Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts. Watch this webinar to learn how to navigate the most common obstacles of big data analytics. Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics. In this webinar, we will show you how to: • Find the balance between infrastructure and business use cases • Overcome challenges of using multiple tools that address big data analytics • Leverage all your resources (data scientists, IT and analysts) most effectively

1. © 2014 Datameer, Inc. All rights reserved.
How to Avoid Pitfalls in
Big Data Analytics"

2. View Recording ""
You can view the recording of this webinar
at:
http://info.datameer.com/Online-Slideshare-
How-to-Avoid-Pitfalls-in-Big-Data-
Analytics-OnDemand.html

3. © 2013 Datameer, Inc. All rights reserved.
Matt Schumpert @datameer
Senior Director, Solutions Engineering
Matt has been working in the enterprise
infrastructure software space for over 14 years in
various capacities, including sales engineering,
strategic alliances and consulting.
Matt currently runs the pre-sales engineering team at
Datameer, supporting all technical aspects of
customer engagement from initial contact through
roll-out of customers into production.
Matt holds a BS in Computer Science from the
University of Virginia.
#datameer @datameer
About Our Speaker"

4. © 2013 Datameer, Inc. All rights reserved.
Dale Kim @MapR
Director, Product Marketing
Dale Kim is the Director of Product Marketing at
MapR. His background includes a variety of technical
and management roles at information technology
companies. While his experience includes work with
relational databases, much of his career pertains to
non-relational data in the areas of search, content
management, and NoSQL.
Dale holds an MBA from Santa Clara University, and a
BA in Computer Science from the University of
California, Berkeley.
#mapr @mapr
About Our Speaker"

5. Agenda"
▪ Quick introduction to Hadoop
▪ Overview of analytics on Hadoop
▪ Quick tips on big data analytics
▪ Our 5 big data pitfalls to avoid

6. Quick Introduction to Apache Hadoop"
▪ What is Apache Hadoop
– Software framework for reliable, scalable,
distributed computing
– “Divide-and-conquer” approach to
processing large data sets
▪ Hadoop does analytics
– Hadoop is the platform of choice for big data
– If you have big data, then you are analyzing
big data

7. Types of Analytics for Hadoop"
▪ Descriptive – what happened, and why
– The “why” is also known as “diagnostic”
– Data mining, management reporting

8. Types of Analytics for Hadoop [2]"
▪ Predictive – what will happen
– Cross-sell/up-sell (recommendations), fraud/
anomaly detection
▪ Prescriptive – what should I do
– Preventative maintenance,
smart meter analysis
Better with
more data

9. Common Data Types for Hadoop"
▪ Clickstream/user behavior history
▪ Sensor/machine/event logs
▪ Social media profiles & communication
▪ Data warehouse data (structured, SoR)
▪ Long-tail/archive data

10. The Foundation for an Analytics Platform"
▪ Performance
– Make sure you get results in a timely manner
▪ Scalability
– Let your platform grow as your data grows
▪ Reliability
– Keep your users productive
▪ Ease-of-use
– Give users an end-to-end, self-service
platform that delivers fast time-to-insight

11. Quick Tips on Big Data Analytics"
▪ Minimize copying large data volumes across the wire
▪ Plan for production issues (system responsiveness,
performance, high availability, disaster recovery, audits)
▪ Start by looking for ways Hadoop can supplement, not
supplant your existing system
▪ Be wary of reusing a classic app. virtualization stack
▪ Choose "built-on”, not “connects-to" Hadoop vendors
▪ Be wary of lofty claims around machine learning (e.g.,
IBM Watson)
▪ As Hadoop in an emerging technology, pick innovative
rather than legacy vendors

12. Common Pitfalls in Big Data Implementations"
1. Incomplete plan for scaling up
2. Not architecting for maximum uptime
3. Over-use of immature technologies
4. Excessive/insufficient data governance
5. Wasting data scientists’ time with data
preparation

