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Anexinet Big Data Solutions
 

Anexinet Big Data Solutions

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Big Data Solutions offered by Anexinet

Big Data Solutions offered by Anexinet

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    Anexinet Big Data Solutions Anexinet Big Data Solutions Presentation Transcript

    • 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
    • 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
    • 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.