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© 2013 Terracotta Inc. | Internal Use Only
In-Memory &
Hadoop:
Real-time
Big Data Intelligence
© 2013 Terracotta Inc. 2
Your speaker
Manish Devgan
Director of Product
Management
Terracotta
© 2013 Terracotta Inc. 3
What we’ll cover in this webcast
• What’s Hadoop? (quick intro)
• Hadoop’s weaknesses
• Emerging best practices for combining
Hadoop and in-memory data management
• Real-time intelligence example
• Getting started with in-memory and Hadoop
• Q & A
© 2013 Terracotta Inc. 4
4© 2013 Terracotta Inc. | Internal Use Only
What is Hadoop?
© 2013 Terracotta Inc. 5
What is ?
• Hadoop is open-source software data management framework
used to draw insights from data
Components Benefits
HDFS*: Scalable &
distributed Storage
• Data distributed across cluster
nodes
• Name node keeps track of location
MapReduce: Parallel
Processing of data
• Splits a task for processing based
on data locality and then
assembles results
• Comprises of Map() procedure for
filtering & sorting and Reduce()
procedure for summarizing
Scalable
• Efficiently store and process large
data sets
Reliable
• Get redundant storage, with failover
across cluster
Rich & Flexible
• Complimentary set of tools &
frameworks
• Store data in any format
Economical
• Deploy on commodity hardware
*Hadoop Distributed File System
© 2013 Terracotta Inc. 6
What is ?
• With Hadoop, you can ask interesting questions about your data
and get answers economically
Questions Hadoop can help answer
How can I target promotions to my customers for better
sales?
How risky are each of my customers?
Which advertisement should I show to optimize return?
How relevant is a result for a given search?
When will my machinery likely have a malfunction?
© 2013 Terracotta Inc. 7
7© 2013 Terracotta Inc. | Internal Use Only
Hadoop’s Weaknesses
© 2013 Terracotta Inc. 8
Hadoop’s Weaknesses
• No support for real-time insights
• No support to facilitate interactive and exploratory data analysis
• Challenging framework for computation beyond Map Reduce
• Lacks tools for business analysts
© 2013 Terracotta Inc. 9
9© 2013 Terracotta Inc. | Internal Use Only
Emerging best practices
for combining Hadoop and
in-memory data management
© 2013 Terracotta Inc. 10
Combining Hadoop and In-memory Data Management
- Businesses are looking for ways to mine real-time insights to
provide competitive advantages
- Increased adoption of transactional system data for analytics is
blurring the line between OLTP and OLAP
- New frameworks and products are bringing in-memory
technologies to the Hadoop ecosystem
© 2013 Terracotta Inc. 11
Real-time Data Integration with Hadoop
Web
Apps
Mobile
Apps
Dashboards
& Mashups
In-memory Data Management Platform
Real-time Data Apps
Transactional
Apps
Operational
Intelligence
Log Data POS Data Social Media Sensors
Data Sources
Events
Images/Video
s
Data Feeds
Real-time
data
Real-time
Insights
© 2013 Terracotta Inc. 12
12© 2013 Terracotta Inc. | Internal Use Only
Real-time intelligence example
© 2013 Terracotta Inc. 13
BigMemory & Hadoop in financial services
Before: Custom ETL connector pushing batch data
Hadoop Cluster
BigMemoryStore
Short Term
Transaction
Data
Long Term
Transaction
Data
Rules &
Triggers
Tagged
Accounts
Credit
Reference
Data
HDFS to BigMemory
Processing
Hadoop M/R
© 2013 Terracotta Inc. 14
BigMemory & Hadoop in financial services
Today: Streaming Data insights
Hadoop Cluster
Insights Hadoop M/R
BigMemory-
Hadoop
Connector
BigMemoryStore
Short Term
Transaction
Data
Long Term
Transaction
Data
Rules &
Triggers
Tagged
Accounts
Credit
Reference
Data
© 2013 Terracotta Inc. 15
15© 2013 Terracotta Inc. | Internal Use Only
Getting started with
in-memory and Hadoop
© 2013 Terracotta Inc. 16
How to get started with In-memory and Hadoop?
• If you already have a Hadoop project, look for use cases where
you want real-time access to insights
• Start with a small-to-medium sized (20-40 nodes) cluster with a
well-defined use case requiring fast access to data
• Consider exploratory use cases where you’re doing iterative
analysis on a data set to get answers faster
© 2013 Terracotta Inc. 17
In-Memory & Hadoop
Questions
Please type yours in the “Questions” panel or in the chat window.
