Content & Metadata Store - Big Data Analytics News

Report
Use Case Couchbase
Common Use Cases
Social Gaming
• Couchbase stores
player and game
data
• Examples
customers include:
Zynga
• Tapjoy, Ubisoft,
Tencent
Mobile Apps
• Couchbase stores user
info and app content
• Examples customers
include: Kobo, Playtika
Ad Targeting
• Couchbase stores
user information for
fast access
• Examples customers
include: AOL,
Mediamind,
Convertro
Session store
• Couchbase Server as a keyvalue store
• Examples customers include:
Concur, Sabre
User Profile Store
• Couchbase Server as a
key-value store
• Examples customers
include: Tunewiki
High availability cache
• Couchbase Server used as a cache tier replacement
• Examples customers include: Orbitz
Content & Metadata
Store
• Couchbase document store
with Elastic Search
• Examples customers
include: McGraw Hill
3rd party data aggregation
• Couchbase stores social media and
data feeds
• Examples customers include:
Sambacloud
Use Case: Content and Metadata Store
Content and Metadata Store
Types of Data
• Content metadata
• Content: Articles, text
• Landing pages for website
• Digital content: eBooks,
magazine, research material
Application Requirements
• Flexibility to store any kind of
content
• Fast access to content metadata
(most accessed objects) and
content
• Full-text Search across data set
• Scales horizontally as more content
gets added to the system
Why NoSQL and Couchbase
• Fast access to metadata and content via object-managed cache
• JSON provides schema flexibility to store all types of content and
metadata
• Indexing and querying provides real-time analytics capabilities
across dataset
• Integration with ElasticSearch for full-text search
• Ease of scalability ensures that the data cluster can be grown
seamlessly as the amount of user and ad data grows
McGraw Hill Education Labs
Learning portal
Use Case: Content and metadata store
Building a self-adapting,
interactive learning portal with
Couchbase
The Problem
As learning move online in great numbers
Growing need to build interactive learning environments that
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Scale!
Scale to millions of
learners
Serve MHE as well as third-party
content
Including
open content
Support
learning apps
Self-adapt via
usage data
The Challenge
Backend is an Interactive Content Delivery Cloud that must:
•
Allow for elastic scaling under spike periods
•
Ability to catalog & deliver content from many
sources
•
Consistent low-latency for metadata and stats access
•
Require full-text search support for content
discovery
•
Offer tunable content ranking & recommendation
functions
Experimented with a combination of:
XML Databases
In-memory Data Grids
SQL/MR Engines
Enterprise Search Servers
The Learning Portal
•
Designed and built as a
collaboration between MHE Labs
and Couchbase
•
Serves as proof-of-concept and
testing harness for Couchbase +
ElasticSearch integration
•
Available for download and
further development as open
source code
Couchbase 2.0
1
Store full-text articles as well
as document metadata for
image, video and text content in
Couchbase
2
Logs user behavior to calculate
user preference statistics (e.g.
video > text)
+
3
4
Elasticsearch
Continuously accept updates
from Couchbase with new
content & stats
Combine user preferences
statistics with custom
relevancy scoring to provide
personalized search results
Architecture
Thank you
[email protected]
@anil.kumar1129

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