What is the past future tense of data?

1©MapR Technologies - Confidential

The Shape of Data to Comeit isn’t what we thought it was


Do you remember

the future?


Some things

turned out as

expected


Guys wearing Fedoras


What about “Big Data”?


Harvard University will have 200 x 106 volumes by 2040

Fremont Rider, 1944


To cope … only short papers should be published. … not more than 2500 characters counting “space,” punctuation marks, etc.

Gray and Ruston in IEEE Transactions on Electronic Computers, 1964


Remember the guy in

the Fedora?


He’s tweeting

about this right now


So what is the big data monorail and what is the

cool hat?


Data curationRigid SchemasEngineered Structure


Data curationRigid SchemasEngineered Structure

MONORAIL


Data as-you-find-it

Flexible schemasLate binding


Data as-you-find-it

Flexible schemasLate bindingCoo

l Hats


MONORAIL


Cool H

ats


Why is it different?How does it work?


More data is being produced more quickly

Data sizes are bigger than even a very large computer can hold

Cost to create and store continues to decrease

The Conventional Answer

BUSTED!


Analytics Scaling Laws

Analytics scaling is all about the 80-20 rule – Big gains for little initial effort– Rapidly diminishing returns

The key to net value is how costs scale– Old school – exponential scaling– Big data – linear scaling, low constant

Cost/performance has changed radically– IF you can use many commodity boxes


Which bytes first?


Net value optimum has a sharp peak well before maximum effort


But scaling laws are changing both slope and shape


More than just a little


They are changing a LOT!


Initially, linear cost scaling actually makes things worse

A tipping point is reached and things change radically …


Evolution of Data Storage

FunctionalityCompatibility

Scalability

Linux

POSIX

Over decades of progress,Unix-based systems have set the standard for compatibility and functionality




Scalability

Linux

POSIX

HadoopHadoop achieves much higher scalability by trading away essentially all of this compatibility




Scalability

Linux

POSIX

Hadoop

MapR enhances Apache Hadoop by restoring the compatibility while increasing scalability and performance


Introducing MapR

MapR offers thetechnology leading

distribution for Hadoop


The Industry-Leaders Choose MapR in the Cloud

Google chose MapR to provide Hadoop on Google

Compute Engine

Amazon EMR is the largest Hadoop provider in revenue

and # of clusters


MapR Supports Broad Set of Use Cases

Log analysis HBase

Customer targeting Social media analysis

Customer Revenue Analytics

ETL Offload

Advertising exchange analysis and optimization

Clickstream Analysis Quality profiling/field

failure analysis

Customer Sentiment

Network Analytics

Monitors and measures behavior of online shoppers

Fraud Detection Channel analytics

Customer Behavior Analysis Brand Monitoring

Customer targeting Viewer Behavioral analytics

Recommendation Engine Family tree connections

Intrusion detection & prevention Forensic analysis

Global threat analytics

Virus analysis

Patient care monitoring

Leading Retailer Recommendation Engine Fraud detection and Prevention

Leading Bank


MapR

MapRThe guys with the

cool hats


MapR’s Innovations


Seamless integration with existing applications

100% POSIX compliant

Industry standard APIs - NFS, ODBC, LDAP, REST

More 3rd party solutions

Proprietary connectors unnecessary

Language neutral


MapR: Lights Out Data Center Ready

Reliable Compute Dependable Storage

Automated stateful failover Automated re-replication Self-healing from HW

and SW failures Load balancing Rolling upgrades No lost jobs or data 99999’s of uptime

End-to-end checksums Strong consistency Business continuity with

snapshots and mirrors Recover to a point in time

with snapshots Mirror across sites for

disaster recovery


Why MapR Is Faster

• Eliminates storage contentionLockless Storage Service™

• Provides throughput at device speed Direct Block Device IO

• Exploits MapR-FS architecture to deliver performance using Hadoop Direct Shuffle

Hadoop Direct Shuffle

• Reduces network overhead using automatic compression

Client Side Compression

• Eliminates sporadic Java garbage collection overhead (system written in C)C vs Java


Security

MapR is pushing the envelope on Hadoop security

Integrates with Linux security (PAM)– Works with any user directory: Active Directory, LDAP, NIS, …

Strong wire-level authentication and encryption– Kerberos and non-Kerberos options

Fine-grained access control– Full POSIX permissions on files and directories– ACLs on tables, column families, columns, cells– ACLs on MapReduce jobs and queues– Administration ACLs on cluster and volumes


Bullet-proof NoSQL with Zero Administration

ReliabilityPerformance Easy Administration

Benefit Features

High Performance Over 1 Million ops/sec with 10 Node Cluster

Continuous Low Latency No I/O Storms, No Compactions

24x7 Applications Instant Recovery, Online Schema Modification, Snapshots, Mirroring

Zero Administration No Processes to Manage, Automated Splits, Self-tuning

High Scalability 1 Trillion Tables

Low TCO Files and Tables on One Platform


MapR M7 vs. CDH – Mixed Load (50-50)


MapR

MapRThe guys with the

cool solutions


MapR

MapRThe future of

the future


Thank You

What is the past future tense of data?

Technology

Transcript of What is the past future tense of data?