Slides from my presentation at MongoDB Days Silicon Valley. I discuss what SlamData is, the challenges it had to solve to build an analytics solution in the NoSQL space, and how specific features of MongoDB help power its advanced analytics functionality.

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John A. De Goes — CTO SlamData Inc.
“SlamData is my new favorite thing — a game changer!” — SlamData User

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Advanced Data Science in the Cloud

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SQL Analytics

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[This Slide Intentionally Left Blank]
NoSQL Analytics

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The
Eight
Deadly
Obstacles
to NoSQL Analytics

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CHaracteristics
1. Generic Data Model

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CHaracteristics
2 Isomorphic Data Model
Data SQL²
{
"userId": 8927524,
"profile": {
"name": "Mary Jane",
"age": 29,
"gender": "female"
},
"comments": [{
"id": "F2372BAC",
"text": "I concur.",
"replyTo": [9817361, "F8ACD164F"],
"time": "2015-02-03"
}, {
"id": "GH732AFC",
"replyTo": [9654726, "A44124F"],
"time": "2015-03-01"
}]
}
SELECT comments[*].replyTo[*]
FROM data

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CHaracteristics
3. Multidimensionality
Data SQL²
{"user_id": 928347234,
"email": null,
"events": [
{"impression":{
"ts": 912348934,
"page": "index.html"}}]}
SELECT user_id,
[events[_] WHERE
events[_].ts < 9347234 ...]
AS events
FROM visitors

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CHaracteristics
4. Unified Schema/Data
Data SQL²
{"user_id": "mary@bigco.com",
"band_plays":{
"Squirrel Nut Zippers": 56,
"Red Hot Tomatoes": 19,
"Big Bad Voodoo Daddy": 102}}
SELECT band_plays{*:} AS artistName,
SUM(band_plays{*}) AS votes
FROM music
GROUP BY band_plays{*:}

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CHaracteristics
5. Polymorphic Queries
Data SQL²
{"type": "click",
"link": "http://foo.com"
"timestamp": 123987172}
{"type": "impression",
"page": "index.html"
"timestamp": 92372}
SELECT COUNT(*) AS count, timestamp
FROM data
GROUP BY timestamp

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CHaracteristics
6. Post-Relational
Data SQL²
{"name": "John Doe",
"blog_posts": [
{"post_id": "89934"},
{"post_id": "92371"}
]}
SELECT authors.name, posts.title
FROM authors
JOIN posts
ON authors.blog_posts[*].post_id =
posts._id

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CHaracteristics
7. Runtime Type Id & ConverSION
Data SQL²
{"email": ["joe@gmail.com",
"joe@slamdata.com"]}
{"email": {
"home": "sarah@gmail.com",
"work": "sarah@slamdata.com"}}
SELECT
CASE TYPEOF email
-- old: email stored in 2nd el:
WHEN 'array' THEN email[1]
-- new format:
WHEN 'map' THEN email.work
ELSE email
END AS email
FROM users

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CHaracteristics
8. Structural Pattern Matching
Data SQL²
{"user_id": "toby@bing.com",
"events": [{"type": "purchase",
"timestamp": 12392342,
"order_id": "2ffa34aa"},
{"type": "click",
"timestamp": 92327123,
"link": "http://foo.com"}]}
SELECT
CASE user_events
WHEN […, e1, e2, …] THEN
e1.timestamp - e2.timestamp
END AS delta
FROM users

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● Open Source, Visual
analytics for NoSQL
● Search, Explore, Refine,
Analyze, and Visualize
● Embed interactive reports
on live MongoDB Data
● Extended SQL support that
runs 100% in-database

18. Market Response
@slamdata @jdegoes
● Launched version 1 just 13 months ago
● 3,000 Users & Growing Fast
● Many Fortune 500 companies
“Thank you for making such a wonderful product!
SlamData helps us analyze data from our local
database for MongoDB, which consist millions of
records, with thousands of new records added on a
daily basis.”
— SlamData User

19. Why MongoDB?
@slamdata @jdegoes
1. Modern Data Model

20. Why MongoDB?
@slamdata @jdegoes
2. Ad Hoc Queries

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Why MongoDB?
3. Mainstream NoSQL

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Why MongoDB?
4. Open Source

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SlamData Open Source Project
http://slamdata.com (website)
http://github.com/slamdata/slamdata (code)
Characteristics of NoSQL Analytics Systems
http://slamdata.com/whitepapers/characteristics-of-nosql-analytics-systems/ (whitepaper)
Quasar NoSQL Analytics Engine
http://quasar-analytics.org (website)
http://github.com/quasar-analytics/quasar (code)
Quasar BI Connector for Tableau, Qlik, Cognos, etc.
http://github.com/quasar-analytics/quasar-fdw (code)
Thank you for listening! Questions?

24. LIVE DEMO
@slamdata @jdegoes
Appendix A