Watch this presentation to learn how the MongoDB BI Connector lets you use MongoDB as a data source for your SQL-based BI and analytics platforms. Learn how to seamlessly create the visualizations and dashboards that will help you extract the insights and hidden value in your multi-structured data.

2. Introduction to the MongoDB
BI Connector
Joe Drumgoole
Director of Developer Advocacy, EMEA
@jdrumgoole
Joe.Drumgoole@mongodb.com
V1.0

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
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

6. 6
Location & Flow of Data
MongoDB
BI
Connector
Mapping meta-data Application data
{name:
“Andrew”,
address:
{street:…
}}
DocumentTableAnalytics & visualization

7. 7
Defining Data Mapping
mongodrdl –-host 127.0.0.1 --port 27017 –d=<database_name> –c=<collection_name> -
o schema.drdl
mongosqld --addr=127.0.0.1:3307 --mongo-uri=127.0.0.1:27017 –schema=schema.drdl
DRDL mongodrdl
mongosqld
Localhost:3307 Localhost:27017

8. MongoDB DRDL
• DRDL – Document Relational Definition Language
• Maps Nested document schema to tabular schema
$ mongodrdl –d=BIMUGS –c=members -o members.drdl
2017-05-03T14:52:09.819+0100 I MONGODRDL Table "audit", column
"info.args.apikey" has no types: ignoring column.
2017-05-03T14:52:09.819+0100 I MONGODRDL Table "audit", column
"info.args.verbose" has no types: ignoring column.
$ ls *.drdl
members.drdl
$

9. Example Document
{
"batchID" : 1,
"timestamp" : ISODate("2017-05-03T17:49:20.887Z"),
"group" : {
"city" : "Las Vegas",
"who" : "MongoDB LV'ers",
"description" : "…",
"created" : ISODate("2016-05-19T01:04:25Z"),
"country" : "US",
"join_mode" : "open",
"visibility" : "public",
"localized_country_name" : "USA",
"state" : "NV",
"link" : "https://www.meetup.com/Las-Vegas-MongoDB-Meetup/",
"members" : 37,
"timezone" : "US/Pacific",
"urlname" : "Las-Vegas-MongoDB-Meetup",
"id" : 19969928,
"name" : "Las Vegas MongoDB Meetup"
}
}

10. Example DRDL
schema: # Defines a schema file
- db: BIMUGS # from this database (-d=<database name>)
tables: # which has tables (members)
- table: members
collection: members # created from collection members
pipeline: [] # filter to apply
columns: # mapping from document fields to columns
- Name: _id
MongoType: bson.ObjectId
SqlName: _id
SqlType: varchar
- Name: batchID
MongoType: int
SqlName: batchID
SqlType: int

11. Demo
• Create DRDL
• Start mongosqld
• Show data in mysql command line
• Show data in Tableau
• Create a basic Tableau report
• Edit the DRDL and rerun tableau
• (Maybe show creating a new pipeline)

12. 12
Optionally Manually Edit DRDL File
• Redact attributes
• Use more appropriate types
(sampling can get it wrong)
• 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

13. 13
Next Steps
➢ Try the MongoDB Connector for BI
https://www.mongodb.com/lp/download/mongodb-enterprise
➢ Review the docs!
https://docs.mongodb.com/bi-connector/current/
➢ FAQ
https://docs.mongodb.org/bi-connector/faq/

14. Q&A?