13. Incomplete Plan for Scaling Up"
RDBMS
VS.
• Monolithic, RDBMS-based system
• Vertical scaling
• Large upgrade expenditure
• Commodity server-based Hadoop system
• Horizontal scaling
• Incremental expenditure

14. Incomplete Plan for Scaling Up [2]"
▪ Relatively easy to extrapolate existing data
load to future
▪ But, must also factor in:
– Larger time windows of data
• Expanding beyond 3-month time window broke system
• Now can store 18-months, results in more accurate
analytics
– More data sources
• Typically, new sources that could not be added before
– More use cases and users
• More divisions want to join system

15. Not Architecting for Maximum Uptime"
Separate user communities and data are isolated, but…
greater infrastructure complexity and risk

16. Not Architecting for Maximum Uptime [2]"
▪ Separate physical clusters for separate
“tenants” appears easy
▪ Multiple clusters lead to:
– Infrastructural complexity, more risk of error
– More points of failure
▪ Instead, leverage software components to
help logically separate users/data

17. Not Architecting for Maximum Uptime [3]"
▪ Global Storage Solutions Company
▪ Deployed file-serving HBase application
▪ Introduce ad-hoc analytics in same cluster
▪ No resource fencing, poor workload mgmt.
▪ Result: Significant downtime

18. Over-Use of Hadoop Ecosystem Technologies"
▪ Research group at a Fortune 500
▪ Anxious to deliver the first NoSQL project
▪ Built an overly complex data model
▪ Deployed HBase with no support/expertise
▪ Lack of integration/analytics = limited success

19. Excessive / Insufficient Data Governance"
▪ Under-Governed
– Users deleting “unused data” after a project
– Incorrectly interpreted as data loss by others
– Result: panic
▪ Over-Governed
– Fortune 500 deployed Hadoop as a shared IT service
– Needed chargebacks based on data volume
– Setup a “walled garden” for each project
– Result: no sharing, no collaboration, fewer insights

20. Wasting Data Scientists’ Time with Data Prep"
▪ DS groups are often the first tenants on Hadoop
▪ Traditional DS tools are weak in data prep
▪ Hadoop tools like Pig unfamiliar to DS users
▪ Result: 80% of time spent on data wrangling

21. Demo …"

22. Datameer: Purpose-Built for Hadoop"

23. The #1 Data Discovery Platform"
Source: GigaOM, 03/14

24. MapR Distribution for Hadoop"
BIG
DATA
BEST
PRODUCT
BUSINESS
IMPACT
Hadoop
Top
Ranked
Production
Success
Look for our follow-up blog post at:
www.mapr.com/blog

25. The Power of the Open Source Community"Management
MapR Data Platform
APACHE HADOOP AND OSS ECOSYSTEM
Security
YARN
Pig
Cascading
Spark
Batch
Spark
Streaming
Storm*
Streaming
HBase
Solr
NoSQL &
Search
Juju
Provisioning
&
coordination
Savannah*
Mahout
MLLib
ML, Graph
GraphX
MapReduce
v1 & v2
EXECUTION ENGINES DATA GOVERNANCE AND OPERATIONS
Workflow
& Data
Governance
Tez*
Accumulo*
Hive
Impala
Shark
Drill*
SQL
Sentry* Oozie ZooKeeperSqoop
Knox* WhirrFalcon*Flume
Data
Integration
& Access
HttpFS
Hue
*
Cer&fica&on/support
planned
for
2014

26. Projects to Follow"
▪ Apache Spark – fast, large-scale data
processing engine
– MapR is only distribution for Hadoop to
support the entire Spark stack
▪ Apache Drill – fast query execution engine
– MapR-initiated open source project
– Supports instant
querying and broad
data format support

28. For more information"
" http://www.datameer.com
" http://www.mapr.com
" @datameer
" @MapR
" mschumpert@datameer.com
" dalekim@mapr.com
Learn more
Contact
#datameer @datameer