This document discusses building an IoT Kafka pipeline using MemSQL in under 5 minutes. It begins with an overview of IoT, Kafka, and operational data warehouses. It then discusses MemSQL and how it functions as an operational data warehouse by continuously loading and querying data in real-time. The document demonstrates launching a MemSQL cluster, creating schemas and pipelines to ingest, transform, persist and analyze IoT data from Kafka. It emphasizes MemSQL's ability to handle different data types and scales from IoT at high throughput with low latency.

1. Building an IoT Ka.a Pipeline in
Under 5 Minutes
@garyorenstein @memsql
MemSQL 1

2. Step 1
Launch Cluster
!MemSQL 2

3. Today’s Talk
IoT is big
Ka#a is popular
Real-&me pipelines need opera&onal data warehouses
How can we do this simply (in under 5 minutes)
MemSQL 3

4. About MemSQL
The Real-Time Data Warehouse
for the front lines of your business
MemSQL 4

5. Real Time and Opera.onal
Go Hand In Hand
• Live applica+ons and embedded analy+cs
• Con+nuous data processing (not batch)
• Real-+me AND historical data together
MemSQL 5

6. Gartner: An Opera,onal Data Warehouse
• Manages structured data
• Loads con0nuously for embedded analy0cs in applica0ons
• Supports real-&me data warehousing
• Func0ons as an opera0onal data store
MemSQL 6

7. Gartner: An Opera,onal Data Warehouse
• Manages structured data
• Loads con0nuously for embedded analy0cs in applica0ons
• Supports real-&me data warehousing
• Func0ons as an opera0onal data store
• Query op&miza&on plays a role (new)
• Many queries are repe00ve, mul0plying effects of
op0miza0on
MemSQL 7

8. Magic Quadrant for Data Management Solu4ons for Analy4cs
February 2017 Highlights
• Disrup(on accelera(ng
• Demand to address mul(ple data types
• Demand for distributed processing and storage
• Cloud gaining trac(on
Data may include interac/on and observa/onal data from
Internet of Things (IoT) sensors
MemSQL 8

9. MemSQL 9

10. MemSQL 10

11. MemSQL
#1 Opera)onal
Data Warehouse
Cri$cal Capabili$es for Data Warehouse and
Data Management Solu$ons for Analy$cs
MemSQL 11

12. A Real-Time IoT Pipeline
MemSQL 12

13. MemSQL 13

14. All Types of “Things”
Mul$ple data types
Massive scale with distributed systems
Always-on
Machine learning and predic$ve analy$cs
MemSQL 14

15. 4 Building Blocks of Enterprise IoT
MemSQL 15

16. MemSQL 16

17. Ingest
• From batch to con-nuous
• High throughput
• Massively parallel
• Exactly-once seman-cs
MemSQL 17

18. Transform
• In-line, in real-,me
• Alter, enrich, score machine learning
• Use exis,ng models with PMML
• Add new models
MemSQL 18

19. Persist
• Full durability and availability
• Fast via memory op4miza4on
• Both real-4me and historical data
• Scale with distributed systems
• Robust security
MemSQL 19

20. Analyze
• ANSI SQL
• Sophis.cated query execu.on
• Code compila.on for fast queries
• Easy business intelligence integra.ons
MemSQL 20

21. MemSQL 21

22. Demo
Building and IoT Ka.a Pipeline in
Under 5 Minutes
MemSQL 22

23. MemSQL 23

24. MemSQL 24

25. Launch Cluster
!MemSQL 25

26. Database Speed Test
MemSQL 26

27. Create Schema and Pipelines
MemSQL 27

28. Show Sample Queries
MemSQL 28

29. ⌚ Times Up!
MemSQL 29

30. MemSQL 30

31. MemSQL 31

32. Please visit our
booth!
Grab a shirt!
MemSQL | Transac/ons |
Analy/cs | Cloud
MemSQL 32

33. Thank You!
@garyorenstein @memsql
MemSQL 33