Webinar: MongoDB and Analytics: Building Solutions with the MongoDB BI Connector
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Transcript of Webinar: MongoDB and Analytics: Building Solutions with the MongoDB BI Connector
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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
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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
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Questions?
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