Data analytics can offer insights into your business and help take it to the next level. In this talk you'll learn about MongoDB tools for building visualizations, dashboards and interacting with your data. We'll start with exploratory data analysis using MongoDB Compass.

1. Data Analytics with
MongoDB

2. JANE FINE
Director of Product Marketing,
Analytics
jane.fine@mongodb.com
@janeuyvova
SETH PAYNE
Product Manager
seth.payne@mongodb.com

3. Data Analytics with MongoDB
Custom Code +
Charting
Libraries
ETL +
3rd Party BI
Tools
MongoDB BI Connector
+
3rd Party BI Tools
MongoDB
Charts
MongoDB
Compass

4. What To Expect
MongoDB Connector for BI
MongoDB Charts
MongoDB Aggregation Pipeline / MongoDB Compass
…
Lots of demos

5. MongoDB Connector for
Business Intelligence

6. 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

7. Atlas Hosted BI Connector
MongoSQLD
...

8. MongoDB ODBC Driver
MongoSQLD
...
ODBC Driver

9. Schema Translation
{
_id: ObjectId("5b9a6d75f68257c5a994558d"),
id: 2318,
listing_url:
"https://www.airbnb.com/rooms/2318",
name: "Casa Madrona",
summary: "Gorgeous 1917 Dutch Colonial…”
calendar: [
{ listing_id: 2318,
date: "2019-08-15",
available: "true",
price: 296
},
{ listing_id: 2318,
date: "2019-08-14",
available: "true",
price: 296
}
,...
mysql> show tables;
+----------------+
| Tables_in_test |
+----------------+
| sea |
| sea_calendar |
| sea_ratings |
+----------------+
3 rows in set (0.06 sec)

10. 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 some benefits of schema flexibility

11. MongoDB Charts

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

13. Wouldn’t it be nice if...
You could visualize your MongoDB Data…
without needing to write your own code
without needing to move your data into a different repository
without needing to purchase and configure third-party tools
without losing the richness of the Document Model

14. MongoDB Charts beta
The fastest way to build
visualizations over your
MongoDB data
Built for the MongoDB Document
Model
Visualize live data from on-prem
or Atlas DB

15. Charts Basic Concepts
A data source is a reference to a MongoDB collection or view that
contains data you want to visualize.
A chart is a visualization of data from a single data source.
A dashboard is a collection of charts which you manage as a unit
(name, layout, sharing)

16. What to Expect
Common chart types
Aggregation functions
Filtering
Sample Mode
Binning
Sorting
Type handling
Polymorphic collections
Nested documents
Array reductions
Charting Capabilities Document Model Support

17. MongoDB Charts - When to Use
• The fastest way to build visualizations over your MongoDB
data
• Ad hoc analyses
• Benefit from the Document Model
• Collaboration
• Self-service
• Intuitive enough for domain experts, non-devs to use!

18. MongoDB Aggregation
Framework
and MongoDB Compass

19. Rich Queries
Point | Range | Geospatial | Faceted Search | Aggregations | JOINs |
Graph Traversals
JSON Documents Tabular Key-Value Text GraphGeospatial
MongoDB Aggregation Framework

20. MongoDB
{ customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [ {
number : "1-212-777-1212",
type : "work"
},
{
number : "1-212-777-1213",
type : "cell"
}]
...
Expressive
Queries
Find anyone with phone # “1-212…”
Check if the person with number “555…” is on the “do not call” list
Geospatial
Find the best offer for the customer at geo coordinates of 42nd St.
and 6th Ave
Text Search Find all tweets that mention the firm within the last 2 days
Aggregation
Count and sort number of customers by city, compute min, max, and
average spend
Native Binary
JSON Support
Add an additional phone number to Mark Smith’s record without
rewriting the document
Update just 2 phone numbers out of 10
Sort on the modified date
JOIN
($lookup)
Query for all San Francisco residences, lookup their transactions,
and sum the amount by person
Graph Queries
($graphLookup)
Query for all people within 3 degrees of separation from Mark
Rich query functionality

21. Advanced data processing
pipeline for transformations
and analytics
Multiple stages
Similar to a unix pipe
Rich Expressions
Collection
db.orders.aggregate( [
$match stage {$match: { status: "A" } },
$group stage { $group: { _id:
"$cust_id",total: { $sum: "$amount" } } }
] )
{
cust_id: "A123",
amount: 500,
status: "A",
}
{
cust_id: "A123",
amount: 250,
status: "A",
}
{
cust_id: "B212",
amount: 200,
status: "A",
}
{
cust_id: "A123",
amount: 300,
status: "D",
}
Orders
{
cust_id: "A123",
amount: 500,
status: "A",
}
{
cust_id: "A123",
amount: 250,
status: "A",
}
{
cust_id: "B212",
amount: 200,
status: "A",
}
{
id: "A123",
total: 750
}
{
id: "B212",
total: 200
}
$match $group
Aggregations

22. $match
$group
$facet
$geoNear
$graphLookup
Pipeline Stages
Mathematical
– $add, $abs,
$substract,
$multiply, $divide,
$log, $log10,
$stdDevPop,
$stdDevSam,
$avg, $sqrt, $pow,
$sum, $zip,
$convert, etc.
Array
– $push, $reduce,
$reverseArray,
$addToSet,
$arrayElemAt,
$slice, etc.
Operators
$lookup
$project
$sort
$unwind
Conditionals
– $and, $or, $eq, $lt,
$lte, $gt, $gte,
$cmp, $cond,
$switch, $in, etc
Date
– $dateFromParts,
$dateToParts,
$dateFromString,
$dateToString,
$dayOfMonth,
$isoWeek,
$minute, $month,
$year, etc.
String
– $toUpper,
$toLower, $substr,
$strcasecmp,
$concat, $split,
etc.
Laterals
– $exp, $let, $literal,
$map, $type, etc
Aggregation Features

23. MongoDB Compass
Developer / Data Analyst Tool
Data management and manipulation
document view
table view
Visual schema analyzer
with query builder
export to language
Aggregation pipeline builder
A good place to start

24. 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

25. Range of Possibilities
Custom Code +
Charting
Libraries
ETL +
3rd Party BI
Tools
MongoDB BI
Connector +
3rd Party BI
Tools
MongoDB
Charts
MongoDB
Compass

26. analyst
data engineer
developer
analyst
analyst
Range of Possibilities
Custom Code +
Charting Libraries
ETL +
3rd Party BI Tools
MongoDB BI
Connector +
3rd Party BI Tools
MongoDB
Charts
MongoDB
Compass
developer
data scientist
developer
analyst
data scientist data engineer

27. Thank you!