR, planning
• Social Media
– Text analysis, Filtering, languages
– Link to customer, sales,other channelinteractions
• Supply chain sensors – sampled at minutes or seconds
– Telematics
– Real time, Eventdetection,trending,static and dynamicrules
– Link to HR, thresholds, forecasts,routing,planning
Retail Plateaus
30B records per day

71
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
1,000,000,000
10,000,000,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Data Size Plateaus over Time
Lather, Rinse,
Repeat. Trend
line is roughly
Moore’s Law.
Delay or skip a
generation if new
data set is two
orders of
magnitude
instead of one.

72
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
1,000,000,000
10,000,000,000
100,000,000,000
1,000,000,000,000
10,000,000,000,000
100,000,000,000,000
1,000,000,000,000,000
10,000,000,000,000,000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
Customer Data Size vs. 2x per 12mo
Customer Data
Moore's Law - 2x/18mo
2x/12 mo
But all studies show
demand/creation of
data increasing
significantly faster
than Moore’s law
If we started only 1
OOM behind, we are
now 5 OOM away from
user demand for data
Data
Exhaust

73
FINANCE
Revenue
Expenses
Customers
CUSTOMER
CARE
Customer
Products
Orders
Case History
SALES
Orders
Customers
Products
MARKETING
Customers
Orders
Campaign
History
OPERATIONS
Inventory
Returns
Manufacturing
Supply Chain
How many
batteries
are in
inventory
by plant?
What is the
trend of
warranty
costs?
How many
people
made a
warranty
claim last
week?
How many
sales have
been
made
quarter to
date?
Which
customers
should get a
communica
tion on
extended
warranties?
54 32 29 49 66

75
Given the rise in warranty costs, isolate the problem to be a
plant, then to a battery lot.
Communicate with affected customers, who have not made
a warranty claim on batteries, through Marketing and
Customer Service channels to recall cars with affected
batteries.
2855
Inventory
Returns
Manufacturing
Supply Chain
Customer Service
Orders
Revenue
Expenses
Case History
Customers
Products
Pipeline
Customers
Campaign
History
FINANCE
SALESMARKETING
OPERATIONS
CUSTOMER
EXPERIENCE
2855

76
Manufacturing: Data Overlap Analysis
New Business Improvement Opportunities through Data Leverage
Sales Force Profitability Analysis 100% 80% 66% 24% 41% 66% 0% 24% 0% 24%
Transportation Planning 11% 100% 87% 28% 64% 56% 22% 34% 13% 45%
Production Planning 6% 57% 100% 19% 83% 35% 17% 28% 9% 40%
Vendor Managed Inventory 12% 100% 100% 100% 78% 100% 61% 100% 39% 100%
Global Pricing Rationalization 4% 50% 100% 17% 100% 27% 15% 29% 11% 41%
Fulfillment (Perfect Order) 16% 94% 90% 48% 58% 100% 35% 53% 19% 56%
Manufacturing Quality
Optimization
0% 76% 88% 60% 66% 71% 100% 79% 43% 73%
Preventative Maintenance Analysis 7% 75% 93% 61% 80% 68% 50% 100% 27% 82%
Warranty Claims Analysis 0% 94% 100% 83% 100% 83% 94% 93% 100% 98%
Quality Life Cycle Improvement 4% 57% 75% 35% 64% 41% 26% 47% 16% 100%
SalesForce
ProfitabilityAnalysis
ProductionPlanning
VendorManaged
Inventory
GlobalPricing
Rationalization
TransportationPlanning
Fulfillment
(PerfectOrder)
ManufacturingQuality
Optimization
Preventative
Maintenance
Analysis
WarrantyClaims
Analysis
QualityLifeCycle
Improvement
If
Then

78
PRODUCT
SENSOR
SOCIAL
MEDIA
CUSTOMER
CARE AUDIO
RECORDINGS
DIGITAL
ADVERTISING
CLICKSTREAM
65 41 32 19 28
How many
visitors did
we have to
our hybrid
cars
microsite
yesterday?
What are
the
temperature
readings for
batteries by
Manufactur
er?
What is the
sentiment
towards line
of hybrid
vehicles?
Which
customers
likely
expressed
anger with
customer
care?
Which ad
creative
generated
the most
clicks?

