MongoDB & Analytics

Building solutions with the MongoDB BI Connector

Patrick Sheehan – Sr. Solutions Architect, MongoDB - Palo Alto, CA

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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

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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

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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

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{ 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 SchemaRDBMS

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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

String

Number

Geo-Location

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MongoDB Connector for BIVisualize 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

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Location & Flow of Data

MongoDB BI

Connector

Mapping meta-data Application data

{name:“Andrew”,address: {street:…}}

DocumentTableAnalytics & visualization

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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

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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: homeSalespipeline: []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

Data, Data Every WhereDemo

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Next Steps Download the MongoDB 3.2 Whitepaperhttps://www.mongodb.com/collateral/mongodb-3-2-whats-new

Try the MongoDB Connector for BIhttps://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/

FAQhttps://docs.mongodb.org/bi-connector/faq/

Developers Notebookhttps://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/README.md

Questions?