Webinar: Introducing the MongoDB Connector for BI 2.0 with Tableau

Introducing the MongoDB
Connector for BI 2.0 with Tableau
Buzz Moschetti
Enterprise Architect
buzz.moschetti@mongodb.com
@buzzmoschetti
Vaidy Krishnan
Senior Product Marketing Manager
vkrishnan@tableau.com

Agenda
• Introduction to MongoDB
• What is The BI Connector?
• Analytics with Tableau on MongoDB
• Demo
• Best Practices

3
MongoDB:
The Post-Relational General Purpose Database
Document
Data
Model
Open-
Source
Fully Featured
High Performance
Scalable
{
name: “John Smith”,
pfxs: [“Dr.”,”Mr.”],
address: “10 3rd St.”,
phone: {
home: 1234567890,
mobile: 1234568138 }
}

4
MongoDB Company Overview
600+ employees 2500+ customers
Over $311 million in funding
Offices in NY & Palo Alto and
across EMEA, and APAC

6
db-engines.com Ranks ~300 Databases

7
Nexus Architecture
Scalability
& Performance
Always On,
Global Deployments
Flexibility
Expressive Query Language
& Secondary Indexes
Strong Consistency
Enterprise Management
& Integrations
• Deep Dive

8
Major Sweet Spots
Big Data Product & Asset
Catalogs
Security &
Fraud
Internet of Things Database-as-a-
Service
Mobile
Apps
Customer Data
Management
Single View Social &
Collaboration
Content
Management
Intelligence Agencies
Top Investment and
Retail Banks
Top Global Shipping
Company
Top Industrial Equipment
Manufacturer
Top Media Company
Top Investment and
Retail Banks
Complex Data
Management
Top Investment and
Retail Banks
Embedded / ISV
Cushman & Wakefield

Agenda
• Introduction To MongoDB
• Analytics with Tableau on MongoDB
• Demo
• Best Practices

10
MongoDB Query Language is Powerful
> db.results.values.aggregate([
{$match: { runnum:23, timeSeriesPath: "CDSSpread.12M//1909468128” }
,{$project: { timeSeriesPath: "$timeSeriesPath", values: foml }}
,{$unwind: {path: "$values", idx: "v_idx"}}
,{$match: {values: {$gt: 60}, {$or: [ {idx: 0}, {idx: {$size: . . .}
,{$group: {_id: {a: "$timeSeriesPath", b: term: "$idx"},
n: {$sum:1}, max: {$max: "$values"}, min: {$min: "$values"}},
sdev: {$stdDevPop: "$values"}}
,{$lookup: { from: ”deskLimits", localField: ”instID", foreignField:
”instID", as: ”inst"}}
,{$match: {maxDeskLimit: {$gt: {$cond: [ {$gt: [2, $max]}, 2, $max]}}}}
,{$group: {_id: "$deskID", total: {$sum: “$max”}}}
]);

11
Able To Leap Tall Buildings in a Single Bound!
> db.foo.insert({_id:1, "poly": [ [0,0], [2,12], [4,0], [2,5], [0,0] ] });
> db.foo.insert({_id:2, "poly": [ [2,2], [5,8], [6,0], [3,1], [2,2] ] });
> db.foo.aggregate([
{$project: {"conv": {$map: { input: "$poly", as: "z", in: {
x: {$arrayElemAt: ["$$z”,0]}, y: {$arrayElemAt: ["$$z”,1]}
,len: {$literal: 0} }}}}}
,{$addFields: {first: {$arrayElemAt: [ "$conv", 0 ]} }}
,{$project: {"qqq":
{$reduce: { input: "$conv", initialValue: "$first", in: {
x: "$$this.x”, y: "$$this.y"
,len: {$add: ["$$value.len", // len = oldlen + newLen
{$sqrt: {$add: [
{$pow:[ {$subtract:["$$value.x","$$this.x"]}, 2]}
,{$pow:[ {$subtract:["$$value.y","$$this.y"]}, 2]}
] }} ] } }}
,{$project: {"len": "$qqq.len"}}
{ "_id" : 1, “len" : 35.10137973546188 }
{ "_id" : 2, "len" : 19.346952903339393 }

