The document discusses different ways to visualize data stored in MongoDB. It begins with an overview of the importance of data visualization and a brief history. It then discusses different architecture options for analytics using MongoDB, including using hidden replicas, building your own solution, MongoDB Compass, the BI Connector, and MongoDB Charts. For each option, it provides details on what it is and when it would be best to use that particular method. It concludes with an overview of the typical lifecycle for visualizing and sharing MongoDB data.

1. #MDBE17
Sam Weaver, MongoDB Product Manager
TPT: UNDERSTANDING YOUR
MONGODB DATA
#samuel_weaver

2. #MDBE17
SAM WEAVER
Product Manager

3. #MDBE17
AGENDA
Background
The importance of data visualization
How do we visualize data in MongoDB (+demo’s)
Q&A
01
02
03
04

4. WHERE ARE WE
NOW?

5. #MDBE17
TERMINOLOGY
“Business
Intelligence” “Business
Analytics”
Data
visualization
ANALYTICS

6. #MDBE17
DATA GROWTH IS EXPLOSIVE
• More data created in the last 2 years
than entire previous history of the
human race
• By 2020:
‒ 1.7MB per person every second

7. #MDBE17
THE STATE OF THE ANALYTICS MARKET
• Analytics is big $!
• $130B in 2016
• $200B+ in 2020
• Less than 0.5% of data is analyzed
and used – imagine the potential!

8. #MDBE17
• Self service
• Mobile access
• Spark
• Real-time analytics
• On prem and cloud
• On demand reporting
EVOLUTION OF ANALYTICS
2017
Today2015 20162014
• Dedicated reporting team
• Desktop access
• Hadoop
• Batch analytics
• On prem only
• Monthly reports

9. THE IMPORTANCE
OF DATA VIZ

10. #MDBE17

11. #MDBE17
EARLY DATA VISUALIZATION
Charles Minard (1869)
-- Napolean’s march and
retreat on Moscow in
1812.

12. I
X Y
10 8.04
8 6.95
13 7.58
9 8.81
11 8.33
14 9.96
6 7.24
4 4.26
12 10.84
7 4.82
5 5.68
9.00 7.50
10.00 3.75
0.816

13. I
X Y
10 8.04
8 6.95
13 7.58
9 8.81
11 8.33
14 9.96
6 7.24
4 4.26
12 10.84
7 4.82
5 5.68
9.00 7.50
10.00 3.75
0.816

14. I
X Y
10 8.04
8 6.95
13 7.58
9 8.81
11 8.33
14 9.96
6 7.24
4 4.26
12 10.84
7 4.82
5 5.68
9.00 7.50
10.00 3.75
0.816
II III IV
X Y X Y X Y
10 9.14 10 7.46 8 6.58
8 8.14 8 6.77 8 5.76
13 8.74 13 12.74 8 7.71
9 8.77 9 7.11 8 8.84
11 9.26 11 7.81 8 8.47
14 8.1 14 8.84 8 7.04
6 6.13 6 6.08 8 5.25
4 3.1 4 5.39 19 12.5
12 9.13 12 8.15 8 5.56
7 7.26 7 6.42 8 7.91
5 4.74 5 5.73 8 6.89
9.00 7.50 9.00 7.50 9.00 7.50Mean
10.00 3.75 10.00 3.75 10.00 3.75Variance (Population)
0.816 0.816 0.817 Correlation (Pearson)

18. SO YOU WANT TO
VISUALIZE?

21. #MDBE17
THING’S TO THINK ABOUT
• Use the correct architecture
• Determine what your needs are
‒ Multiple data sources?
‒ Huge amounts of complex data?
‒ Quick self service?
• Choose the right solution for you

22. ARCHITECTURE
FOR ANALYTICS

23. #MDBE17
USING HIDDEN REPLICAS
• Hidden secondaries maintain
a copy of the primary’s data
set
• Hidden secondary's are used
for workloads with different
access patterns
• Cannot become primary
Client
Primary
Secondary
Secondary
Secondary
Secondary
P=0 Hidden=True
Analytics

24. BUILD YOUR OWN

25. #MDBE17
BUILD YOUR OWN
• Pros
‒ Custom tailored solution: fits exactly as required!
• Cons
‒ High investment
‒ Maintenance
‒ Deep understanding of the underlying tech and its language(s)

26. MONGODB
COMPASS

27. #MDBE17
MONGODB COMPASS
• Developer tool
• Data management and manipulation
• Interesting schema analysis
• Used daily: a good first place to start

28. #MDBE17
WHEN TO USE?
• Day-to-day development/operations
• Adding indexes
• Viewing server stats
• Data manipulation
• 10,000->1ft view of data

29. BI CONNECTOR

30. #MDBE17
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

31. #MDBE17
BI CONNECTOR ARCHITECTURE

32. #MDBE17
WHEN TO USE?
• Multi data sources (not just mongodb)
• Business analysts
• Extremely powerful but high ramp

33. MONGODB CHARTS

34. #MDBE17
MONGODB CHARTS
• Lightweight
• Intuitive
• Build visualizations on MongoDB data (nested, polymorphic)
• Share content in a dashboard

35. #MDBE17
WHEN TO USE?
• When you want quick answers
• No need to flatten / ETL your mongodb data
• Self service for the technical audience

36. #MDBE17
LIFECYCLE
1. Acquire 2. Prep
- Calcs
- Groups
- Data types
3. Visualize
- Bar
- Pie
- Line
4. Explore
- Dashboards
5. Share
- Export
- Collaborate
- Embed

37. DEMO