79
2855
SENSOR
DIGITAL
ADVERTISING
CLICKSTREAM
INTERACTIONS
RATINGS &
REVIEWS
CUSTOMER PORTAL
INTERACTIONS
EXTERNAL
INTERACTIONS
SOCIAL MEDIA
IVR Routing
RFID
ELECTRONIC
COMMERCE
FINANCE
SALESMARKETING
Inventory
Returns
Manufacturing
Supply Chain
Customer
Service
Orders
Revenue
Expenses
Case History
Customers
Products
PipelineCustomers
Campaign History
OPERATIONS
CUSTOMER
CARE – AUDIO
RECORDINGS
Maps
Telemetry
SERVER LOGS
CUSTOMER
EXPERIENCE
Enterprise Data

80
2855
Schema on
Write
Evolving
Schema
Schema on
Read
Enterprise
Data
Evolving
Data
New Data
Sources
Schema?

81
2855
High, Well
Known Quality
Directional
Quality
Unknown/Low
quality
Well Defined
Data Model
Curated JSON,
XML, DB
Extracted
Attributes
Curation Required

82
2855
Out of Deviation
Sensor Readings
Fraud
Events
CUSTOMER EXPERIENCE
SALES
MARKETING
OPERATIONS
Abandoned
Carts
Online Price
Quotes
Social Media
Influencers
FINANCE
11234
Minimum Viable
Curation
Minimum Viable
Data Quality

83
2855
Out of Deviation
Sensor Readings
Fraud
Events
CUSTOMER
EXPERIENCE
SALES
MARKETING
OPERATIONS
Abandoned
Carts
Online Price
Quotes
SocialMedia
Influencers
FINANCE
11234
Sensor data
formatting
Unit
Normalization
Vehicle/version
normalization
Make/model/year
selection
Data Comprehension,
Pipelines

84
2855
Enterprise
Wide Use
10s to 100s
of Users
10s Users
Analysts /
engineers
Business
Analysts
Action list, Report,
Dashboard Users
Share across
Enterprise
Share in
department
Share over
cube wall
User Base and Sharing

85
Evolving
Consumption
2855
Enterprise
production
analytics
Departmental
analytics
Exploratory
analytics
Repeatable,
auditable
results
Ad-Hoc
Query, Self
Service
Data Labs

86
Evolving Consumption
Requirements
2855
Production tools,
curated data,
integrated across
business areas
Wide variety
of tools and
data forms
Targeted data access,
many applications,
response time SLAs
High CPU,
moderate to
low IO
Bulk scans, large
computation,
transformation, data access,
specific integration
Moderate CPU,
High IO, Resource
management

87
MARKETING
FINANCE
SALES
OPERATIONS
CUSTOMER
EXPERIENCE
Given the rise in warranty
costs, isolate the
problem to be a plant
and the specific lot.
Exclude 2/3rd of the
batteries fromthe lot
that are fine.
Communicate with
affected customers, who
havenot made a
warranty claim, through
Marketing and Customer
Service channels to
recall cars with affected
batteries.
2855
MANUFACTURING
CAMPAIGN
HISTORY
COSTS
PRODUCTS
CUSTOMERS
CASE HISTORY
SENSOR
Access Wide Variety of Data
to Answer a Question

DATA ARCHITECTURE
We’re so focused on the light switch that we’re not
talking about the light

History: This is how BI was done through the 80s
First there were files and reporting programs.
Application files feed through a data processing pipeline to
generate an output file. The file is used by a report formatter
for print/screen. Files are largely single-purpose use.
Every report is a program written by a developer.
Data pipeline
code

History: This is how BI ended the 80s
The inevitable situation was...
Data pipeline
code

History: This is how we started the 90s
Collect data in a database. Queries replaced a LOT of
application code because much was just joins. We
learned about “dead code”
SQL
SQL
SQL
SQL
SQL

Pragmatism and Data
Lessons learned during
the ad-hoc SQL era of the
DW market:
When the technology is
awkward for the users, the
users will stop trying to use
it.
Even “simple” schemas
weren’t enough for anyone
other than analysts and
their Brio…
Led to the evolution of
metadata-driven SQL-
generating BI tools, ETL
tools.

BI evolved to hiding query generation for end users
With more regular schema models, in particular
dimensional models that didn’t contain cyclic join paths, it
was possible to automate SQL generation via semantic
mapping layers.
We developed data pipeline building tools (ETL).
Query via business terms made BI usable by non-technical
people.
ETL
SQL
Life got much easier…for a while

Today’s model: Lake + data engineers, looks familiar…
The Lake with data pipelines to files or Hive tables
is exactly the same pattern as the COBOL batch..
Dataflow
code
We already know that people don’t scale…

The architecture from 1988 we SHOULD HAVE BEEN USING
The general concept of a
separate architecture for BI
has been around longer, but
this paper by Devlin and
Murphy is the first formal
data warehouse architecture
and definition published.
95
“An architecture for a business and
information system”, B. A. Devlin,
P. T. Murphy, IBM Systems Journal,
Vol.27, No. 1, (1988)
Slide 95Copyright Third Nature, Inc.

