Di Massimo Brignoli.

1. Massimo Brignoli
Principal Solution Architect
massimo@mongodb.com
@massimobrignoli
L’architettura di Classe Enterprise di Nuova
Generazione

2. Agenda
• Nascita dei Data Lake
• Overview di MongoDB
• Proposta di un’architettura
EDM
• Case Study & Scenarios
• Data Lake Lessons Learned

3. Quanti dati?
• Una cosa non manca alla aziende: dati
– Flussi dei sensori
– Sentiment sui social
– Log dei server
– App mobile
• Analisti stimano una crescita del volume di dati del 40%
annuo, 90% dei quali non strutturati.
• Le tecnologie tradizionali (alcune disegnate 40 anni fa) non
sono sufficienti

4. La Promessa del “Big Data”
• Scoprire informazioni collezionando ed analizzando i dati
porta la promessa di
– Un vantaggio competitivo
– Risparmio economico
• Un esempio diffuso dell’utilizzo della tecnologia Big Data è la
“Single View”: aggregare tutto quello che si conosce di un
cliente per migliorarne l’ingaggio e i ricavi
• Il tradizionale EDW scricchiola sotto il carico, sopraffatto dal
volume e varietà dei dati (e dall’alto costo).

5. La Nascita dei Data Lake
• Molte aziende hanno iniziato a guardare verso un’architettura
detta Data Lake:
– Piattaforma per gestire i dati in modo flessibile
– Per aggregare I dati cross-silo in un unico posto
– Permette l’esplorazione di tutti i dati
• La piattaforma più in voga in questo momento è Hadoop:
– Permette la scalabilità orizzontale su hardware commodity
– Permette una schema di dati variegati ottimizzato in lettura
– Include strati di lavorazione dei dati in SQL e linguaggi comuni
– Grandi referenze (Yahoo e Google in primis)

6. Perché Hadoop?
• Hadoop Distributed FileSystem è disegnato per scalare su
grandi operazioni batch
• Fornisce un modello write-one read-many append-only
• Ottimizzato per lunghe scansione di TB o PB di dati
• Questa capacità di gestire dati multi-strutturati è usata:
– Segmentazione dei clienti per campagne di marketing e
recommendation
– Analisi predittiva
– Modelli di Rischio

7. Ma va bene per tutto?
• I Data Lake sono disegnati per fornire l’output di Hadoop alle
applicazioni online. Queste applicazioni hanno dei requisiti
tra cui:
– Latenza di risposta in ms
– Accesso random su un sottoinsieme di dati indicizzato
– Supporto di query espressive ed aggregazioni di dati
– Update di dati che cambiano valori frequentemente in real-time

8. Hadoop è la risposta a tutto?
• Nel nostro mondo guidato ormai dai dati, i millisecondi sono
importanti.
– Ricercatori IBM affermano che il 60% dei dati perde valore alcuni
millisecondi dopo la generazione
– Ad esempio identificare una transazione di borsa fraudolenta è inutile
dopo alcuni minuti
• Gartner predice che il 70% delle installazioni di Hadoop fallirà
per non aver raggiunto gli obiettivi di costo e di incremento
del fatturato.

9. Enterprise Data Management Pipeline
…
Siloed source databases
External feeds
(batch)
Streams
Stream icon from: https://en.wikipedia.org/wiki/File:Activity_Streams_icon.png
Transform
Store raw
data
AnalyzeAggregate
Pub-sub,ETL,fileimports
Stream Processing
Users
Other
Systems

10. More specifically
• Join non necessarie causano pessime performance
• Costoso scalare verticalmente
• Lo schema rigido rende difficile il consolidamento
di datai variabili o non strutturati
• Ci sono differenze nei record da eliminare
durante la fase di aggregazione
• Process often takes many hours overnight
• Data is too stale for intraday decisions and
engagement

11. First, Quick MongoDB Background

12. Documents Enable Dynamic Schema & Optimal
Performance
Relational MongoDB
{ customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [
{
number : “1-212-777-1212”,
dnc : true,
type : “home”
},
number : “1-212-777-1213”,
type : “cell”
}]
}
Customer
ID
First Name Last Name City
0 John Doe New York
1 Mark Smith San Francisco
2 Jay Black Newark
3 Meagan White London
4 Edward Daniels Boston
Phone Number Type DNC
Customer
ID
1-212-555-1212 home T 0
1-212-555-1213 home T 0
1-212-555-1214 cell F 0
1-212-777-1212 home T 1
1-212-777-1213 cell (null) 1
1-212-888-1212 home F 2

