GO BIG WITH DATA PLATFORMS: HADOOP AND TERADATA 1700

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "GO BIG WITH DATA PLATFORMS: HADOOP AND TERADATA 1700"

Transcription

1 GO BIG WITH DATA PLATFORMS: HADOOP AND TERADATA 1700 Cesar Rojas Director of Product Marketing Data Science & Hadoop Spring Teradata User Group Meetings: Los Angeles

2 AGENDA What is a Data Platform? Teradata and Hadoop Teradata Portfolio for Hadoop Integrated Big Data Platform 1700 When to use which? Q&A 2 Copyright Teradata

3 TERADATA UNIFIED DATA ARCHITECTURE System Conceptual View ERP MOVE MANAGE ACCESS Marketing Marketing Executives SCM CRM INTEGRATED DATA WAREHOUSE Applications Operational Systems Images DATA PLATFORM Business Intelligence Customers Partners Audio and Video PLATFORM FAMILY Data Mining Frontline Workers Machine Logs Text INTEGRATED BIG DATA PLATFORM APPLIANCE FOR HADOOP INTEGRATED DISCOVERY PLATFORM Math and Stats Business Analysts Data Scientists Languages Web and Social BIG ANALYTICS APPLIANCE Engineers SOURCES ANALYTIC TOOLS & APPS USERS

4 Key Trend: Data Platform Pressure on IT budgets requires companies to optimize workloads and drive down costs Data-driven insights and analytics are integral to daily operations, driving mission-critical requirements Long-term trending, year-over-year comparisons, and regulatory compliance are driving companies to retain more raw data for longer periods Companies have a Hadoop first mentality and want to see Hadoop succeed or fail before they consider other technology 4 Copyright Teradata

5 Requirements of a Data Platform Data Platform > History and long term storage > Transformations > Batch processing > Raw data capture > Low $/Terabyte DATA PLATFORM Data Mining Engineers Integrated Data Platform Math and Stats > Multiple uses on one platform > Current, archive and raw data > Ad hoc deep analytics on new data Data Scientists > Resource Flexibility Languages 5 Copyright Teradata ANALYTIC TOOLS USERS

6 Teradata and Hadoop Positioning Teradata Hadoop Characteristics High performance analytics and complex joins High concurrency SQL (ANSI and ACID compliant) Advanced workload mgmt High Availability Data Governance Emerging Late Binding One-stop support Security Use Cases Low $/TB Long-Term Raw Data Storage ETL offload Reporting Deep Analytics Characteristics Fast Data Landing and Staging MapReduce, Hive, Pig Emerging SQL/SQLlike interfaces Batch-oriented processing Low workload concurrency Multi-structured and file based data Late Binding Open Source Community 6 Copyright Teradata

7 TERADATA PORTFOLIO FOR HADOOP

8 Why are customers adopting Hadoop? Cost containment on growing data No-ETL data loading Open source Flexibility Development Community CEO read about it on plane Copyright Teradata

9 Hadoop coming to enterprises 202 Customers Surveyed * Source: IDC's Red Hat Hadoop Usage Survey, August 2013 Hadoop is increasingly being adopted by customers to handle non-traditional data / use cases 9 Copyright Teradata

10 Challenges with today s Hadoop deployments Hadoop technologies lack enterprise features on Availability, Manageability, Supportability. Scarcity of Hadoop Resources (know-how, talent ), Finding & Retaining people; steep learning curve Unfamiliarity and lack of integration tools to existing and new data sources inside enterprises Hadoop world is constantly evolving with multiple players; lacks stability with software Challenges are slowing down full production 10 Copyright Teradata

11 Teradata Strategy for Hadoop Market Objectives - Become #1 advisor to customers in design & implementation of Hadoop - Provide complete Hadoop solutions (hardware, software, services & support) which leverage core Teradata IP and skills Product: close Hadoop enterprise gaps & invest in strengths - Provide enterprise-ready Hadoop offerings Easier to deploy, manage, secure, monitor, and service as part of ecosystem Business-friendly interfaces to our analytical platforms (SQL-H, Teradata Studio) - Develop IP and partnerships in key Hadoop use cases (e.g., data lake ) - Interoperability & connectors with popular distros (e.g., Sqoop w/cloudera) GTM and ecosystem strategies - Ride the hype of Hadoop; steer conversation to biz value - Promote Teradata Portfolio for Hadoop (products & services) but support other Hadoop distributions: provide customer choice - Message Hadoop strengths in staging/etl of Unified Data Architecture - Engage community via Hortonworks: credibility & influence product direction 11 Copyright Teradata

