Anexinet Big DataSolutions for Big Data Analytics
Big Data DefinedVolume 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 processesVariety 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
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
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
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 OfferingsStrategy 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 DataBig 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 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
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
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.
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