Nikita Shamgunov, CTO and Co-founder of MemSQL

Driving the On-Demand Economy with Predictive Analytics

In-Memory Computing

Scale-out

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

In-Memory and Scale-out in Action

▪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 behindbig data and real-time transformation

Moving to Real-Time Data

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

What is MemSQL?

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

MemSQL Pipelines

Exactly Once

Automatically Distributed

Language Agnostic

CREATE PIPELINE ASLOAD 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 .shTransform

Live Demoelection.memsql.com

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 Kafka2. Create Pipeline and point at Kafka

a. What are the schemas of the table?b. Sentiment analysisc. What is the connective tissue between Kafka and

applying sentiment analysis?

Run a set of commands1. Creating tables2. Creating pipelines

a. We will see data flowing into MemSQL3. Build a web app similar to election.memsql.com

a. Quicker alternative: Tableau

Launch Tableau1. Already streaming data from public Kafka into MemSQL as

seen earlier2. Connecting Tableau

a. Similar dashboard to election.memsql.comb. Display sentiment analysis time series

Thank You