MongoDB is known for being a developers database of choice, but what about data analysts? MongoDB 3.2 has introduced the MongoDB BI Connector – to allow users to connect to an instance using their analytics tool of choice. Now users of Tableau, QlikView, Excel, Cognos, and countless others can connect to MongoDB and immediately begin building reporting solutions. In this webinar, we will cover the architecture needed to use the BI Connector with MongoDB. We will also demonstrate how to build reports with your data.

2. MongoDB & Analytics
Building solutions with the MongoDB BI Connector
Patrick Sheehan – Sr. Solutions Architect, MongoDB - Palo Alto, CA

3. 3
Context – Business Intelligence (BI)
Business intelligence (BI) is a technology-
driven process for analyzing data and
presenting actionable information to help
corporate executives, business managers
and other end users make more informed
business decisions.
BI encompasses a variety of tools,
applications and methodologies that enable
organizations to collect data from internal
systems and external sources, prepare it for
analysis, develop and run queries against
the data, and create reports, dashboards
and data visualizations to make the
analytical results available to corporate
decision makers as well as operational
workers.
Porta Ultricies
Commodo Porta

4. 4
BI is a BIG Deal!
Gartner Says Worldwide Business Intelligence and Analytics
Market to Reach $16.9 Billion in 2016
Global revenue in the business intelligence (BI) and analytics market is forecast to reach
$16.9 billion in 2016, an increase of 5.2 percent from 2015, according to the latest
forecast from Gartner, Inc.
According to Gartner, the BI and analytics market is in the final stages of a multiyear shift
from IT-led, system-of-record reporting to business-led, self-service analytics. As a result,
the modern business intelligence and analytics (BI&A) platform has emerged to meet
new organizational requirements for accessibility, agility and deeper analytical insight.
Source: Gartner NewsRoom, February, 2016: http://www.gartner.com/newsroom/id/3198917

5. 5
Context - MongoDB
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

6. 6
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
location:
[45.123,47.232],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
MongoDB
Document Model with Flexible Schema
RDBMS

7. 7
Documents are Rich Data Structures
{
first_name: ‘Paul’,
surname: ‘Miller’,
cell: 447557505611,
city: ‘London’,
location: [45.123,47.232],
Profession: [‘banking’, ‘finance’, ‘trader’],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
Fields can contain an array
of sub-documents
Fields
Typed field values
Fields can contain arrays
Number

8. 8
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

9. 9
Location & Flow of Data
MongoDB
BI
Connector
Mapping meta-data Application data
{name:
“Andrew”,
address:
{street:…
}}
DocumentTableAnalytics & visualization

10. 10
Defining Data Mapping
mongodrdl --host 192.168.1.94 --port 27017 -d myDbName
-o myDrdlFile.drdl
mongobischema import myCollectionName myDrdlFile.drdl
DRDL
mongodrdl mongobischema
PostgreSQL
MongoDB-
specific
Foreign Data
Wrapper

11. 11
Optionally Manually Edit DRDL File
• Redact attributes
• Use more appropriate types
(sampling can get it wrong)
• Rename tables (v1.1+)
• Rename columns (v1.1+)
• Build new views using
MongoDB Aggregation
Framework
• e.g., $lookup to join 2 tables
- table: homesales
collection: homeSales
pipeline: []
columns:
- name: _id
mongotype: bson.ObjectId
sqlname: _id
sqltype: varchar
- name: address.county
mongotype: string
sqlname: address_county
sqltype: varchar
- name:
address.nameOrNumber
mongotype: int
sqlname:
address_nameornumber
sqltype: varchar

12. Data, Data Every WhereDemo

13. 13
Next Steps
 Download the MongoDB 3.2 Whitepaper
https://www.mongodb.com/collateral/mongodb-3-2-whats-new
 Try the MongoDB Connector for BI
https://www.mongodb.com/lp/download/mongodb-enterprise
 Review the docs!
https://docs.mongodb.org/bi-connector/
https://docs.mongodb.org/bi-connector/installation/
https://docs.mongodb.org/bi-connector/schema-configuration/
https://docs.mongodb.org/bi-connector/components/
 FAQ
https://docs.mongodb.org/bi-connector/faq/
 Developers Notebook
https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/README.md

14. Questions?