12 TERADATA PORTFOLIO FOR HADOOP Taking Hadoop from Silicon Valley to Main Street Most Trusted and Flexible Hadoop Platforms for Your Next-Generation Unified Data Architecture 1. Teradata Aster Big Analytics Appliance 2. Teradata Appliance for Hadoop 3. Teradata Commodity Offering with Dell 4. Hortonworks Data Platform software-only support resell Complete consulting and training capability > Big Analytics Services across the UDA > Data Integration Optimization ETL, ELT across the UDA > Hadoop deployment and mentoring > Teradata delivering Hortonworks training > Hadoop Managed Services operations and administration Customer Support for Hadoop > World-class Teradata customer support, backed by Hortonworks 12 Copyright Teradata

13 Introducing the Appliance for Hadoop Teradata Appliance for Hadoop is enterprise class > Landing area and data lake for raw files of any type > Data refining engine some transformations and simple math at scale > Archival system for histories of data with low or unknown value Teradata Enterprise Access for Hadoop > Enables business user to easily access Hadoop data with standard SQL from within the Teradata Database and BI tools > SQL-H provides on-the-fly access to data, leveraging HCatalog > Teradata Studio w/smart loader for Hadoop: ad-hoc data movement Best-of-breed Technology Partner Value Add > Hortonworks engineering relationship: SQL-H, Viewpoint integration with Ambari, and high performance Hadoop nodes > Protegrity, Informatica, Revelytix 13 Copyright Teradata

14 Teradata Vital Infrastructure Teradata Appliance for Hadoop Highlights Aster and Teradata SQL-H Teradata Studio with Smart Loader Value Added Software from Partners Teradata Viewpoint Teradata Connector for Hadoop (TDCH) Intelligent Start and Stop NameNode Failover Teradata Distribution for Hadoop (Based on Hortonworks HDP) Optimized hardware for Hadoop BYNET V5 40GB/s InfiniBand interconnect 14 Copyright Teradata

15 InfiniBand BYNET V5 Value-Add Performance > Automated network load balancing > High speed interconnect > Intra and Inter system communication High Availability > Automated network failover > Redundancy across two active fabrics > Multiple level network isolation Server Management (SM) Service-ability > Delivers automated core addressing and naming services > Gives Services org a holistic view of systems on the fabric > Provides automated hardware monitoring > Proactive phone home alerting It s not just for performance! 15 Copyright Teradata

16 Why Teradata Appliance for Hadoop? Building a Hadoop Cluster Teradata Multiple vendor relationships Procure, Set up, Install Updates Hardware, Software Integration test deploy Multiple consoles One vendor easier acquisition Quick to set up, Plug n play Elimination of integration complexity Predictable performance Single pane of glass management 16 Copyright Teradata

17 Teradata Appliance for Hadoop Teradata Open Distribution for Hadoop (TDH) Core Based on Hortonworks Leading Hadoop distribution Highest number of committers for Apache Hadoop Influence Hadoop roadmap via projects Teradata Enterprise Level Components Value added enterprise components Simplify Hadoop Operations Intelligent Hadoop Builder 17 Copyright Teradata

18 Teradata Appliance for Hadoop Optimized Hadoop Infrastructure Hardened, Finetuned system Hardware and Software Tuned for high Performance Preconfigured nodes in a readyto-go box BYNET Connectivity High speed network for data transfer Automatic Load Balancing Network Machine failover Teradata Vital Infrastructure 24X7 Proactive monitoring of components Automatic Alert creation and notification Reduced incidents 20 Copyright Teradata

19 Teradata Appliance for Hadoop Enterprise Access for Hadoop SQL Access to Hadoop Data On-the-fly SQL access from Teradata/Aster Give business analysts ANSI SQL 90+ prepackaged analytics High Speed Connectors Teradata connectors for Hadoop ( TDCH ) Smart Loader functionality via Teradata Studio Drag/drop data across systems 25 Copyright Teradata

20 Teradata Appliance for Hadoop Enterprise Readiness Availability Manageability World Class TD Support Critical NameNode availability Automatic failover via BYNET Redundant network access via dual networks Centralized management for multiple systems Integrated monitoring & reporting Configurable UI, Metrics analysis Industry leading support from Teradata Connected to Teradata vital infrastructure Single vendor support for all platforms TD Services for Hadoop Leadership in Data Architectures Applying Best practices & process Years of expertise in serving large customer data environments 32 Copyright Teradata