But 30 years ago we did not expect so many different
models of deployment,execution and use. Needs change
DeployETL
Data Data Storage
Alerts / Reports/
Decisioning
Deploy
f
Data Streams Intelligent Filter
/ Transform Model Execution
Analytics for eyeballs and analytics for machines are different

Decouple the Architecture
The core of a data warehouse isn’t the database, it’s the
data architecture that the database and tools implement.
We need a new data architecture that is not limiting:
▪ Deals with change more easily and at scale
▪ Does not enforce requirements and models up front
▪ Does not limit the format or structure of data
▪ Assumes the range of data latencies in and out, from streaming
to one-time bulk
▪ Allows both reading and writing of data
▪ Makes data linkable, and provide governance where required
▪ Does not give up the gains of the last 25 years

The goal is to decouple: solve the application and
infrastructure problems separately, independently
Data Acquisition
Collect & Store
Incremental
Batch
One-time copy
Real time
Platform Services
Data storage
Data arrives in many latencies, from real-time to one-time. Acquisition
can’t be limited by the management or consumption layers.
Data acquisition should not be
directly tied to the needs of
consumption. It must operate
independently of data use.

Data hoarding is not a data management strategy

Platform Services
Data Management
Process & Integrate
Data storage
Data management should not be subject to the constraints of a single use
Data management
has historically been
blended with both
data acquisition and
structuring data for
client tools. It should
be an independent
function.

Platform Services
Data Access
Deliver & Use
Data storage
This separates uses of data from each other, allowingeach type of
use to structure the data specific to its own requirements.
Data access is already
somewhat separate
today. Make the
separation of different
access methods a formal
part of the architecture.
Don’t force one model.

The full analytic environment subsumes all the functions
of a data lake and a data warehouse, and extends them
Data Acquisition
Collect & Store
Incremental
Batch
One-time copy
Real time
Platform Services
Data Management
Process & Integrate
Data Access
Deliver & Use
Data storage
The platform has to do more than serve queries; it has to be read-write.

The data architecture must align with system components
because each of them addresses different data needs
Incremental
Collect
Batch
One-time copy
Real time
Manage & Integrate
Data Acquisition
Collect & Store
Data Management
Process & Integrate
Data Access
Deliver & Use
Separating concerns is part of the mechanism for change isolation

Divide the data architecture to address three different goals
Collection
Creation, collection,
storage of new data
Distribution
Organization and
provisioning of data
to multiple points
of use
Consumption
Direct support of
data use
Separation of concerns, coordination of process
Collect Curate Consume

The design focus is different in each area
Ingredients
Goal: available
User needs a recipe
in order to make
use of the data.
Pre-mixed
Goal: discoverable
and integrateable
User needs a menu
to choose from the
data available
Meals
Goal: usable
User needs utensils
but is given a
finished meal

Food supply chain: an analogy for analytic data
Multiple contexts of use, differing quality levels
You need to keep the original because just like baking,
you can’t unmake dough once it’s mixed.

Data has to be moved, standardized, tracked
There is a lot of data policy and governance to think about
Collect Manage & Integrate
Data Acquisition
Collect & Store
Data Management
Process & Integrate
Data Access
Deliver & Use
CRM
User reg
Orders
Leads
Cust
Cust
Cust
Cust
Master DW
Mktg
Campaign
analysis
Rec
engine

Data curation
The problem with so many
sources, types, formats and
latencies of data is that it is
now impossible to create
one model for all of it in
advance.
Data modeling is about the
inside of a dataset. Curation
is about the set.
Data curation, rather than
data modeling, is becoming
the more important data
management practice.

The missing ingredient from most data projects
Specifically,
metadata kept
separate from
the data.

The data is in zones of management, not isolating layers
Raw data in an immutable
storage area
Standardized or
enhanced data
Common or
usage-
specific
data
Transient data
Relax control to enable self-service while avoiding a mess.
Do not constrain access to one zone or to a single tool.
Focus on visibility of data use, not control of data.