© 2013 Terracotta Inc. 18
Connect with Terracotta
• Download “BigMemory & Hadoop” white paper
− Visit: www.terracotta.org (Resources > White Papers)
• Download “BigMemory-Hadoop Connector”
− Visit: www.terracotta.org/downloads/hadoop-connector
• Contact Manish Devgan
− Email: mdevgan@terracottatech.com
• Follow us on Twitter
− @big_memory
• Stay Tuned

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Terracotta Hadoop & In-Memory Webcast

  • 1. © 2013 Terracotta Inc. | Internal Use Only In-Memory & Hadoop: Real-time Big Data Intelligence
  • 2. © 2013 Terracotta Inc. 2 Your speaker Manish Devgan Director of Product Management Terracotta
  • 3. © 2013 Terracotta Inc. 3 What we’ll cover in this webcast • What’s Hadoop? (quick intro) • Hadoop’s weaknesses • Emerging best practices for combining Hadoop and in-memory data management • Real-time intelligence example • Getting started with in-memory and Hadoop • Q & A
  • 4. © 2013 Terracotta Inc. 4 4© 2013 Terracotta Inc. | Internal Use Only What is Hadoop?
  • 5. © 2013 Terracotta Inc. 5 What is ? • Hadoop is open-source software data management framework used to draw insights from data Components Benefits HDFS*: Scalable & distributed Storage • Data distributed across cluster nodes • Name node keeps track of location MapReduce: Parallel Processing of data • Splits a task for processing based on data locality and then assembles results • Comprises of Map() procedure for filtering & sorting and Reduce() procedure for summarizing Scalable • Efficiently store and process large data sets Reliable • Get redundant storage, with failover across cluster Rich & Flexible • Complimentary set of tools & frameworks • Store data in any format Economical • Deploy on commodity hardware *Hadoop Distributed File System
  • 6. © 2013 Terracotta Inc. 6 What is ? • With Hadoop, you can ask interesting questions about your data and get answers economically Questions Hadoop can help answer How can I target promotions to my customers for better sales? How risky are each of my customers? Which advertisement should I show to optimize return? How relevant is a result for a given search? When will my machinery likely have a malfunction?
  • 7. © 2013 Terracotta Inc. 7 7© 2013 Terracotta Inc. | Internal Use Only Hadoop’s Weaknesses
  • 8. © 2013 Terracotta Inc. 8 Hadoop’s Weaknesses • No support for real-time insights • No support to facilitate interactive and exploratory data analysis • Challenging framework for computation beyond Map Reduce • Lacks tools for business analysts
  • 9. © 2013 Terracotta Inc. 9 9© 2013 Terracotta Inc. | Internal Use Only Emerging best practices for combining Hadoop and in-memory data management
  • 10. © 2013 Terracotta Inc. 10 Combining Hadoop and In-memory Data Management - Businesses are looking for ways to mine real-time insights to provide competitive advantages - Increased adoption of transactional system data for analytics is blurring the line between OLTP and OLAP - New frameworks and products are bringing in-memory technologies to the Hadoop ecosystem
  • 11. © 2013 Terracotta Inc. 11 Real-time Data Integration with Hadoop Web Apps Mobile Apps Dashboards & Mashups In-memory Data Management Platform Real-time Data Apps Transactional Apps Operational Intelligence Log Data POS Data Social Media Sensors Data Sources Events Images/Video s Data Feeds Real-time data Real-time Insights
  • 12. © 2013 Terracotta Inc. 12 12© 2013 Terracotta Inc. | Internal Use Only Real-time intelligence example
  • 13. © 2013 Terracotta Inc. 13 BigMemory & Hadoop in financial services Before: Custom ETL connector pushing batch data Hadoop Cluster BigMemoryStore Short Term Transaction Data Long Term Transaction Data Rules & Triggers Tagged Accounts Credit Reference Data HDFS to BigMemory Processing Hadoop M/R
  • 14. © 2013 Terracotta Inc. 14 BigMemory & Hadoop in financial services Today: Streaming Data insights Hadoop Cluster Insights Hadoop M/R BigMemory- Hadoop Connector BigMemoryStore Short Term Transaction Data Long Term Transaction Data Rules & Triggers Tagged Accounts Credit Reference Data
  • 15. © 2013 Terracotta Inc. 15 15© 2013 Terracotta Inc. | Internal Use Only Getting started with in-memory and Hadoop
  • 16. © 2013 Terracotta Inc. 16 How to get started with In-memory and Hadoop? • If you already have a Hadoop project, look for use cases where you want real-time access to insights • Start with a small-to-medium sized (20-40 nodes) cluster with a well-defined use case requiring fast access to data • Consider exploratory use cases where you’re doing iterative analysis on a data set to get answers faster
  • 17. © 2013 Terracotta Inc. 17 In-Memory & Hadoop Questions Please type yours in the “Questions” panel or in the chat window.
  • 18. © 2013 Terracotta Inc. 18 Connect with Terracotta • Download “BigMemory & Hadoop” white paper − Visit: www.terracotta.org (Resources > White Papers) • Download “BigMemory-Hadoop Connector” − Visit: www.terracotta.org/downloads/hadoop-connector • Contact Manish Devgan − Email: mdevgan@terracottatech.com • Follow us on Twitter − @big_memory • Stay Tuned