This document discusses big data solutions and analytics. It defines big data in terms of volume, velocity, and variety of data. It contrasts big data analytics with traditional business intelligence, noting that big data looks for untapped insights rather than dashboards. It also provides examples of scalable big data platform architectures and advanced analytics capabilities. Finally, it outlines Anexinet's big data offerings including strategy, starter solutions, projects, and partnerships.

1. Anexinet Big Data
Solutions for Big Data Analytics

2. Big Data Defined
Volume Velocity
• Datasets that grow too large to • Large volume streaming data that
easily manage in traditional RDBMS can overwhelm traditional BI & ETL
• TBs, PBs, ZBs processes
Variety Value
• Data sources extraneous to • Big Data can have a
traditional business systems that transformational effect on business
can be unstructured and require when the proper systems and
text analytics processes are put in place

3. Big Data vs. Classic BI
 What is different from classic DW/BI and Big Data Analytics?
 Businesses today treat data warehouse & business intelligence as must-have reporting and
operational capability
 Businesses that are not fully mature in BI lifecycle may struggle with Big Data
 Big Data Projects look for untapped analytics, not BI dashboards
 SCALE: Think Volume, Variety and Velocity
 Yahoo! Uses Microsoft SQL Server & Analysis Services, with Hadoop, Oracle & Tableau
 38,000 machines distributed across 20 different clusters
 2-petabyte Hadoop cluster that feeds 1.2 terabytes of raw data each day into Oracle RAC
 Data is compressed and 135 gigabytes of data per day is sent to a SQL Server 2008 R2 Analysis
Services cube
 Cube produces 24 terabytes of data each quarter
 http://www.microsoft.com/casestudies/Case_Study_Detail.aspx?CaseStudyID=710000001707

4. Scalable Big Data Platform Architecture
HDFS Cluster In-memory
cubes
MapReduce
Framework
Analytical
Advanced in-
Columnstore
MPP memory analytics
Tables
Database
Hadoop Analytics
Star Ad-hoc data
Schemas discovery
Data Warehouse End User Reporting
© Copyright 2013 Anexinet Corp. 4

5. Go Beyond Dashboards. Provide Advanced Analytics.
 Large number of data
Tableau
points adds new business
value
 Big Data advanced
analytics requires tool that Microsoft Power
can sample complex data View
sources
 Must provide quick
aggregations of large data
sets that are easily Qlikview
consumed by the human
eye
 Must provide “data
discovery” for ad-hoc
analysis

6. Marketing Samples
 Enhance marketing
campaigns with Big Data
 Social analytics,
customer analytic,
targeted marketing,
brand sentiment
 Big Data has proven
transformational for
marketing organizations
(Razorfish, Yahoo!,
NBC, [x+1])
Web Analytics from Google Analytics

7. Anexinet Big Data Offerings
Strategy Engagement
• Customer stakeholder interviews & interactive sessions
• Define Big Data Requirements
• Design Big Data Strategy
• Deliver Strategy & Roadmap Documents
Starter Solution
• Let Anexinet handle the hardest parts of a Big Data solution
* Getting started
* Collecting & processing data
* Uncover business value from Big Data
Big Data Project Engagement
• End-to-end Big Data project
* Big Data Discovery
* Big Data Platform
* Big Data Analytics
* Big Data Visualizations

8. Partnerships
Big Data Platforms Big Data Databases Big Data Visualizations
• EMC Greenplum • HP Vertica • QlikView
• Hortonworks • EMC Greenplum • Tableau
(OSS, MSFT, HP) • Microsoft PDW • Microsoft PowerPivot
• Cloudera • Oracle Exalytics • Microsoft Power View
(OSS, Oracle, HP) • Oracle Big Data
Appliance

9. A Credible Partner to Deploy Big Data Solutions
Security Integration Configuration Governance
• Ensure • ETL / ELT • Configure the • Ensure Data
privacy of PII • Integrate Big Data Quality
Hadoop into environment to • MDM
• Conform Big your DW & maximize • Process
Data solution Analytics throughput, Governance
to your environments performance
enterprise • Integrate Big and analytics to
security Data into your IT meet your
investments stated SLA goals
standards

10. Top Impediments to Successful Big Data Analytics

11. Big Data Buzzword Glossary
 Big Data: Think 3 v’s, unstructured data, data that is not currently managed in DW. This is the data that
companies need to do game-changing analytics.
 Big Data Analytics: Business insights gained from mining Big Data to transform business processes
 Columnar: Column-oriented databases that are used in Big Data scenarios because of their speed and
compression capabilities, i.e. HP Vertica, HBase
 Hadoop: Apache open-source framework for Big Data processing. Made up of multiple components. The
leading Big Data platform. Marketed by Couldera & Hortonworks.
 In-memory DB: A database that resides fully in memory, eliminating IO bottlenecks. Very important in Big
Data Analytics systems, i.e. Microsoft PowerPivot, SSAS 2012, SAP HANA
 MapReduce: Distributed data programming and processing framework. A key aspect of processing Big
Data is using a MapReduce framework across distributed clusters of commodity servers. Available as
open source in the Hadoop framework and in various Hadoop distribution flavors.
 MPP: Massively Parallel Processing database engine, mostly used for data warehouse & BI workloads.
I.e. SQL Server PDW, IBM Netezza, Teradata
 NoSQL: Key-value data store for quick eventual-ACID schemaless database writes. Big Data systems will
use these to store data coming in from sources that dump large amounts of data quickly, i.e. Cassandra,
MongoDB.