This data architecture resolves rate of change problems
Raw data in an immutable
storage area
Standardized or
enhanced data
Common or
usage-
specific data
Transient data
New data of
unknown value,
simple requests for
new data can land
here first, with little
work by IT.
More effort applied to
management, slower.
Optimized for
specific uses /
workloads. Generally
the slowest change.
Not fast vs slow:
fast vs right
Not flexibility vs
control: flexibility
vs repeatability
Agile for
structure change
vs agile for
questions / use

The concept of a zone is not a physical system. It’s data architecture
Apps
DW
The biggest decision is to separate all data collection from
the data integration from consumption.
Physical system/technology overlays are separate, depend
on the specific use cases and needs of the organization.
Dist FS
RDBMSs
Decompress &
process device logs
RDBMS
RDBMS
multiple
Discovery
platform

Data has to be moved, standardized, tracked
There is a lot of data policy and governance to think about
Collect Manage & Integrate
Data Acquisition
Collect & Store
Data Management
Process & Integrate
Data Access
Deliver & Use
CRM
User reg
Orders
Leads
Cust
Cust
Cust
Cust
Master DW
Mktg
Campaign
analysis
Rec
engine

HOW DO YOU EVOLVE
WHAT YOU HAVE
NOW?

Agile methods without agile architectures fail

119
Process and governance are part of architecture
Data policy and organizational model go together. Policy
choices are like land use policies. Want free access to
water? Ribbon farms in a capitalist world, domains in a
feudal manor.

Reinforcing relationships resist change, despite
radical technology and practice shifts
Note how only one third is tech
Architectural
Regime
MethodologyTechnology
Organization
Organization
defines wherethe
work is done and
the roles.
Technology
defines what
work can be done
in a given area. Methodology
defines how
work is done
and what that
work is.
Slide 120

What about the technology? Do I need an <X>?

122
“Technology first” thinking creates implementation problems
If you start with technology,it will constrain the problems you can solve

123
Blended Architectures Are a Requirement,
Not an Option
Data Warehouse + Data Lake
On Premise + Cloud
RDBMS + S3 + HDFS
Commercial + Open Source
You can’t just buy one thing platform from one vendor. We aren’t building a
death star.
Each of the zones is likely to have products specific to that zone’s usage. The
uses differ, the people using them differ, shouldn’t the tools should differ too?

In other words…
Software is like puppies.
Getting a puppy is easy, raising one is hard.
“The short term benefits of using a new [type of]
database exceed the long term cost of operating it.”
Dan Mckinley

Choose Boring Technology
You only get so many chances
to make big changes. Don’t
waste them.
You can spend time focusing
on the goal and worry less
about the known tech, or you
can spend time learning the
new tech but less time
focusing on the goal.
The important thing is not the
choice of tech, it’s knowing
when the time is right to make
a new tech choice.

TANSTAAFL
When replacing the old
with the new (or ignoring
the new over the old) you
always make tradeoffs,
and usually you won’t see
them for a long time.
Technologiesare not
perfect replacementsfor
one another. Often not
better, only different.

Manage your data
(or it will manage you)
Data management is where
developers are weakest.
Modern engineering
practices are where data
management is weakest.
You need to bridge these
groups and practices in the
organization if you want to
do meaningful work with
data. Remember Conway’s
Law when you build.

“Begin with the end in mind”
The starting point can’t be with
technology. That’s like starting
with bricks when designing a
house. You may get lucky but…
The goals and specific uses are
the place to start
▪ Use dictates need
▪ Need dictates capabilities
▪ Capabilities are solved with
technology
This is how you avoid spending $20M on a new cluster in order to serve data to
analysts whose primary requirements aremet with laptops.

A good design is not the one that
correctly predicts the future, it’s
one that makes adapting to the
future affordable.
— Venkat Subramaniam

Mark Madsen is the global head of
architectureat Think Big Analytics,
Prior to that he was president of Third
Nature, a research and consulting firm
focused on analytics, data integration
and data management.Mark is an
award-winning author, architectand
CTO whose work has been featured in
numerous industry publications.Over
the past ten years Mark received
awards for his work from the American
Productivity& Quality Center, TDWI,
and the Smithsonian Institute.He is an
international speaker, chairs several
conferences, and is on the O’Reilly
Strataprogram committee. For more
information or to contactMark, follow
@markmadsen on Twitter.
About the Presenter

Todd Walter
Chief Technologist - Teradata
• Chief Technologist for Teradata
• A pragmatic visionary, Walter helps business leaders,
analysts and technologists better understand all of the
astonishing possibilities of big data and analytics
• Works with organizations of all sizes and levels of
experience at the leading edge of adopting big data, data
warehouse and analytics technologies
• With Teradata for more than 30 years and served for more
than 10 years as CTO of Teradata Labs, contributing
significantly to Teradata’s unique design features and
functionality
• Holds more than a dozen Teradata patents and is a
Teradata Fellow in recognition of his long record of
technical innovation and contribution to the company

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