1. 1 6 J A N U A RY, 2 0 1 8 | H I LT O N
# M D B l o c a l
DATA ANALYTICS IN
MONGODB

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PRODUCT
MANAGER
SAM WEAVER
MONGODB @samuel_weaver

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SAM WEAVER
PRODUCT MANAGER, MONGODB
@samuel_weaver

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1. Background
2. The importance of data visualization
3. Methods for data visualization in MongoDB
4. Q&A
AGENDA

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WHERE ARE WE NOW?

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TERMINOLOGY
“Business
Intelligence” “Business
Analytics”
ANALYTICS

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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

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

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EVOLUTION OF ANALYTICS
• Self service
• Mobile access
• Spark
• Real-time analytics
• On-prem and cloud
• On demand reporting
2018
Today2015 20162014
• Dedicated reporting team
• Desktop access
• Hadoop
• Batch analytics
• On prem only
• Monthly reports

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IMPORTANCE OF DATA VIZ

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EARLY DATA VISUALIZATIONS
Charles Minard (1869)
-- Napoleon’s march and
retreat on Moscow in
1812.

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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

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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

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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)

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SO YOU WANT TO VISUALIZE?

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• 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
THINGS TO THINK ABOUT

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ARCHITECTURE FOR
ANALYTICS

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HIDDEN REPLICAS
• Hidden secondaryies maintain
a copy of the primary’s data
set
• Hidden secondaryies are used
for workloads with different
access patterns
• Cannot become primary
Client
Primary
Secondary
Secondary
Secondary
Secondary
P=0 Hidden=True
Analytics

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BUILD YOUR OWN

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BUILD YOUR OWN
• Pro’s
• Custom tailored solution: fits exactly as required!
• Con’s
• High investment
• Maintenance
• Deep understanding of the underlying tech and its language(s)

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USE THE TOOLS WE GIVE YOU

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

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

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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

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BI CONNECTOR

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WHEN TO USE
• Multi datasources (not just Mongodb)
• Business analysts
• Extremely powerful but high ramp

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

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WHEN TO USE
• When you want quick answers
• No need to flatten / ETL your Mongodb data
• Self service for the technical audience

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LIFE CYCLE
1. Acquire 2. Prep
- Calcs
- Groups
- Data types
3. Visualize
- Bar
- Pie
- Line
4. Explore
- Dashboards
5. Share
- Export
- Collaborate
- Embed

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