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Jason Stamper and Gary Orenstein

IN-MEMORY COMPUTING WEBCASTMARKET PREDICTIONS 2017

Market Predictions 2017: In-Memory Computing Jason Stamper, Analyst, 451 Research@jasonstamper

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451 Research is an information technology research & advisory company

Founded in 2000

210+ employees, including over 100 analysts

1,000+ clients: Technology & Service providers, corporate advisory, finance, professional services, and IT decision makers

10,000+ senior IT professionals in our research community

Over 52 million data points each quarter

4,500+ reports published each year covering 2,000+ innovative technology & service providers

Headquartered in New York City with offices in London, Boston, San Francisco, and Washington D.C.

451 Research and its sister company Uptime Institute comprise the two divisions of The 451 Group

Research & Data

Advisory Services

Events

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451 Research’s view of the ‘Total Data’ Model

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Growth?

2015 2016 2017 2018 2019 2020

$69,612$79,612

$90,777

$103,126

$116,796

$132,049

14%2015-20 CAGR

Source: 451 Research, Forecast: Total Data 2016 - Data Platforms and Analytics Market Sizing and Forecasts.

284 Vendors included in this analysis

9 Market segments included

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Defining the in-memory database“An in-memory database (IMDB; also main memory database system or MMDB or memory resident database) is a database management system that primarily relies on main memory for computer data storage. It is contrasted with database management systems that employ a disk storage mechanism.

Main memory databases are faster than disk-optimized databases because the disk access is slower than memory access, the internal optimization algorithms are simpler and execute fewer CPU instructions. Accessing data in memory eliminates seek time when querying the data, which provides faster and more predictable performance than disk.

Applications where response time is critical, such as those running telecommunications network equipment and mobile advertising networks, often use main-memory databases. IMDBs have gained a lot of traction, especially in the data analytics space, starting in the mid-2000s - mainly due to multi-core processors that can address large memory and due to less expensive RAM.”- Wikipedia

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In-memory systems come in several guises

Pure in-memory database

Disk-based/persistent database with an in-memory ‘option’ or column store

In-memory data grid or fabric

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What’s driving in-memory adoption?

• Increasing # of users & transactions• More data!• Growing # of writes• Insufficient capacity• Declining throughput• Performance inconsistencies• Cost of ETL processes

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How are different data platform and analytic technologies shaping up?

$120,000

$100,000

$80,000

$60,000

$40,000

$20,000

$0

2015 2016 2017 2018 2019 2020

Event/Stream ProcessingData ManagementDistributed Data Grid/CacheAnalytic DatabaseSearchPerformance ManagementReporting and AnalyticsHadoopOperational Databases

$140,000

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Some questions to ask Will my data fit in memory?! Is the platform optimized for analytics, transactions or both? Is the platform durable (ACID compliant)? What about

restores? What is the programming model – does it support SQL? Is there an open source or community edition? Does it support production requirements such as high

availability, cross data center replication, granular user permissions, and SSL?

Can I run it in the cloud, on-premises or both?

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Some potential solutions, and their pros and cons

• In-memory ‘options’ added to existing relational databases

• Pure in-memory databases

• Data streaming offerings

• Analytics as a service or database as a service – on-prem/hybrid/cloud

• In memory data grid/cache

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Some predictions for 2016/7• Multi-modal databases – that can handle both transactions (OLTP) and analytics

(OLAP) become the norm (with in-memory being a key enabler)

• In-memory databases continue to grow – both pure and ‘hybrid’

• E-commerce, ad-tech, gaming, financial services, high tech grow in-memory use particularly fast, but all businesses waking up to ‘latency sensitivity’

• From a slow start, the Internet of Things (IoT) gathers pace, thanks to both demand, and the ability to do something about it

• A new President of the US, and Andy Murray to become world #1

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Twitter.com/jasonstamper

Architecting with In-Memory

Gary Orenstein

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The nature of transactions has changed.

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Traditional Transactions Modern TransactionsBATCH• Exactly-Once• Governed• Structured• ERP/CRM Applications

REAL TIME• Duplicates• Optional auditing• Unstructured• Social and Sensor

feeds

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Modern TransactionsREAL TIME• Duplicates• Optional auditing• Unstructured• Social and Sensor

feeds

Traditional TransactionsBATCH• Exactly-Once• Governed• Structured• ERP/CRM Applications

Converged TransactionsREAL TIME• Exactly-Once• Governed• Any Structure• All Sources

A Real-Time Data Platform

Processing real-time and batch datato maximize traditional

and modern transactions

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Architecting A Real-Time Data Platform

Database Workloads

Data WarehouseWorkloads

Real-Time Streaming

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Architecting A Real-Time Data Platform

Orchestration / Containers

Cloud / On-Premises Platform

MessagingInputs Real-Time Applications

Business Intelligence Dashboards

Relational Key-Value Document Geospatial

Existing Data Stores

Database Workloads

Data WarehouseWorkloads

Real-Time Streaming

Hadoop Amazon S3MySQL

Transformation

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A Deeper Look at Two Industry Sectors

Customer 360

Supply Chain Logistics

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DellEMC Adopts MemSQL for Customer 360 Application

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The Need for “CALM”Customer Asset Lifecycle Management

Forenterprise salesWho needaccurate and timely customer informationCALM is areal-time applicationProvidingup to the moment customer 360 dashboards 

For enterprise salesWho need accurate and timely customer information

CALM is a real-time applicationProviding up to the moment customer 360o dashboards 

Install Base

Pricing

Device Config

Contacts

Contracts

Analytics Contracts

Component Data

Offers

Scorecard

Source: DellEMC

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Data Lake Architecture

D A T A P L A T F O R M

V M W A R E V C L O U D S U I T E

E X E C U T I O N

P R O C E S S GREENPLUM DBSPRING XD PIVOTAL HD

Gemfire

H A D O O P

ING

ES

TIO

ND

AT

A G

OV

ER

NA

NC

E

Cassandra PostgreSQL MemSQL

HDFS ON ISILONHADOOP ON SCALEIO

VCE VBLOCK/VxRACK | XTREMIO | DATA DOMAIN

A N A L Y T I C S T O O L B O X

Network WebSensor SupplierSocial Media MarketS T R U C T U R E DU N S T R U C T U R E D

CRM PLMERP

APPLICATIONS

Apache R

angerA

ttivioC

ollibraR

eal-T

ime

Mic

ro-B

atch

Bat

ch

Source: DellEMC

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BUSINESS BENEFITS

Deliver real-time customer updates to EMC Sales Department Scale customer analytics to increase sales productivity Drive real-time personalization for operational efficiency

TECHNICAL ACHIEVEMENT Execute joins across multiple distributed data sets

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The Pace of Supply Chain SuccessAmazon

30 minute post-order drone deliveries

FedEx

317 million packages shipped over Xmas with real-time tracking

Uber Freight

Matching shippers with trucks in real time

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| MemExAn IoT showcase application that powers supply chain

monitoring and management through predictive analytics.

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TECHNICAL BENEFITS Processes 2 million data points, based on 2,000 sensors across

1,000 warehouses Two million reads and writes per second Combines MemSQL, Apache Kafka, and Streamliner with

machine learning, IoT sensors, and predictive analytics Enables enterprises to predict throughput of supply warehouses

| MemEx

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Data Producers(simulating

sensor activity)

Raw Sensor 1 + Predictive Score 1

S1 P1

1

| MemEx

Real-TimeScoring

MemEx UI

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THANK YOU!

Follow our speakers on Twitter@jasonstamper @garyorenstein

Try MemSQL today at memsql.com/download