13. Document Model Benefits
Agility and flexibility
Data model supports business change
Rapidly iterate to meet new requirements
Intuitive, natural data representation
Eliminates ORM layer
Developers are more productive
Reduces the need for joins, disk seeks
Programming is more simple
Performance delivered at scale
{
customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [
{
number : “1-212-777-1212”,
dnc : true,
type : “home”
},
number : “1-212-777-1213”,
type : “cell”
}]
}

14. MongoDB Technical Capabilities
Application
Driver
Mongos
Primary
Secondary
Secondary
Shard 1
Primary
Secondary
Secondary
Shard
2
…
Primary
Secondary
Secondary
Shard
N
db.customer.insert({…})
db.customer.find({
name: ”John Smith”})
1. Dynamic Document
Schema
{ name: “John
Smith”,
date: “2013-08-
01”,
address: “10 3rd
St.”,
phone: {
home:
1234567890,
mobile:
1234568138 }
}
2. Native language
drivers
5. High
performance
- Data
locality
- Indexes
- RAM
3. High
availability
6. Horizontal scalability
- Sharding
4. Workload
Isolation

15. Morphi
a
MEAN
Stack
Java Python PerlRuby
Drivers & Ecosystem

16. Scale
250M Ticks/Sec
300K+ Ops/Sec
500K+ Ops/SecFed Agency
Performance
Petabytes
10s of billions of objects
13B documents
Data
1,400 Servers
1,000+ Servers
250+ Servers
Entertainment Co.
Cluster
Asian Internet Co.

17. 3.2 Features Relevant for EDM
• WiredTiger as default storage engine
• In-memory storage engine
• Encryption at rest
• Document Validation Rules
• Compass (data viewer & query builder)
• Connector for BI (Visualization)
• Connector for Hadoop
• Connector for Spark
• $lookUp (left outer join)

18. Data Governance with Document Validation
Implement data governance without
sacrificing agility that comes from dynamic
schema
• Enforce data quality across multiple
teams and applications
• Use familiar MongoDB expressions to
control document structure
• Validation is optional and can be as
simple as a single field, all the way to
every field, including existence, data
types, and regular expressions

19. MongoDB Compass
For fast schema discovery and
visual construction of ad-hoc
queries
• Visualize schema
– Frequency of fields
– Frequency of types
– Determine validator rules
• View Documents
• Graphically build queries
• Authenticated access

20. MongoDB Connector for BI
Visualize and explore multi-dimensional
documents using SQL-based BI tools. The
connector does the following:
• Provides the BI tool with the schema of the
MongoDB collection to be visualized
• Translates SQL statements issued by the BI
tool into equivalent MongoDB queries that
are sent to MongoDB for processing
• Converts the results into the tabular format
expected by the BI tool, which can then
visualize the data based on user
requirements

21. Dynamic Lookup
Combine data from multiple
collections with left outer joins for
richer analytics & more flexibility in
data modeling
• Blend data from multiple sources
for analysis
• Higher performance analytics with
less application-side code and less
effort from your developers
• Executed via the new $lookup
operator, a stage in the MongoDB
Aggregation Framework pipeline

22. Aggregation Framework – Pipelined Analysis
Start with the original collection; each record
(document) contains a number of shapes (keys),
each with a particular color (value)
• $match filters out documents that don’t
contain a red diamond
• $project adds a new “square” attribute with a
value computed from the value (color) of the
snowflake and triangle attributes
• $lookup performs a left outer join with
another collection, with the star being the
comparison key
• Finally, the $group stage groups the data by
the color of the square and produces statistics
for each group

