The Path to Truly Understanding Your MongoDB Data
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Transcript of The Path to Truly Understanding Your MongoDB Data
#MDBlocal
DATA ANALYTICS / UNDERSTANDING YOUR DATA
SEATTLE
# M D B l o c a l
#MDBlocal
WIFI: MONGODB PW: mdb
#MDBlocal
SAM WEAVER
PRODUCT MANAGER, MONGODB
@samuel_weaver
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AGENDA
1. Background2. The importance of data visualization3. Methods for data visualization in MongoDB 4. Lunch
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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 20162012
• 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)-- Napolean’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
#MDBlocal
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 7.50 Mean
10.00 3.75 10.00 3.75 10.00 3.75 Variance (Population)
0.816 0.816 0.817 Correlation (Pearson)
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SO YOU WANT TO VISUALIZE?
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# M D B l o c a l
#MDBlocal
• 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 secondary's maintain a copy of the primaries data set
• Hidden secondary's are used for workloads with different access patterns
• Cannot become primary
Client
Primary
Secondary
Secondary
Secondary
SecondaryP=0 Hidden=True
Analytics
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TOOLING
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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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DEMO
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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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THANK YOU
# M D B l o c a l
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# M D B l o c a l
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