12
… But It Doesn’t Natively Speak SQL
> db.restaurants.sql("select * from restaurants where cusine =
'Peruvian'");
2017-01-12T14:57:23.930-0500 E QUERY [main] TypeError:
db.restaurants.sql is not a function

13
The MongoDB BI Connector: A “SQL Bridge”
MongoDB
MongoDB
BI
Connector
Anything
That
Speaks
MySQL
select A.fn, A.LN, P.prodType, T.amt, T.td
from tx T
JOIN product P on T.product = P.prod
JOIN acct A on T.acct = A.acct
where
A.acct in ('A5' , 'A10')
and T.td = '2015-03-01 00:00:00’
and P.prodType = 'CAR'
db.tx.aggregate([
{$match:{td:ISODate(“2015-03-01 00:00:00”)},
{$lookup:{from: “acct”, localfield: “acct” …
{$match:{acct: {$in: [“A5”, “A10” ]}},
{$lookup:{from: “product”, localfield: “prod”
{$match: {prodType: “CAR”}}

14
The MongoDB BI Connector: A “SQL Bridge”
MongoDB
MongoDB
BI
Connector
select A.fn, A.LN, P.prodType, T.amt, T.td
from tx T
JOIN product P on T.product = P.prod
JOIN acct A on T.acct = A.acct
where
A.acct in ('A5' , 'A10')
and T.td = '2015-03-01 00:00:00’
and P.prodType = 'CAR'
db.tx.aggregate([
{$match:{td:ISODate(“2015-03-01 00:00:00”)},
{$lookup:{from: “acct”, localfield: “acct” …
{$match:{acct: {$in: [“A5”, “A10” ]}},
{$lookup:{from: “product”, localfield: “prod”
{$match: {prodType: “CAR”}}

15
Authentication & Entitlements are ALSO Bridged
MongoDB
MongoDB
BI
Connector
biUser?mechanism=
MONGODB-CR,source=authDB
password=*******
client = connect(biUser,
*******);

16
A Mapping File is The Key Ingredient
schema:
- db: food
tables:
- table: restaurants
collection: restaurants
columns:
- Name: _id
MongoType: bson.ObjectId
SqlName: _id
SqlType: varchar
- Name: address.building
MongoType: string
SqlName: address.building
SqlType: varchar
MongoDB
MongoDB
BI
Connector

17
Mapping Generator to Get You Started
MongoDB
MongoDB
BI
Connector
mongodrdl –d food –c restaurants –o food.drdl
mongosqld –schema=food.drdl

Connectivity
Access to all
data
Performance
Fast interaction
with all data
Discovery
Finding the right
data.
Tableau’s Big Data Focus

Analytics for All your Data
Broad access to Big Data
platforms
Visual analytics without coding
Platform query performance
Consistent visual interface
Hybrid data architecture

Big Data Connectivity Roadmap
2010 2012 2013 2014 2015
Tableau v6.1.4
Cloudera Hadoop
Tableau v7.0.10
Hortonworks
Hadoop
Tableau v8.2.3
IBM BigInsights
Tableau v9.0
Spark SQL
Tableau v5.2
Pivotal Greenplum
& HAWQ
2011
Tableau v7.0.10
Cloudera Impala
Tableau v7.0.7
MapR Hadoop
Tableau v7.0.10
Datastax Enterprise
& Cassandra
Tableau v8.1.4
Splunk
Tableau v8.0.1
Amazon Redshift
Tableau v8.2.3
MarkLogic
Tableau v8.3.2
Amazon EMR
Tableau v8.0
Google BigQuery
Today
2016 2017

Cold, Warm, Hot Framework
• The Data Lake
• Store Everything and
Anything
• Unknown Questions
with Unknown Answers
• Unstructured / Data
Mining / Data Science
• Data Warehouses
• Data marts prepared
for entity analytics
• Known questions
with unknown
answers
• Regularly refreshed
business concepts
• In-memory computing
• Precomputed aggregates
to answer specific
questions
• Known questions with
known answers
• Dashboards

Aggregated dataPrepared data
Data
Size
Performance
Large data (raw or prepared)
Cold, Warm, Hot Strategy

Aggregated dataPrepared data
Data
Size
Performance
Large data (raw or prepared)
Cold, Warm, Hot Strategy with Optimized MongoDB