21 Teradata Hadoop for Active Data Archive Active data archive for better data management Situation High performance storage is expensive. A large integrated pharmacy HC provider deals with a variety of data with different business value. All data cannot be store on the same system. Ever expanding data is only adding to this challenge. Problem Long terms storage data cannot be queried and it takes a long time for retrieval. No analysis can be performed on the archived data. Losing out on business value from this valuable data. Solution Used Teradata Hadoop nodes to store all the data coming in from weblogs, medical data, JSON files. Hadoop also serves as a enrichment layer to enhance data for high-end analytics consumption. The complete solution provides easy movement of data from Hadoop, Aster and Teradata. Impact Reduced storage costs for data variety Perform adhoc analytics on the multiple versions of data Retrieve data in minutes ( vs. days with tape archives ) Reduced load and improved performance of DW/Databases 40 Copyright Teradata

22 Active Data Archive Different kinds of data exists at the customer s architecture > Enterprise data, web logs, medical records All of this data needs to be retained and queried Current Limitations: Storage costs are prohibitively expensive to store all data in the enterprise databases; Not all data is enterprise and has same value Overloaded production and backup systems > Necessity to keep only business and mandated data in enterprise DBs Current archival systems have very long restore times No ability to query or ad-hoc analytics on archived data 41 Copyright Teradata

23 New Data Architecture with Teradata Hadoop Platform Enterprise Data < 1 year old Oracle Teradata SQL Operational Queries Enterprise Data Platforms Business Analysts SQOOP Connectors HIVE Querylayer Visualization Tools Tableau Microstrategy Excel, ODBC/JDBC Unstructured Data Weblogs, voice call recordings HCATOG METADATA LAYER Node 1 Node 2 Node 3 Node N HDFS/MAP REDUCE Cluster Teradata Hadoop Platform HIVE Queries on data > 1 year old Enterprise data < 1 year goes to high-end databases Data > 1 year and unstructured data goes to Hadoop 42 Copyright Teradata

24 New Data Journey Realization Business Value Efficient use of higher-end systems > Reduce network traffic, IO, CPU consumption > Reduce load of traditional systems > Ease of backup of enterprise data > Integrated architecture for data management Unlocking business value from historical data > HiveQL queries to query Hadoop data > SQL-H queries to query combined data > Archived data access in minutes (vs. days) Reduced spend on Enterprise DBs > Store and analyze the data where it belongs 43 Copyright Teradata

25 TERADATA INTEGRATED BIG DATA PLATFORM 1700

26 INTEGRATED BIG DATA PLATFORM Copyright Teradata

27 Paradigm Shift One Platform for Many Uses Extreme Data Appliance 1700 Integrated Big Data Platform 1700 Single Use Platform > Analytical Archiving Low $/TB Large Capacity Low / Slow Performance Low Concurrency Configured for Data Capacity Only Multiple Usages > Contextual Analytics > Resource Flexibility > Always On > Corporate Memory Lower $/TB > Comparable to Hadoop Configure based on Nodes for Higher Performance across Multiple Workloads 46 Copyright Teradata

28 One Platform, Many Uses Contextual Analytics Resource Flexibility Always On Corporate memory Unrefined Multi-structured data Current data Archival data Raw data IDW data years 1-5 IDW data years 5-10 Unrefined structured data 47 Copyright Teradata

29 Integrated Big Data Platform Contextual Analytics Resource Flexibility Always On Corporate memory Deep analytics Data Labs Data refinery Hadoop integration Ad hoc projects Peak workload assist Disaster recovery High availability Archive reporting & retrieval Audit and compliance 48 Copyright Teradata

30 Differentiation Use case Integrated Big Data Platform Differentiation Contextual Analytics Resource Flexibility Always On Corporate Memory Deep analytics Data Labs Data refinery Hadoop integration Ad hoc projects Peak workloads assist Disaster Recovery High Availability Archive reporting and retrieval Audit and compliance Join big data to context in IDW Best big data SQL and performance Self-service sandboxes and Hadoop queries Push-down transformers Easy query balancing across systems Workload management 49 Copyright Teradata Automated failover Sync two systems with robust tools Query rerouting Full security, trustworthy data Easy, selfservice queries No spinning tapes, no programming

31 Value-add Use case Integrated Big Data Platform Value Added Contextual Analytics Resource Flexibility Always On Corporate Memory Deep analytics Data Labs Data refinery Hadoop integration Ad hoc projects Peak workloads assist Disaster Recovery High Availability Archive reporting and retrieval Audit and compliance QueryGrid and Smart Loader for Hadoop Data Labs selfservice exploration Workload management for SLAs Unity Director select query routing of apps and users Workload management surge controls 50 Copyright Teradata Unity Loader for dual loading Unity Director for automatic failover Workload management for outage SLAs Unity Director for query routing based on data depth

