Cloudera & SAS Partnership Overview. Graham Pymm Cloudera Systems Engineer

Transcription

1 Cloudera & Partnership Overview Graham Pymm Cloudera Systems Engineer 1

2 Strong Executive & Product Level Alignment Management: Formal Alliance forged in January 2013 CTO level commitment from both companies Technical: internal development teams have a Cloudera first policy and all internal work is performed on Cloudera clusters. Dedicated Cloudera resources at Cloudera HQ and HQ working with R&D has dedicated R&D resources optimize solutions for the Cloudera platform Release activities: Joint training courses in plan, provide education on Cloudera and content for analytics on big data Engineering schedule coordination ensure quick uptake of new releases from each side

3 Strong Go-To-Market Alignment More than 80% of deployments on are running Cloudera Master Reciprocal Services Agreement in place Joint Data Scientist Training course ensure best practices Visual Analytics and Cloudera Enterprise Data Hub Starter Service package offering Cloudera and Confidential

4 and Cloudera Benefits Seize New Opportunities from All of Your Data. Make more precise decisions by analyzing all structured and unstructured data sets. Drive compelling cusmer engagements improve revenue and service levels. Accelerate Time--Value. Visual Analytics simplify working with data. Cloudera Manager simplify big data system administration. In-memory data and analytics processing for faster performance. Reduce Costs, Risks and Uncertainty. provides scalable and cost-effective big data management. Largest community of trained developers, administrars and data scientists. Joint and Cloudera research and development ensures maximum cusmer value. Utilities usable by cusmers configure and install with Cloudera

5 Architectural elements of on CDH Hi-performance in-memory analytics on CDH Inmemory agents Inmemory agents CDH Inmemory agents Inmemory agents EP for data processing on EP using MR EP using MR CDH EP using MR EP using MR Data extract from Hive Impala CDH

6 nagement ffective big data management Dedicated Rack Dedicated Rack Cloudera Cluster Cloudera Cluster nagement commercial & Cloudera have Dedicated Rack Have Integrated Across Cloudera The Cluster f p Entire Analytics imum commercial have a cusmer value. have a ue. imum p have cusmer a value. Lifecycle Embedded process enables users perform data manipulation, variable transformation, ue. er a? Embedded process enables explorary Embedded users analysis, process perform statistical enables data users manipulation, modeling perform machine data variable learning manipulation, transformation, ty er a? techniques, variable integrated transformation, analytics complements modeling terprise y plements - analytics Data Hub complements explorary Embedded analysis, process explorary statistical (EDH) comparison enables modeling users analysis, and scoring perform statistical & machine - all data modeling learning inside manipulation, & techniques, machine the variable learning integrated environment. transformation, techniques, modeling integrated modeling DH) Wouter Kroon <[email protected]> lps erprise elevate Data the Hub conversation (EDH) explorary comparison analysis, and scoring comparison statistical - all inside modeling and scoring the & machine - all inside environment. learning the techniques, n environment. n plements Wouter Kroon <[email protected]> integrated modeling <[email protected]> ersation lps DH) in the elevate organization. the conversation comparison Analytics and scoring - all inside the environment. n in Analytics Wouter Kroon <[email protected]> rsation use the cases organization. for. Analytics Analytics use p. Positioning cases for the. integration products for integrate with Cloudera egration re - Positioning has foothold the integration products for products integrate for with integrate Cloudera with Cloudera p. re d over the entire Analytics Life cycle egration ra s ability has a foothold close or shorten MANAGE products over the for entire over Analytics integrate the entire Life with cycle Analytics Cloudera Life cycle d ra s r shorten ability close or shorten MANAGE MANAGE over the entire Analytics Life cycle r shorten alignment and integration MANAGE integration iar, alignment as no other and integration Manage Data Manage Data Explore Data Explore Data integration rs iar, doop tighter as no integration other with Manage /Access Data Manage EXPLORE /Access Data Visual Analytics Explore Data rs tighter integration with Visual Explore Analytics Data doop with EXPLORE Manage /Access EXPLORE Cloudera /Access Impala In-memory for Data /Access Cloudera Impala Visual Explore Analytics Data Visual Analytics with Data Quality Accelerar /Access DEPLOY Data Quality Accelerar* Cloudera Impala EXPLORE /Access /Access Cloudera Impala Visual Analytics DEPLOY DEPLOY In-database Code Data duct Management, Sales, & /Access Data Quality Accelerar* Cloudera Accelerar Impala In-database Quality Accelerar* Code Accelerar* Sales duct Management,, & Sales, MONITOR & In-database Code Accelerar* DEPLOY Data In-database Quality Accelerar* Code Accelerar* MONITOR MONITOR Develop Models ercial distributions Develop Models In-memory Develop for Models Sales, & In-database Code Accelerar* Develop Models ributions ercial distributions MONITOR In-memory In-memory Deploy & Monir loudera, Visual Analytics, Deploy Monir HPA Products Develop for Models HPA for Products DEVELOP Scoring Accelerar ccelerar loudera, for Visual Cloudera Analytics, Deploy DEVELOP MODELS Scoring & Monir Accelerar Visual Statistics Visual HPA Statistics* Products Analytics, Deploy & Monir ibutions In-memory HPA for Products DEVELOP ccelerar for Cloudera MODELS In-memory for Scoring Accelerar Visual Statistics* era Analytics, MODELS Deploy Scoring & Monir Accelerar Visual HPA Statistics* Products DEVELOP * Available August 2014 * Available August 2014 era age MODELS Scoring Accelerar * Available Visual August Statistics* 2014 age * Available August 2014 professional services (phase I s professional (phase I er markets). services (phase I Cloudera and Confidential s er (phase markets). I adoop have a r value. cs complements ub (EDH) conversation tion. Analytics adoop. he integration othold lose or shorten and integration er ration with ent, Sales, distributions isual Analytics, loudera rvices (phase I Embedded process enables users perform data manipulation, variable transformation, explorary analysis, statistical modeling & machine learning techniques, integrated modeling comparison and scoring - all inside the environment. n Wouter Kroon <[email protected]> DEPLOY & MONITOR MANAGE DEVELOP MODELS EXPLORE products for integrate with Cloudera over the entire Analytics Life cycle Manage Data /Access /Access Cloudera Impala Data Quality Accelerar* In-database Code Accelerar* Deploy & Monir Scoring Accelerar Explore Data Visual Analytics Develop Models In-memory for HPA Products Visual Statistics* * Available August 2014

7 & Cloudera Product Touch-Points Access for & Access for Impala: Enables data access in Base solutions developed on Base can connect CDH: Enterprise Miner, Data Integration Studio, Text Miner, /Stat, /Graph, Forecast Studio, /ETL and many more Data Management: Data Integration using CDH as source or sink through Data Integration Studio Metadata Server support for CDH High Performance Analytics: On-cluster, high-performance machine learning/statistics for big data Visual Analytics: On-cluster, high-performance business intelligence for big data Data Loader High-performance model scoring and deployment with faster time results Run DS/DS2 code natively inside the cluster using Map Reduce Load existing dataset in the in-memory products

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