cloudy with a chance of on-prem - jim scott, strata hadoop 2017 sj

1© 2017 MapR Technologies 1© 2017 MapR Technologies

Cloudy with a Chance of On-PremJim Scott@kingmesal

2© 2017 MapR Technologies 2

Data is at the Core of Business Transformation

1980s Now

Early databases

Enterprise applications

The Internet Digital Transformation


Data Drives Market Disruption


Market Disruptors Use DataTo Impact Business Outcomes in Real-Time

Real-timeoperational impact

Higher customer engagement and lower acquisition cost

Eliminate cost,waste, time delay

Continuous insight analysis and application


Industry Leaders Are Investing in Disruptive Technology Now

Legacy technology investmentNext-Gen technology investment

2013 2014 2015 2016 2017 2018 2019 2020 (80,000)

(40,000)

-

40,000

80,000

120,000

$ (millions) Investment in Next-Gen vs. Legacy Technologies for Data

Total $ growth of IT market

Innovating and reducing costs at the same time90% of data is on

next-gen technology in just four years

Source: IDC, Gartner; Analysis & Estimates: MapRNext-gen consists of cloud, big data, software and hardware related expenses


MapRHadoop

Pressure to deal with Volume, variety,

velocity of new data

Customers

Pressure to deliver more to customer

base

Pressure to figure out the cloud and

containersOn

premAzureAWS

Your partner’ssystems

Yourlegacy

Business systems

Pressure to reduce cost of

legacy


Time to pause fora moment...


Cloud and Container Terminology• Data Gravity

– Data ingress and egress

• Cloud Neutrality– Freedom of choice– Prevent vendor lock-in

• Container Image and Repository

Other terms that I can explain ???


Anything Can Fail!

Prepare for the worst, hope for the best…


Customer 360

Website

User Profiles

Click Stream

Activity Stream

Website

User Profiles

Click Stream

Activity Stream

Amazon Google Cloud

On-Prem

User Profiles

Click Stream

Activity Stream

Salesforce


Hadoop & Spark

Cluster

Document DB

Classic Data Warehouse

NoSQL

Application Server

MessageMiddleware

Search ServerIts not the circles,

It’s the lines that are hard

Expensive to Stitch | Fragile | Limitations for Speed, Scale, Reliability

Cloud Processing Alone Does not Solve the Crisis of Complexity


Putting It In One Distribution Does Not Converge Anything!


Global Cloud Processing Requirements

Global single namespaceOmni-directional replication

Location awareness

Strong consistency by default

Continuous, Coordinated, Data flows


Source: 2016 Docker Survey

Cloud and Containers


Challenge with Containers

• Organizations using containers to improve resource utilization, increase developer efficiency, deploy microservices

• Most of containerized applications today are stateless– If destroyed and re-deployed, no data is lost and no services are

disrupted (ex: simple web front-ends)– Sharing state across many nodes is difficult and/or cost-prohibitive

• Container environments don’t provide the reliable storage needed to support stateful applications


New Product

MapR Announces the Converged Data Platform for Docker

Supports containerization of existing and new applications by providing containers with

persistent data access from anywhere


MapR Expands and Simplifies Container Usage

Flexible Hybrid/Multi Cloud Infrastructure

Shared Data Services

Per-tenant Processing & Microservices

Converged App Containers

Enterprise Apps

Data Center


MapR Data Services for Containers

• Pre-built, certified container image for connecting to MapR services

• Secure authentication at container level, secure connection

• High performance

• Extensible support for application layers

• Available in Docker Hub, Dockerfile for customizability

Space for Customer Application

MapR Persistent Application Client Container(PACC)

MapR POSIX Client for Containers

MapR Converged Client for

Containers


MapR Lets Enterprises Run Existing Apps in Containers

MapR Cluster

Advantages• Stateful containers that can restart/move

to any node or data center and access data• Shared data repository for multiple apps,

operations & analytics• Decoupled scaling of compute & storage

Use Cases• RDBMS Persistence (MySQL, Vertica, SAP)• Container Image Store (Docker Registry)• Rapid-ingest Logging (Impressions, Clicks)• Source Control Persistence (Git, Mercurial)• Image Store (Thumbnails, High Res)

Container Environment (Mesos, Kubernetes,Docker Swarm)

MariaDB Volume Logs Volume

Reliable, Persistent, High-throughput Storage

Solution: MapR PACC allows existing applications to store state in MapR Converged Data Platform, ensuring resilience and availability across application and infrastructure failures.


While Allowing Enterprises to Adopt Microservices

Advantages• Ubiquitous, reliable, high performance data services,

no per-app data containers

Use Cases• Stateful microservices• Inter-microservice Communication• Per-microservice database

MapR Cluster

Container Environment (Mesos, Kubernetes)

MariaDB Volume

Reliable, Persistent, High-throughput Storage

Solution: MapR PACC establishes secure connection to MapR Converged Data platform, which provides all three types of persistence.


Container Summary• Huge Opportunity – Organizations are

rapidly moving to containerize

• Huge Gap for stateful application support

• MapR provides a high value, highly differentiated solution

• Our tactical solution is part of a much larger strategic initiative


Converged Data Platform

Analytical Applications

Operational Applications

Converged ApplicationsComplete Access to Real-time and

Historical Data in One Platform

Developers Creating Database and Event Based

Applications

(Bottom Line Initiatives) (Top Line Initiatives)

Analysts Creating BI Reports and KPIs on Data

Warehouse

Historical Data Current Data


Web-Scale StorageMapR-FS MapR-DB

Real Time Unified Security Multi-tenancy Disaster Recovery Global NamespaceHigh Availability

MapR StreamsEvent StreamingDatabase

MapR Platform Services: Open API ArchitectureAssures interoperability, avoids lock-in

HDFS API

POSIXNFS

SQL,HBase

APIJSONAPI

KafkaAPI


Converged Application Benefits

• Consumers scale horizontally with partitions• 1:1 mapping between consumer and partition• Enables predictable scaling as production needs grow

• Data can be seamlessly replicated to another cluster• Enables HA with zero code changes

• Data is indexed dynamically according to receivers, senders• Scales beyond the capabilities of Kafka

• Snapshots can be taken to capture state• Enables faster testing and deployment of applications


Reduce costs to improve efficiency

Extend capabilities

to grow revenues

Innovate for disruptiveadvantage

Put the Power of MapR to Work for Your BusinessWherever you are on your big data journey


Converge, Transform and Grow with MapR

Innovatefor Growth

RealizeCost Savings


@kingmesal

[email protected]

Engage with us!

kingmesal

cloudy with a chance of on-prem - jim scott, strata hadoop 2017 sj

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