Downloaded 12 times
![@slamdata @jdegoes
[This Slide Intentionally Left Blank]
NoSQL Analytics](https://image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-7-2048.jpg)
![@slamdata @jdegoes
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](https://image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-10-2048.jpg)
![@slamdata @jdegoes
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](https://image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-11-2048.jpg)
![@slamdata @jdegoes
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](https://image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-14-2048.jpg)
![@slamdata @jdegoes
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](https://image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-15-2048.jpg)
![@slamdata @jdegoes
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](https://image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-16-2048.jpg)
![@slamdata @jdegoes
[This Slide Intentionally Left Blank]
NoSQL Analytics](https://crownmelresort.com/image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-7-2048.jpg)
![@slamdata @jdegoes
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](https://crownmelresort.com/image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-10-2048.jpg)
![@slamdata @jdegoes
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](https://crownmelresort.com/image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-11-2048.jpg)
![@slamdata @jdegoes
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](https://crownmelresort.com/image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-14-2048.jpg)
![@slamdata @jdegoes
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](https://crownmelresort.com/image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-15-2048.jpg)
![@slamdata @jdegoes
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](https://crownmelresort.com/image.slidesharecdn.com/slamdatausecase-mongodbdayssv12-02-2015-151208215012-lva1-app6892/75/SlamData-How-MongoDB-Is-Powering-a-Revolution-in-Visual-Analytics-16-2048.jpg)
Uploaded byJohn De Goes
SlamData - How MongoDB Is Powering a Revolution in Visual Analytics
The document introduces Slamdata, an open-source platform for advanced analytics on NoSQL databases, particularly MongoDB. It highlights various characteristics and features of NoSQL analytics, including a generic data model, polymorphic queries, and structural pattern matching. The platform, which has gained traction with 3,000 users, enables users to analyze vast amounts of unstructured data efficiently, while providing extended SQL support.
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SlamData - How MongoDB Is Powering a Revolution in Visual Analytics
- 1. @slamdata @jdegoes John A.De Goes — CTO SlamData Inc. “SlamData is my new favorite thing — a game changer!” — SlamData User
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- 3. @slamdata @jdegoes Advanced DataScience in the Cloud
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- 7. @slamdata @jdegoes [This SlideIntentionally Left Blank] NoSQL Analytics
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- 10. @slamdata @jdegoes CHaracteristics 2 IsomorphicData 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
- 11. @slamdata @jdegoes CHaracteristics 3. Multidimensionality DataSQL² {"user_id": 928347234, "email": null, "events": [ {"impression":{ "ts": 912348934, "page": "index.html"}}]} SELECT user_id, [events[_] WHERE events[_].ts < 9347234 ...] AS events FROM visitors
- 12. @slamdata @jdegoes CHaracteristics 4. UnifiedSchema/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{*:}
- 13. @slamdata @jdegoes CHaracteristics 5. PolymorphicQueries 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
- 14. @slamdata @jdegoes CHaracteristics 6. Post-Relational DataSQL² {"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
- 15. @slamdata @jdegoes CHaracteristics 7. RuntimeType 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
- 16. @slamdata @jdegoes CHaracteristics 8. StructuralPattern 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
- 17. @slamdata @jdegoes ● OpenSource, 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
- 21. @slamdata @jdegoes Why MongoDB? 3.Mainstream NoSQL
- 22. @slamdata @jdegoes Why MongoDB? 4.Open Source
- 23. @slamdata @jdegoes SlamData OpenSource 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?
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