Today’s on-demand economy drives companies to provide fast load times, personalization, and instantaneous service for hungry end-users across all types of applications. Yet most still use dated, legacy systems to process and analyze data. In this session, Ankur Goyal, VP of Engineering at MemSQL will showcase implementing a one-click Lambda Architecture with Apache Spark, Apache Kafka and an operational database, resulting in lightning fast analytics on large, changing datasets.

1. Winning the On-Demand Economy
with Spark and Predic9ve Analy9cs
Ankur Goyal, VP Engineering, MemSQL
Gartner BI March 20161
1
Deckset Plain Jane Black

2. We live in an on-demand economy

3. Consumers are condi.oned to instant
services, like Uber, Stripe, and Airbnb

4. Where does that leave enterprises?

5. Racing to meet internal and external
expecta1ons for speed and
personaliza,on

6. Batch processing is the enterprise enemy

7. Enterprises must move from overnight to
Real-&me, intra-day opera&ons

8. Because businesses need to
make every moment work for them

9. The key to harnessing data in real 1me?
A real-(me data pipeline with Apache Ka3a, Apache Spark,
and an Opera(onal Database such as MemSQL

10. Massive Ingest and Concurrent Analy5cs
• Instant accuracy to the latest repin
• Build real-5me analy5c applica5ons
• 1 GB/sec totaling 72 TB/day

11. Using Real-Time for Personaliza3on
• Reach overlap and ad op/miza/on
• Over 60,000 queries per second
• Millisecond response /mes

12. MemSQL PowerStream
Predic'ng the global health of wind turbines
Dataset: 200,000 wind turbines, 20,000 wind farms
<$20,000 annual cost for AWS hardware

13. Let's build PowerStream

14. Move from a Real-Time Dashboard to
Predic7ve Applica7ons

15. Ques%ons?
Stop by MemSQL Booth #119