CTO View: Driving the On-Demand Economy with Predictive Analytics

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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.

Technology

Nikita Shamgunov, CTO and Co-founder of MemSQL
Driving the On-Demand Economy
with Predictive Analytics

Imagine scaling a
database on industry
standard hardware.
Need 2x the
performance?
Add 2x the nodes.

Trying to build
scale-out for
a traditional
product

▪ Every piece of technology is scalable
▪ Analyzing data from hundreds of thousands of
machines
▪ Delivering immense value in real-time
• Real-time code deployment
• Detecting anomalies
• A/B testing results
▪ Fundamentally making the business faster by providing
data at your fingertips
An Insider’s View

▪ An enterprise solution that could scale
▪ Work well with existing tools and infrastructure
▪ Database is only as successful as the ability to
quickly and easily build applications on top of it
An Eye to Adoption

Embrace the tools and projects behind
big data and real-time transformation

Keeping Pace
On-demand economy Real-Time Data Predictive Analytics

In our world, fresh accurate analytics means live data
▪ We’ll build a pipeline from scratch
We get predictive analytics via real-time scoring and
modeling
▪ I’ll show an example and we’ll see more across the talks
Visualizations make the data consumable
▪ Off-the-shelf options like Tableau, as well as custom
Today in My Talk

Scalable SQL database
Familiar syntax
Really really fast

MemSQL Confidential18
Product or Services Scores
for Operational Data
Warehouse
Critical Capabilities for Data
Warehouse and Data
Management Solutions for
Analytics
Gartner, July 2016

Exactly Once
Automatically Distributed
Language Agnostic

CREATE PIPELINE AS
LOAD DATA KAFKA "hostname:9092/tweets"
INTO TABLE tweets

Master Aggregator
Leaf Node Leaf Node Leaf Node Leaf Node
Child Aggregators

Master Aggregator
Leaf Node Leaf Node Leaf Node Leaf Node
Child Aggregators
.sh .sh .sh .sh
Transform

Real-time Twitter
Feed
Public Kafka
Load into table "tweets"
Load into table "tweet-sentiment"
MemSQL Pipelines
1. Extract 2. Transform 3. Load
Custom
dashboard
Tableau
dashboard

1. Assume data is already published somewhere in Kafka
2. Create Pipeline and point at Kafka
a. What are the schemas of the table?
b. Sentiment analysis
c. What is the connective tissue between Kafka and applying
sentiment analysis?

Run a set of commands
1. Creating tables
2. Creating pipelines
a. We will see data flowing into MemSQL
3. Build a web app similar to election.memsql.com
a. Quicker alternative: Tableau

Launch Tableau
1. Already streaming data from public Kafka into MemSQL as seen
earlier
2. Connecting Tableau
a. Similar dashboard to election.memsql.com
b. Display sentiment analysis time series

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This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

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Microsoft's Threat Matrix for Kubernetes helps organizations understand the attack surface a Kubernetes deployment introduces to their environments. This ensures that adequate detections and mitigations are in place. By covering over 40 different attacker techniques, defenders can learn about Kubernetes-specific mitigations and controls to deploy to their environments. In this session, we will explore the MS-TA9013 Host Path Mount technique, which is commonly used by attackers to perform privilege escalation in a Kubernetes cluster. Attendees will learn how attackers and defenders can: * Escape the container's host volume mount to gain persistence on an underlying node * Move laterally from the underlying node into the customer's cloud environment * Analyze Kubernetes audit logs to detect pods deployed with a hostPath mount * Deploy an admission controller that prevents new pods from using a hostPath mount

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With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

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CTO View: Driving the On-Demand Economy with Predictive Analytics

1. Nikita Shamgunov, CTO and Co-founder of MemSQL Driving the On-Demand Economy with Predictive Analytics

3. In-Memory Computing

4. Scale-out

5. Imagine scaling a database on industry standard hardware. Need 2x the performance? Add 2x the nodes.

6. Trying to build scale-out for a traditional product

8. In-Memory and Scale-out in Action

9. ▪ Every piece of technology is scalable ▪ Analyzing data from hundreds of thousands of machines ▪ Delivering immense value in real-time • Real-time code deployment • Detecting anomalies • A/B testing results ▪ Fundamentally making the business faster by providing data at your fingertips An Insider’s View

10.

11. ▪ An enterprise solution that could scale ▪ Work well with existing tools and infrastructure ▪ Database is only as successful as the ability to quickly and easily build applications on top of it An Eye to Adoption

12. Embrace the tools and projects behind big data and real-time transformation

13. Moving to Real- Time Data

14. Keeping Pace On-demand economy Real-Time Data Predictive Analytics

15. In our world, fresh accurate analytics means live data ▪ We’ll build a pipeline from scratch We get predictive analytics via real-time scoring and modeling ▪ I’ll show an example and we’ll see more across the talks Visualizations make the data consumable ▪ Off-the-shelf options like Tableau, as well as custom Today in My Talk

16. What is MemSQL?

17. Scalable SQL database Familiar syntax Really really fast

18. MemSQL Confidential18 Product or Services Scores for Operational Data Warehouse Critical Capabilities for Data Warehouse and Data Management Solutions for Analytics Gartner, July 2016

19. MemSQL Pipelines

20. Exactly Once Automatically Distributed Language Agnostic

21. CREATE PIPELINE AS LOAD DATA KAFKA "hostname:9092/tweets" INTO TABLE tweets

22. Master Aggregator Leaf Node Leaf Node Leaf Node Leaf Node Child Aggregators

23. Master Aggregator Leaf Node Leaf Node Leaf Node Leaf Node Child Aggregators .sh .sh .sh .sh Transform

24. Live Demo election.memsql.com

25.

26.

27. Real-time Twitter Feed Public Kafka Load into table "tweets" Load into table "tweet-sentiment" MemSQL Pipelines 1. Extract 2. Transform 3. Load Custom dashboard Tableau dashboard

28. 1. Assume data is already published somewhere in Kafka 2. Create Pipeline and point at Kafka a. What are the schemas of the table? b. Sentiment analysis c. What is the connective tissue between Kafka and applying sentiment analysis?

29. Run a set of commands 1. Creating tables 2. Creating pipelines a. We will see data flowing into MemSQL 3. Build a web app similar to election.memsql.com a. Quicker alternative: Tableau

30. Launch Tableau 1. Already streaming data from public Kafka into MemSQL as seen earlier 2. Connecting Tableau a. Similar dashboard to election.memsql.com b. Display sentiment analysis time series

31. Thank You

CTO View: Driving the On-Demand Economy with Predictive Analytics

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