How do we see customers using Tableau on MongoDB
• Use Case
–Data Exploration/Mining
–Ad-Hoc Report Conceptual Modeling
–Query directly/Explore Concepts to Migrate to Analytically Optimized
Data Stores
MongoDB

• Financial Services: Analyze ticks, tweets, satellite imagery, weather trends,
and any other type of data to inform trading algorithms in real time.
• Government: Identify social program fraud within seconds based on program
history, citizen profile, and geospatial data.
• HighTech: Identify unique individuals across any type of device, browser or
app and use a holistic behavioral model to advertise to them.
• Retail: Set up a digital geo-fence around your brick-and-mortar locations to
push in-store incentives to shoppers in real time.
• MongoDB – Verticals & Use Cases

Agenda
• Introduction To MongoDB
• Analytics With Tableau on MongoDB
• Demo
• Best Practices

Basic MongoDB Optimizations
✔ DO: ✗ AVOID
• Model for use
• Index effectively
• Use prejoined array tables
• Leverage custom pipelines in DRDL
• Let dates (SQL timestamp) and decimal
types flow w/o conversion to string
• Casts
• Date arithmetic
• Cross-collection
• Non-equijoins
• Subqueries

Tableau Data Extracts – When to use them?
Extracts Recommended Live Connection Recommended
• Slow SQL to MQL translation
• Smaller dataset sizes needed
• Offline analysis required
• Reduce “big query” impact on
nominal workload performance**
• Fast SQL to MQL translation
• Larger dataset sizes needed
• Real-time analysis required

• Extract Sampling Techniques
• Filters
• Keep only well-known dimensions and measures
• Use short date ranges
• Aggregates
• Aggregate dimensions and measures when possible
• Roll-up dates when possible
• Samples
• Utilize Custom SQL with sample function
• Top N
• May be skewed since non-random sampling
Optimize your Tableau Data Extracts

General Techniques for Improvement
Partition field as filter
Single denormalized table
Monitor for long running queries
• Data blending large datasets
– Executed on the Tableau client side
• Cull Unnecessary joins
– …and take advantage of prejoined tables in the BI Connector
– Imperfectly implemented on many big data systems
– Assume referential integrity
• Inefficient formulas
MongoDB

Leverage a multi-tiered approach based on your
data
TD
E
+
Fast
analytical
database
Aggregated
data
Prepared
data
Raw data
(large)
MongoDB

• Chunks of Human Consumable Data
• Aggregation of Data Tiers
• Year to Quarter to Month to Week
to Day to Records
• Region to Country to State to
County to Zip Code
• Drill Down to Raw Data with Context
• Use Aggregates for Guided Drilling
• Use Action Filters to Navigate the
Pyramid
•Human Scale of Data
Single
Consumable
Chunk of Data
at the Human Scale
(Dashboard)
Aggregation Level
Year(4)Month(48)Week(105)Day(90)RawData
Filter Year
Filter Month
Filter Week
Filter Day
Select Week
Select Month
Select DimensionSelect Dimension
In the Weeds

• Use Action Filters to Jump from Tier
to Tier with a filter context
• Drill Down to the Details
• Leave the Data in the Appropriate
Data Architecture
• Hot - Analytical Query
• Warm - Entity Query
• Cold - Data Discovery
Action Filters: Big Data Secret Weapon
COLD
WARM
HOT
• Dashboard or Document Acceleration
• High Performance
• Aggregations
• Persistence
• Row Level Security
• Live Connections
• Core Report Development
• Data Mining
• Detailed Data
• Raw Data
• Machine Learning

1. Do you have sufficient infrastructure/hardware to deal with the kind of data that will be
analyzed? ~ Law of inertia , nothing moves till there is sufficient force applied to move it
2. Have you chosen an underlying data source that matches your performance aspirations,
and have you engineered it for interactive performance? ~ law of dynamics, Force = mass *
acceleration
3. Have you designed your Tableau vizzes so that the queries run efficiently? ~ For every
action (viz) there is an equal and opposite reaction (from the data source)
Don’t forget the laws of Data Motion

Thank You!
Buzz Moschetti
Enterprise Architect
buzz.moschetti@mongodb.com
@buzzmoschetti
Vaidy Krishnan
Senior Product Marketing Manager
vkrishnan@tableau.com

Webinar: Introducing the MongoDB Connector for BI 2.0 with Tableau

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