Beyond the Basics 3: Introduction to the MongoDB BI Connector
Download as PPTX, PDF2 likes2,312 views
The document introduces the MongoDB BI Connector, which enables the visualization and exploration of multi-dimensional documents using SQL-based BI tools. It highlights the importance of business intelligence (BI) for informed decision-making, backed by a projected market growth to $16.9 billion in 2016. Additionally, it describes the connector's functionality in translating SQL queries to MongoDB queries while facilitating data mapping between document and tabular formats.
![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](https://image.slidesharecdn.com/introtomongodbbiconnector1-170504094226/85/Beyond-the-Basics-3-Introduction-to-the-MongoDB-BI-Connector-10-320.jpg)
![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](https://image.slidesharecdn.com/introtomongodbbiconnector1-170504094226/85/Beyond-the-Basics-3-Introduction-to-the-MongoDB-BI-Connector-12-320.jpg)
Beyond the Basics 3: Introduction to the MongoDB BI Connector
- 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?