MapR-DB is an enterprise-grade, high performance, in-Hadoop NoSQL (“Not Only SQL”) database management system. It is used to add real-time, operational analytics capabilities to Hadoop and now natively support JSON.

1. © 2015 MapR Technologies 1© 2015 MapR Technologies
MapR-DB: New Options For Creating Breakthrough
Next Gen Apps with NoSQL And Hadoop

2. © 2015 MapR Technologies 2
NoSQL Was Designed For Big Data
• RDBMSs has been the default
choice for applications
– But face cost/time challenges for
rapidly growing, varying data sets
• NoSQL was designed for big data
– E.g., User transaction data, sensor
data, IoT data, time series data, etc.
RDBMS
NoSQL

3. © 2015 MapR Technologies 3
Known NoSQL Database Challenges Today
With Other NoSQL Databases
• Data loss
• Data inconsistency
• Long maintenance downtime (e.g.,
compactions, anti-entropy)
• Coarse grained access controls
X
• Cluster/silo sprawl
– Maintenance pains
– Complexity, more error prone
• Constant data movement between
database and analytics cluster
– Excessive bandwidth utilization
– Delays in accessing data
• Modeling of complex data
– Longer app development cycles
– Higher chance of coding errors
• Multiple databases for multiple kinds
of applications

4. © 2015 MapR Technologies 4
Requirements to Resolve Today’s Challenges
• Tighter Hadoop integration
– Reduce cluster sprawl
– Reduce data movement
– Enable real-time analytics on live data
– Lower administrative overhead
• Flexible JSON data model
• Automatic optimizations
– Less maintenance downtime
– Consistent high performance
• Fine grained access controls
– More than simply table/document level
• Globally consistent deployment capability
Hadoop NoSQL
Data Platform

5. © 2015 MapR Technologies 5
MapR-DB Architectural Principles
Dramatically Simpler, High-Performance at Global Scale
• Self-healing from HW and SW failures
– Replicated state and data for instant recovery
– Automated re-replication of data
• High performance and low latency
– Integrated system with fewer software layers
– Single hop to data
– No compactions, low i/o amplification (patented secret sauce)
• Minimal administration
– Single namespace for files and tables (and streams going forward)
– Built-in data management & protection
– Automatic splits and merges as data grows and shrinks
• Global low-latency replication for disaster recovery

6. © 2015 MapR Technologies 6
Built-into Hadoop = Real-time
Hadoop NoSQL
Churn
Analysis
Offers
Fraud
Detection
Customer
Profiles
Log files IoT Data
Batch Copies
Analytical Operational
MapR Distribution
Churn
Analysis
Offers
Fraud
Detection
Customer
Profiles
Log files IoT Data
Analytical + Operational
Analytics as it happens, no cross-cluster copying
Hadoop MapR-DB
Non-MapR:
• Batch-only
• Cluster sprawl
With MapR:
• Real-time data access
• Multi-use-case platform
Revenue
Optimization
Predictive
Analytics
Sentiment
analysis
Click
streams
Call logs
Social
media

7. © 2015 MapR Technologies 7
Real-Time Integration with Other Systems
MapR-DB replication engine is extensible for
integration with any external systems
MapR-DB
Streaming
Real-Time
Reliable Transport
Storm
Elasticsearch
Remote MapR-DB Tables
Future

8. © 2015 MapR Technologies 8
Designed For Global deployments
Multi-master (aka, active/active) replication
Active Read/Write
End Users
• Faster data access – minimize network
latency on global data with local clusters
• Reduced risk of data loss – real-time,
bi-directional replication for synchronized
data across active clusters
• Application failover – upon any cluster
failure, applications continue via
redirection to another cluster

9. © 2015 MapR Technologies 9
Real-Time Analytics With Hadoop
Distributed clusters close to the end
users, with real-time analytics at central
cluster
MapR-DB cluster
(London)
MapR-DB cluster
(New York)
MapR-DB cluster
(Singapore)
MapR-DB/Hadoop
cluster
Hadoop analytics
Operational and analytical workloads
combined in a single cluster in in a
single datacenter
Operationally efficient,
consolidated MapR cluster
Database
operations
Hadoop
analytics
Active Read/Write
End Users

10. © 2015 MapR Technologies 10
Granular Security
Use Access Control Expressions (ACEs) to set granular
permissions.
Example: user:mary | (group:admins & group:VP) &
user:!bob

11. © 2015 MapR Technologies 11
Open Source OJAI API for JSON-Based Applications on
Hadoop
Open JSON Application Interface (OJAI)
Databases Other Systems
MapR-DB
MapR-Client
{JSON}
File Systems

12. © 2015 MapR Technologies 12
Single Cluster Data Lake Capabilities
Paste your MapR distribution for
Hadoop diagram from Part A,
(slide 2) here
MapR-DB MapR-FS
MapR Data Platform
Distribution including
Apache Hadoop
MapR-DB: relational,
time series,
structured data
MapR-FS: emails,
blogs, tweets, log
files, unstructured
data
Agile, self-
service data
exploration
ETL into operational
reporting formats
(e.g., Parquet)
Multi-tenancy:
job/data placement
control, volumes
Access controls:
file, table, column,
column family, doc,
sub-doc levels
Sources
RELATIONAL,
SAAS,
MAINFRAME
DOCUMENTS,
EMAILS
LOG FILES,
CLICKSTREAM
SENSORS
BLOGS,
TWEETS,
LINK DATA
DATA
WAREHOUSES,
DATA MARTS
Auditing:
compliance, analyze
user accesses
Snapshots:
track data lineage
and history
Table Replication:
global multi-master,
business continuity

13. © 2015 MapR Technologies 13
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