32 CONTEXTUAL ANALYTICS Contextual Analytics Resource Flexibility Always On Corporate memory Deep analytics Data Labs Data refinery Hadoop integration Ad hoc projects Peak workload assist Disaster recovery High availability Archive reporting & retrieval Audit and compliance 51 Copyright Teradata

33 Contextual Analytics Deep Analytics Contextual Analytics xdr analytics > Analyze xdr, and smart phone logs > Calling patterns, fraud, usage patterns Consumer sentiment analytics > Brand and products likes/dislikes Clickstream analytics > Optimize website, digital spend, web site design Sensor/machine analytics > Proactive maintenance, provisioning > Healthcare, telematics, > Utilities (water, electricity, etc.) Location based analytics > Manage operations where they occur 52 Copyright Teradata

34 Contextual Analytics Data Refinery Contextual Analytics Consider 1700 when offloading ELT Benefits > Lower cost system > Little to no ETL rewrite > Continue using favorite transformation tools and scripts > Reference data available for transformations > Preserve security and access rights > Teradata Unity automates data sync ELT offload X Considerations > SLA s for data availability on IDW > System-to-system dependencies > Available CPU resources on IDW 53 Copyright Teradata Integrated Big Data Platform Hadoop

35 Handling Multi-structured data with SQL Store data objects in database > Weblogs, JSON, XML, CSV, etc. > VarChar, CLOB, or BLOB Teradata Data Warehouse Built-in functions > Name value pair functions > String handlers, REGEX > JSONpath operators XML XML :25: Mozilla/5.0(Macintos h; U; Intel weblogs JSON > XML and Xquery Table Operators > Dynamic input schema, output schema > Use C++/Java to unravel complex objects into columns Late-binding flexibility 54 Copyright Teradata

36 RESOURCE FLEXIBILITY Contextual Analytics Resource Flexibility Always On Corporate memory Deep analytics Data Labs Data refinery Hadoop integration Ad hoc projects Peak workload assist Disaster recovery High availability Archive reporting & retrieval Audit and compliance 55 Copyright Teradata

37 Resource Flexibility Ad Hoc Projects Resource Flexibility The Executive Request > New inventory supplier > Urgent marketing campaign > Sales manager challenges numbers > Marketing buys sample social media data > What if projects Fast reaction > Fire disrupts supply chain > Hurricane relief plan > Major competitor action Mergers and acquisitions 56 Copyright Teradata

38 Resource Flexibility Peak Workload Assist Resource Flexibility Load balance prime time user activity > Support subset of users > Common during month end, quarter end, retail Mondays Help meet batch SLAs > Daily batch reports > Month end, quarter end, CFO and sales summaries Value-Add Enabling SW > Unity Director, Loader, Data Mover, Ecosystem Manager > Workload Management 57 Copyright Teradata

39 ALWAYS ON Contextual Analytics Resource Flexibility Always On Corporate memory Deep analytics Data Labs Data refinery Hadoop integration Ad hoc projects Peak workload assist Disaster recovery High availability Archive reporting & retrieval Audit and compliance 58 Copyright Teradata

40 Disaster Recovery Always On Always On Maintain all or a portion of the production IDW for use in a true disaster > Unity Director, Unity Loader, Unity Data Mover, Unity Ecosystem Manager Minimum necessary users and applications > Keep the core business running Teradata Unity 59 Copyright Teradata

41 Always On High Availability Always On Data warehouses are operational, mission-critical systems > Continuous data access to end users Planned maintenance of production warehouse > Software updates > Hardware upgrades Unplanned outages > Hardware or software failures hidden from users > Reduces pressure on IT for system recovery 60 Copyright Teradata

42 CORPORATE MEMORY Contextual Analytics Resource Flexibility Always On Corporate memory Deep analytics Data Labs Data refinery Hadoop integration Ad hoc projects Peak workload assist Disaster recovery High availability Archive reporting & retrieval Audit and compliance 61 Copyright Teradata

43 Corporate Memory Archival Reporting and Retrieval Corporate memory Archival Reporting > Marketing - revisit lost customers > CFO - track fraud back further > Manufacturing - compare parts cost trends > Call center - find old warranties, call logs Self-service query and reporting > Long term trends > Ad hoc historical questions > Small tactical look-ups Reduced dependency on tape files Keep years history Audit and government demands Financial security and trust Equal opportunity employment Fair lending practices Tax audit (ugh) Common requirements 5-10 years of data storage Fast report turn around Trusted data Secure environment Self-service queries 62 Copyright Teradata

