Anexinet Big Data

Solutions for Big Data Analytics

Big Data Defined

Volume• Datasets that grow too large to

easily manage in traditional RDBMS• TBs, PBs, ZBs

Velocity• Large volume streaming data that

can overwhelm traditional BI & ETL processes

Variety• Data sources extraneous to

traditional business systems that can be unstructured and require text analytics

Value• Big Data can have a

transformational effect on business when the proper systems and processes are put in place

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

Scalable Big Data Platform Architecture

© Copyright 2013 Anexinet Corp. 4

Hadoop

Data Warehouse

Analytics

End User Reporting

HDFS Cluster

MapReduce Framework

MPP Database

Star Schemas

In-memory cubes

Analytical Columnstore

Tables

Advanced in-memory analytics

Ad-hoc data discovery

Go Beyond Dashboards. Provide Advanced Analytics.

Large number of data points adds new business value

Big Data advanced analytics requires tool that can sample complex data sources

Must provide quick aggregations of large data sets that are easily consumed by the human eye

Must provide “data discovery” for ad-hoc analysis

Tableau

Microsoft Power View

Qlikview

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

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

Partnerships

Big Data Platforms

• EMC Greenplum• Hortonworks (OSS,

MSFT, HP)• Cloudera (OSS, Oracle,

HP)

Big Data Databases

• HP Vertica• EMC Greenplum• Microsoft PDW• Oracle Exalytics• Oracle Big Data

Appliance

Big Data Visualizations

• QlikView• Tableau• Microsoft PowerPivot• Microsoft Power View

A Credible Partner to Deploy Big Data Solutions

Security

• Ensure privacy of PII

• Conform Big Data solution to your enterprise security standards

Integration

• ETL / ELT• Integrate

Hadoop into your DW & Analytics environments

• Integrate Big Data into your IT investments

Configuration

• Configure the Big Data environment to maximize throughput, performance and analytics to meet your stated SLA goals

Governance

• Ensure Data Quality

• MDM• Process

Governance

Top Impediments to Successful Big Data Analytics

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.