The slides from this presentation were used in a live webinar that covered a variety of MongoDB Data Management topics including: How quickly can organizations recover from accidental data loss or ransomware, how to ensure compliance and security of PII when mirroring across different environments, specific architectural considerations to consider when running in a hybrid or pure cloud environment.

1. Optimizing Data Management for
MongoDB
October 11, 2017

2. My Background

3. Why Bother With Backup and Test Data Mgmt?
The average cost of a data loss
incident is $900,000
90% of enterprises delay applications
because of a lack of test data
Source: EMC, Imanis Data

4. Data Replicas Don’t Prevent ALL Data Loss
Human errors: dropping a
collection
Application corruption: incorrect
updates to a collection
Replicas go out of synch
Primary
Secondary Secondary

5. Current MongoDB Backup Options
mongodump
Filesystem/storage
snapshots
Ops Manager

6. MongoDB Backup/Recovery Options:
mongodump
Very resource
intensive
PROBLEM
Not feasible
for granular
recovery
PROBLEMPROBLEM
Not
incremental-
forever
PROBLEM

7. MongoDB Backup/Recovery Options:
filesystem/storage snapshots
Requires
quiescing of
MongoDB
instance for
consistent
snapshots
PROBLEM
Requires
periodic full
backups to
ensure faster
recovery
PROBLEM
Cannot do
point in time
recovery or
back up
specific
collections
PROBLEM

8. MongoDB Backup/Recovery Options: Ops
Manager
Requires
agents on
production –
scaling
increases
overhead
PROBLEM PROBLEM
Storage
optimization
is dependent
on external
media. Not
data-aware
PROBLEM
Extremely
difficult to
restore to a
different
topology

9. Test Data Mgmt Eliminates Costly Application
Delays
Application
teams wait for
production data
A company delayed 3-4 releases a year
Cost the company $450K per year
of companies delay
application releases90%
BUSINESS IMPACT
CUSTOMER EXAMPLE
PRIMARY CHALLENGE

10. Challenges With Typical Test Data
Management
Change
Request - 1
week
Provision
Production
Data - 1
week
Create Test
DB and
Mask Data
- 1 week
Create
Samples of
Production
Data – 2
days
Push
Production
Data To
Test –
Hours
Repeat
Process –
3-4 weeks

11. The Evolution of Data Management
THE NEXT
25 YEARS
THE
TRADITIONAL
WORLD
Data ManagementData Platforms

12. Imanis Data in Production
Test
Cluster
Research
Cluster
Imanis Data GUI
Hadoop/Spark
Cluster
Cassandra
Cluster
Vertica
Cluster
Couchbase
Cluster
Imanis Data
Smart Storage
Cluster
MongoDB
Cluster

13. The Imanis Data Architecture
• Deep de-duplication and compression with app-aware architecture
• Incremental-forever backup architecture
• High availability via erasure coding in distributed cluster architecture
Smart Storage Optimizer

14. The Imanis Data Architecture
Native querying and analytics via
active compute layer
Unbounded scale with a
Hadoop-native architecture
Smart Storage Optimizer
Active Compute Services Distributed File System

15. The Imanis Data Architecture
• Google-like catalog shortens
data recovery time
• Automatic schema
generation for mirroring and
backups
• Granular recovery at an
object level
• Recovery to multiple
topologies
• Native integration with
LDAP and Kerberos for
authentication
• Role-based access control
defines specific privileges
• Stateless, consistent,
irreversible, and one-way
masks for PII data
Smart Storage Optimizer
Active Compute Services Distributed File System
Metadata Catalog Data Orchestration ServicesSecurity Services

16. Smart Storage Optimizer
The Imanis Data Architecture
GUI CLI API
Active Compute Services Distributed File System
• ‘Single pane of glass’ for multiple use cases and data platforms
• Agentless architecture minimizes management overhead
• GUI, CLI, REST-based Talena API options
Metadata Catalog Data Orchestration ServicesSecurity Services

17. Machine Intelligence: ThreatSense
• Proactively identify
anomalous data loss and
ransomware to reduce
downtime
• Collects nearly 50
attributes to set baseline
• Enables user input to
optimize machine
learning

18. Q&A