23. DB & Partner Ecosystem

24. RANK DBMS MODEL SCORE GROWTH (20 MO)
1. Oracle Relational DBMS 1,442 -5%
2. MySQL Relational DBMS 1,294 2%
3.
Microsoft SQL
Server
Relational DBMS 1,131 -10%
4. MongoDB Document Store 277 172%
5. PostgreSQL Relational DBMS 273 40%
6. DB2 Relational DBMS 201 11%
7. Microsoft Access Relational DBMS 146 -26%
8. Cassandra Wide Column 107 87%
9. SQLite Relational DBMS 105 19%
Only non-relational in the top 5; 2.5x ahead of nearest NoSQL Competitor

25. Partner Ecosystem (500+)

26. MongoDB Architecture Patterns
1. Operational Data Store (ODS)
2. Enterprise Data Service
3. Datamart/Cache
4. Master Data Distribution
5. Single Operational View
6. Operationalizing Hadoop
System of Record
System of Engagement

27. Enterprise Data Management Pipeline
…
Siloed source databases
External feeds
(batch)
Streams
Stream icon from: https://en.wikipedia.org/wiki/File:Activity_Streams_icon.png
Transform
Store raw
data
AnalyzeAggregate
Pub-sub,ETL,fileimports
Stream Processing
Users
Other
Systems

28. MongoDB Hadoop/Spark Connector
Distributed
processing/
analytics
• Sub-second latency
• Expressive querying
• Flexible indexing
• Aggregations in database
• Great for any subset of
data
• Longer jobs
• Batch analytics
• Append only files
• Great for scanning all data
or large subsets in files
- MongoDB Hadoop
Connector
- Spark-mongodb
Both provide:
• Schema-on-read
• Low TCO
• Horizontal scale

29. How to choose the data management layer for each or
all stages?
Processing
Layer
?
When you want:
1. Secondary indexes
2. Sub-second latency
3. Aggregations in DB
4. Updates of data
For:
1. Scanning files
2. When indexes
not needed
Wide column store
(e.g. HBase)
For:
1. Primary key
queries
2. If multiple indexes
& slices not
needed
3. Optimized for
writing, not
reading

30. Data Store for Transformed Dataset
…
Siloed source databases
External feeds
(batch)
Streams
Stream icon from: https://en.wikipedia.org/wiki/File:Activity_Streams_icon.png
Transform
Store raw
data
AnalyzeAggregate
Pub-sub,ETL,fileimports
Stream Processing
Users
Other
Systems

31. Data Store for Raw Dataset
…
Siloed source databases
External feeds
(batch)
Streams
Stream icon from: https://en.wikipedia.org/wiki/File:Activity_Streams_icon.png
Transform
Store raw
data
AnalyzeAggregate
Pub-sub,ETL,fileimports
Stream Processing
Users
Other
Systems
Store
raw data
Transfor
m
- Typically just writing
record-by-record from
source data
- Usually just need high
write volumes
- All 3 options handle that
Transform read requirements
- Benefits to reading multiple datasets
sorted [by index], e.g. to do a merge
- Might want to look up across tables
with indexes (and join functionality in
MDB v3.2)
- Want high read performance while
writes are happening
Interactive querying on
the raw data could use
indexes with MongoDB

32. Data Store for Transformed Dataset
…
Siloed source databases
External feeds
(batch)
Streams
Stream icon from: https://en.wikipedia.org/wiki/File:Activity_Streams_icon.png
Transform
Store raw
data
AnalyzeAggregate
Pub-sub,ETL,fileimports
Stream Processing
Users
Other
Systems
AggregateTransform
Often benefits to
updating data as
merging multiple
datasets
Dashboards &
reports can have
sub-second latency
with indexes
Aggregate read requirements
- Benefits to using indexes for grouping
- Aggregations natively in the DB would help
- With indexes, can do aggregations on slices of
data
- Might want to look up across tables with
indexes to aggregate

33. Data Store for Aggregated Dataset
…
Siloed source databases
External feeds
(batch)
Streams
Stream icon from: https://en.wikipedia.org/wiki/File:Activity_Streams_icon.png
Transform
Store raw
data
AnalyzeAggregate
Pub-sub,ETL,fileimports
Stream Processing
Users
Other
Systems
AnalyzeAggregate
Dashboards &
reports can have
sub-second
latency with
indexes
Analytics read requirements
- For scanning all of data, could
be in any data store
- Often want to analyze a slice
of data (using indexes)
- Querying on slices is best in
MongoDB

