This document discusses options for visualizing data from MongoDB databases. It begins by outlining some architectural considerations for visualizing hidden secondary databases that maintain copies of primary data for different access patterns. It then discusses several options for visualizing MongoDB data, including building custom visualizations using code and charting libraries, using MongoDB's BI Connector to connect to third-party BI tools like Excel, and using MongoDB Compass or Charts for interactive data exploration and visualization without code. It provides advice on which tools are best suited for different use cases like exploratory analysis, self-service visualization, or complex reporting needs. The document concludes with recommendations to think about your specific architecture, needs, and tools that fit those needs.

1. Data Analytics with MongoDB
for fun and profit
Michael Gordon
CFO, MongoDB
Dr. Grigori Melnik
VP, Products, MongoDB
@gmelnik

2. Safe Harbor Statement
The development, release, and timing of any features or functionality
described for our products remains at our sole discretion. This
information is merely intended to outline our general product direction
and it should not be relied on in making a purchasing decision nor is
this a commitment, promise or legal obligation to deliver any material,
code, or functionality.

3. data is a new oil

4. data is a new soil

6. Options for Visualizing MongoDB Data
Custom Code +
Charting Libraries
ETL +
3rd Party BI Tools
MongoDB BI Connector +
3rd Party BI Tools
MongoDB
Charts
MongoDB Compass

7. Things to Think About
• Use the correct architecture
• Determine what your needs are
• Multiple heterogeneous data sources?
• Huge amounts of complex data?
• Sophisticated aggregations?
• Quick self-service?
• Need to interact? share? collaborate?
• Choose the right tools for you

9. Architectural
Considerations

10. Architectural Considerations
• Hidden secondaries
maintain a copy of the
primary data set
• Hidden secondaries are
used for workloads with
different access patterns
• Cannot become primary

11. Build Your Own

12. Custom Code with Some Reuse
• Powerful Aggregation Framework
• Rich collection of drivers (incl. Python and R)
• 3rd party charting libraries (d3, Vega, Vega.lite etc)
• Pros:
• Custom tailored solution: fits exactly as required!
• Idiomatic to your development language
• High degree of expressiveness
• Cons:
• High investment
• Maintenance
• Deep understanding of the underlying tech and its language(s)

13. Exploratory Analysis with
MongoDB Compass

14. MongoDB Compass
• Developer tool
• Data management and
manipulation
• document view
• table view
• Visual schema analyzer
• with query builder
• export to language
• Aggregation pipeline builder
• A good first place to start

15. MongoDB Compass - When to Use
• Exploratory data analysis
• Data preparation & basic manipulation
• Data ingestion via JSON or CSV import
• Day-to-day development/operations
• Adding and understanding indexes
• Adding validation rules
• Authoring & troubleshooting aggregation pipelines
• Viewing real-time server stats
• 10,000 → 1ft view of data

16. Demo:
Exploratory Analysis with
Compass

17. MongoDB
BI Connector

18. MongoDB BI Connector
• Visualize and explore MongoDB
data in SQL-based BI tools:
• Automatically discovers the schema
• Translates complex SQL statements
issued by the BI tool into MongoDB
aggregation queries
• Converts the results into a tabular
format for rendering inside the BI tool

19. BI Connector & ODBC Driver
ODBC Driver mongosqld
...

20. MongoDB BI Connector - When to Use
• Want to speak SQL to MongoDB
• Multi data sources (not just MongoDB)
• Business analysts
• Reporting only
• Powerful but you lose the benefits of the Document Model

21. Demo:
Data Analysis with
Microsoft Excel and
BI Connector

22. MongoDB
Charts

23. Wouldn’t it be lovely if...
You could visualize your MongoDB data natively…
• without needing to write custom code
• without needing to move your data into a different repository or
• without needing to wrangle with flaky ETL pipelines
• without needing to purchase and configure third-party tools
• without losing the richness of the Document Model

24. MongoDB Charts
• Lightweight
• Intuitive
• Build visualizations on MongoDB
data (nested, polymorphic)
• Share content easily

25. MongoDB Charts - When to Use
• The fastest way to build visualizations over your MongoDB data
• Ad hoc analyses
• Benefit from the Document Model
• No need to flatten /ETL your mongodb data
• Type handling
• Polymorphic collections
• Nested documents
• Array reductions
• Prebuilt dashboards for collab
• Self-service
• Intuitive enough for domain experts, non-devs to use!

26. Demo:
Data Viz with Charts

27. Lifecycle
1. Ingest 2. Explore/Prepare
‒ Calcs
‒ Groups
‒ Data types
3. Visualize
‒ Chart types
‒ Binning
‒ Limiting
‒ Multi-series
4. Analyze/Use
‒ Dashboards
‒ Key views on data
‒ Patterns
‒ Drilldowns
‒ Pivots
5. Share
‒ Export
‒ Collaborate
‒ Embed

28. Final Thoughts
To think clearly about the relationship between evidence and conclusion,
the relevant question is How do I know that? Answering this question
requires self-awareness about the quality and integrity of information,
and particularly how that information arrived to one’s own world.
Similarly, to ask others, How do you know that? How do they know that?
These questions are among the best you can ask analytically.
~ Edward Tufte
you
How do I know that?
they
{ }

29. Other Recommended Sessions

30. Thank you