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Top 5 Factors to Consider When

Choosing a Big Data Solution

Robin Schumacher, VP Products

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• VP Products, DataStax

• Director of Product Management MySQL, then

EnterpriseDB

• VP Product Management at Embarcadero

Technologies

• DBA with Oracle, Teradata, SQL Server, DB2,

others…

• Database software reviewer for various magazines

• Author of 3 database books

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• Founded in April 2010

• Commercial leader in Apache Cassandra™, the

popular open-source “big data” database

• 140+ customers

• 40+ employees

• Home to Apache Cassandra Chair & most

committers

• Headquartered in San Francisco Bay area

• Funded by prominent venture firms

Overview of DataStax

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• Define big data

• Identify “must have’s” of a big data

solution

• Discuss difficulty in getting all of them

from a business and technical

perspective

• Brief tour of NoSQL, Cassandra and

DataStax Enterprise

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What big data is and the

domains of data that need to be

considered.

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“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."

* All definitions have one thing in common: new technology is needed for big

data…

”Datasets whose size is beyond the ability of typical database software

tools to capture, store, manage, and analyze "

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1. Real-time – transactional, online, streaming, low

latency data

2. Analytic – aggregated data from real-time feeds or

other sources; many times batch in nature

3. Search – supporting data, both external and

internal, used for locating desired information and/or

objects (e.g. products, documents, etc.)

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Research done by McKinsey & Company shows the eye-opening,

10-year category growth rate differences between businesses that

smartly use their big data and those that do not.

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What are the top five things to

consider in a big data solution?

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The characteristics that define big data are:

1. Velocity – includes the speed at which data comes in,

and the number of events/elements being stored

2. Variety – involves structured, semi-structured,

unstructured data

3. Volume – can equate to TB-PB’s of data

4. Complexity – typically entails the difficulty distributing

the data (e.g. multi-data centers, cloud, etc.) and

managing the data traffic/movement (e.g. ETL,

migrations, etc.)

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• Data has high rate of input

• Data has large quantity of elements/events

•Sensor data

•Media streaming

•Mobile devices

•Financial streams

•Web clickstream

•Traffic monitoring

•Patient care

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• Includes structured, semi, and unstructured

• Necessitates new data model and file formats

• Involves, real-time, analytic, and search data

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• TB’s to PB’s

• Also involves data maintenance functions

(e.g. purging, etc.)

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The McKinsey report found that the average investment firm with fewer than 1,000

employees has 3.8 petabytes of data stored, experiences a data growth rate of 40 percent

per year, and stores structured, semi-structured, and unstructured data. Overall, McKinsey

found that 15 out of 17 industry sectors in the United States have more data stored per

company than the U.S. Library of Congress (which had 235 terabytes of information at the

time of McKinsey’s study)

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• Typically involves data distribution,

movement, etc., across multiple data centers

and geographies

• Can be on-premise, cloud, or hybrid

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Getting a big data technology that provides two out of three can be

challenging; finding one that supplies all three can be very hard.

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NoSQL, Cassandra, and

DataStax Enterprise for big data.

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NoSQL is a broad class of next-generation database management

systems that differ from the classic model of the relational database

management system (RDBMS) in some significant ways, most

important being they:

• Sport a less-rigid, more dynamic data model

• Look to provide user controlled trade-off’s to the CAP theorem

• Do not support ANSI SQL or operations such as joins

• Attempt to solve some or all of the challenges of big data

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A NoSQL solution like Apache Cassandra:

• Handles high velocity data with ease

• Uses schema that support broad varieties of data

• Scales from GB’s to PB’s with linear performance

capabilities

• Is built to handle multi-location/data center use cases

• Is designed for continuous availability

• Offers quick installation and configuration for multi-node

clusters

• Is open source and/or cost 80-90% less than RDBMS’s

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* Uses Cassandra and Hadoop for data management

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YCSB Benchmark

Source: http://blog.cubrid.org/dev-platform/nosql-benchmarking/?utm_source=NoSQL+Weekly+List&utm_campaign=143fae86b2-

NoSQL_Weekly_Issue_41_September_8_2011&utm_medium=email

Cassandra is:

Nearly 4x better in writes

Nearly 2x better in reads

Over 12x better in reads/updates

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“Cassandra was just a better design all around – more truly horizontally scalable

and with less management overhead – and there‟s no single point of failure. I

looked at Cassandra‟s architecture and thought, „Yeah, that‟s how you do it.‟”

- Matt Conway, VP of Engineering

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“The hundreds of millions of web pages that contain this information

are stored in a multi-terabyte cache that grows continually as we

crawl the web, analyzing new pages and finding new versions of

existing pages.” – Zoominfo Architect on using Cassandra

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“I can create a Cassandra cluster in any region of the world in 10

minutes. When marketing guys decide we want to move into a

certain part of the world, we’re ready.” - Netflix architect

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• Fully integrated smart big data platform

• Production certified Cassandra

• Continuously available analytics with Hadoop

• Scalable enterprise search with Solr

• Built in workload isolation

• No costly and error-prone ETL operations

• Easy migration of RDBMS and log data

• Simple to install and grow

• OpsCenter management solution

• 80-90% less cost than RDBMS vendors

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DataStax Enterprise ServerNo ETL and Built-in Workload Isolation

• Data written to any node is automatically and transparently written to all other

nodes.

• Mixed workload management is automatic; real-time, analytic, and search

workloads/nodes do not compete for compute or data resources with other

nodes.

ETL

Staff /

Processes

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DataStax Enterprise ServerMulti-Data Center and Cloud Capable

• Built-in capabilities to maintain the same database cluster between many

different data centers

• Able to easily do on-premise data centers and cloud use case models

Data Center 1 Data Center 2

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• DataStax OpsCenter is a visual management and monitoring

solution for DataStax Enterprise

• Manage and monitor all Cassandra and Hadoop and Solr

operations

• Visual alerts and notifications

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1. Does it handle high data velocity?

2. Can it tackle all types of data?

3. How well does it perform with large data volumes?

4. Can it handle complex distribution and

implementation use cases (e.g. on-premise/cloud,

multi-geo)?

5. How does it stack up in hitting the big data “bulls

eye?” (i.e. cost, saleable performance, and

operational ease are concerned)?

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DataStax Enterprise is tailor made for high-velocity, multi-variety,

large volume, and complex deployment use cases that involve big

data.

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Recommended Reading

http://www.datastax.com/resources/whitepapers

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Next Steps

Download DataStax Enterprise and try it in your own

environment.

• Go to

www.datastax.com/download

• Download a copy of DataStax

Enterprise

• Installs and configures in minutes

• Completely free for development

use

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For More Information

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