34. Data Store for Last Dataset
…
Siloed source databases
External feeds
(batch)
Streams
Stream icon from: https://en.wikipedia.org/wiki/File:Activity_Streams_icon.png
Transform
Store raw
data
AnalyzeAggregate
Pub-sub,ETL,fileimports
Stream Processing
Users
Other
Systems
Analyze
Users
Dashboards &
reports can have
sub-second
latency with
indexes
- At the last step, there are
many consuming systems and
users
- Need expressive querying with
secondary indexes
- MongoDB is best option for the
publication or distribution of
analytical results and
operationalization of data
Other
Systems
Often digital
applications
- High scale
- Expressive querying
- JSON preferred
Often
RESTful
services,
APIs

35. More Complete EDM Architecture & Data Lake
…
Siloed source
databases
External feeds
(batch)
Streams
Data processing pipeline
Pub-sub,ETL,fileimports
Stream Processing
Downstrea
m Systems
… …
Single CSR
Application
Unified
Digital Apps
Operational
Reporting
…
… …
Analytic
Reporting
Drivers & Stacks
Customer
Clusterin
g
Churn
Analysis
Predictiv
e
Analytics
…
Distributed
Processing
Governance to
choose where to
load and process
data
Optimal
location for
providing
operational
response times
& slices
Can run
processing on
all data or
slices
Data Lake

36. Example scenarios
1.Single Customer View
a. Operational
b. Analytics on customer segments
c. Analytics on all customers
2.Customer profiles & clustering
3.Presenting churn analytics on high value customers

37. Single View of Customer
Spanish bank replaces Teradata and Microstrategy to
increase business and avoid significant cost
Problem Why MongoDB Results
Problem Solution Results
Took days to implement new
functionality and business policies,
inhibiting revenue growth
Branches needed an app providing
single view of the customer and real
time recommendations for new
products and services
Multi-minute latency for accessing
customer data stored in Teradata and
Microstrategy
Built single view of customer on
MongoDB – flexible and scalable app
easy to adapt to new business needs
Super fast, ad hoc query capabilities
(milliseconds), and real-time analytics
thanks to MongoDB’s Aggregation
Framework
Can now leverage distributed
infrastructure and commodity
hardware for lower total cost of
ownership and greater availability
Cost avoidance of 10M$+
Application developed and deployed
in less than 6 months. New business
policies easily deployed and executed,
bringing new revenue to the company
Current capacity allows branches to
load instantly all customer info in
milliseconds, providing a great
customer experience
Large Spanish
Bank

38. Case Study
Insurance leader generates coveted single view of
customers in 90 days – “The Wall”
Problem Why MongoDB ResultsProblem Solution Results
No single view of customer, leading
to poor customer experience and
churn
145 years of policy data, 70+
systems, 15+ apps that are not
integrated
Spent 2 years, $25M trying build
single view with Oracle – failed
Built “The Wall” pulling in
disparate data and serving single
view to customer service reps in
real time
Flexible data model to aggregate
disparate data into single data
store
Churn analysis done with Hadoop
with relevant results output to
MongoDB
Prototyped in 2 weeks
Deployed to production in 90
days
Decreased churn and improved
ability to upsell/cross-sell

39. Top 15
Global Bank
Kicking Out Oracle
Global bank with 48M customers in 50 countries terminates
Oracle ULA & makes MongoDB database of choice
Problem Why MongoDB Results
Problem Solution Results
Slow development cycles due to RDBMS’
rigid data model hindering ability to meet
business demands
High TCO for hardware, licenses,
development, and support
(>$50M Oracle ULA)
Poor overall performance of customer-
facing and internal applications
Building dozens of apps on MongoDB,
both net new and migrations from Oracle
– e.g., significant portion of retail
banking, including customer-facing and
backoffice apps, fraud detection, card
activation, equity research content mgt.)
Flexible data model to develop apps
quickly and accommodate diverse data
Ability to scale infrastructure and costs
elastically
Able to cancel Oracle ULA. Evaluating
what apps can be migrated to MongoDB.
For new apps, MongoDB is default choice
Apps built in weeks instead of months or
years, e.g., ebanking app prototyped in 2
weeks and in production in 4 weeks
70% TCO reduction