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DATA ANALYTICS / UNDERSTANDING YOUR DATA

SEATTLE

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WIFI: MONGODB PW: mdb

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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

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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 7.50 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

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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 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

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