Winning the On-Demand Economy with Spark and Predictive Analytics

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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.

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

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

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

Batch processing is the enterprise enemy

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

Because businesses need to
make every moment work for them

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

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

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

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

Move from a Real-Time Dashboard to
Predic7ve Applica7ons

In the on-demand economy real-time analytics is both a necessity and a competitive advantage. The next evolution in the on-demand economy is in predictive analytics fueled by live streams of data—in effect knowing what customers want before they do. This session will feature technical examples of real-time pipelines, machine learning, and custom dashboards as well as off-the-shelf dashboards with Tableau.

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Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics. To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/

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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

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