Wide Column Store for Big Data describes Apache Cassandra as a massively scalable and decentralized structured data store for managing large volumes of real-time data. Cassandra uses a distributed hash table and sharding for elastic scalability and replication for fault tolerance. It offers tunable consistency levels and a flexible data model using a wide column structure without a rigid schema. Cassandra can support real-time queries, analytics through Hadoop integration, and Solr-based fulltext search, making it a suitable big data solution.

1. Wide Column Store for Big Data
APACHE CASSANDRA
Kai Spichale

2. Outline
 Motivation
 Introduction to Cassandra
 Big Data Solution

3. „Must Haves“ for Big Data?
 What do modern businesses need for big data?
 A scalable high-performance database
that is easy to use and
cost effective
Scalable
Performance
Cost Operational
Effective Ease

4. „Must Haves“ for Big Data?
 „Modern businesses need to be able to manage large
volumes of realtime data and run analytic and enterprise
search operations on that same data as quickly as possible
to make business decisions.“
Real-Time Analytic/Search
Databases Databases
Data Movement
ETL Process

5. Legacy RDBMS ≠ Big Data
 „Big data is comprised of (1) Velocity – how fast the data is coming in;
(2) Variety – all types are new being captured; (3) Volume – TB‘s to
PB‘s of data; (4) Complexity – mulit-location, data center, etc.“
 “Big data technologies describe a new generation of technologies and
architectures, designed to economically extract value from very large
volumes of a wide variety of data, by enabling high-velocity capture,
discovery, and/or analysis.”
 “Big data is data that exceeds the processing capacity of conventional
database systems. The data is too big, moves too fast, or doesn’t fit the
strictures of your database architectures. To gain value from this data,
you must choose an alternative way to process it.”

6. Trends & Challenges in Data Mngt.
Exponential Data
Key Value
Growth
Cloud Wide Column
Semi Structured
Document
Data
More Connected
Graph
Data

7. Trends & Challenges in Data Mngt.
Exponential Data
Key Value
Growth
Apache
Cloud Wide Column
Cassandra
Semi Structured
Document
Data
More Connected
Graph
Data

8. Apache Cassandra
 A massively scalable, decentralized, structured
data store (aka database).
 Project history:

9. Cassandra is…
Nodes Token
A 0
B 4
C 8
D 12
E 16
F 20
G 24
 O(1) Distributed Hash Table H 28
 Sharding, Replication
 Elastic H A
G B
 Fault tolerant
 No Single Point of Failure
F C
 Durable
E D

10. Cassandra is…
C
 AP-System (CAP Theorem)
 Eventual consistency
A P
 Tunable trade-offs:
 Consistency vs. Latency
 Choose between synchronous or asynchronous
replication for each update
C = Consistency
A = High Availability
P = Partitioning Tolerance

11. Cassandra is…
Keyspace
 A BigTable Clone
Column Family
 No schema Key Row
Column Column
Key Row
Column
Key Row
 Predestined for Column Column Column
 Semi-structured data Column Family
Row
 Sparse data SuperColumn SuperColumn
Column Column Column Column
Row
SuperColumn
Column Column Column

12. Cassandra-based Big Data
Solution
H
Analytics
Hadoop
A
Real-time
Cassandra
 Real-time queries with
Cassandra
G
Analytics B
Real-time
Cassandra
Hadoop
Cassandra Cluster  Distributed Search with
(Replication) Solr
F
Search C
Real-time
Solr
Cassandra
 Analytics with Hadoop
MapReduce
E
Search D
Search
Solr Solr

13. Summary
 Apache Cassandra is a elastic scalable, fault-
tolerant data store
 Tunable consistency levels
 Wide Column: flexible datamodel without schema
 Supports: real-time queries, analytics through
Hadoop integration, Solr-based fulltext search

14. Thank you!
Q&A