44 Performance Comparison Benchmark to sort 1TB of data in 1 minute > This is a very basic benchmark to sort a TB, typical usage with concurrency, joins, mix workloads Teradata will do even better > Hadoop requires 8x the number of nodes to sort 1TB of data in 1 minute Hadoop nodes 206 nodes ~8x servers Another Customer test shows even more impressive results > Query took 1 second on the 1700 vs. 20 minutes on Hadoop Hadoop 90 nodes ~15x nodes servers 63 Copyright Teradata

45 CUSTOMER SUCCESSES 64 Copyright Teradata

46 A/B testing Contextual analytics: join behavior to IDW data Digital investment optimization Hadoop integration Archive reporting and retrieval Dual load Peak workload assist Load refine data Join for image IDW 10PB structured analytics Analyze & Report Singularity 36PB weblogs, IDW copy 65 Copyright Teradata Discover & Explore Hadoop 50PB bot detection, images

47 Large US Credit Card Company Deep history queries Compliance queries BAR / DR Future plan for data load Unity Director Unity Loader ~350TB / 10 nodes each site Regulatory queries, deep history, IDW copy (BAR) 66 Copyright Teradata

48 When to Use Which? You Have Many Choices 67 Copyright Teradata 6700 Hadoop Aster 1700 Structured data X X X Multi-structured X X JSON, XML, weblog ETL Statistics X X X X Interactive Queries X Evolving X X MapReduce X X Graph X X N-Path Predictive analytics In-DB Programmatic SQL-MR In-DB Interactive Performance high low-med med-high med Data Governance high Evolving med-low high Interactive tools All Few All All X

49 THANK YOU TO OUR TUG SPONSOR Trusted supplier to major OEMs for 30 years Joint engineering with Teradata Fully integrated with Teradata nodes and Database New technology > Chromium FX RAID controllers which support 5.2 Gb/s SAS 2.0 > Inde EcoStor technology eliminates the need for cache batteries 68 Copyright Teradata

50 BACKUP SLIDES

51 When to Use Which? Workload Schema Scale Access methods Teradata Appliance for Hadoop 70 Copyright Teradata Integrated Big Data Platform 1700 Batch processing of data at scale. Improving capabilities to support a Hundreds of concurrent users performing interactive analytics. Batch processing small number of interactive users Typically schema can be defined after Typically schema is defined before data is data is stored stored (Native with JSON, XML and Weblog) Can scale to large data volumes at low Can scale to large data volumes at cost moderate or significant cost Data accessed through programs Data accessed through SQL and BI tools created by developers, SQL-like systems, and other methods SQL Flexible programming, evolving SQL ANSI SQL Raw Cleansed (ETL) (Native with JSON, XML and Data Weblog) Access Scans Seeks Complexity Complex processing Complex joins Cost/Efficiency Low cost of storage and processing. Efficient use of CPU/IO Executes on tens to thousands of Very fast response times servers Benefits Parallelization of traditional programming languages (Java, C++, Python, Perl, etc.) Supports higher-level programming frameworks such as Pig and HiveQL Radically changes the economic model for storing high volumes of data Easy to consume data Rationalization of data from multiple sources into a single enterprise view Clean, safe, secure data Cross-functional analysis Transform once, use many

52 Comparing Data Platform Configurations Teradata Appliance for Hadoop Integrated Big Data Platform 1700 Nodes -full rack 18 MPP nodes/cabinet 1+1, 2+1, 3+0 MPP nodes/cabinet Node CPU Storage Total user data capacity Master (Qty. 2): dual 8-core Intel Data (Qty. 16): dual 6-core Intel 192 3TB HDDs/cabinet 152TB/cabinet (9.5 TB/data node uncompressed) Dual 8-core Intel 168 3TB HDDs /cabinet (+6 global hot spares) 229TB/cabinet (114 TB/node uncompressed) Memory Management, troubleshooting and support Availability 256GB per master node 128GB per data node Teradata Vital Infrastructure, Teradata Viewpoint, single source software and hardware support Software data replication Up to 512GB per node Teradata Vital Infrastructure, Teradata Viewpoint, single source software and hardware support Hot standby node available, global hot spare drives Interconnect 40GB InfiniBand 40GB InfiniBand OS SUSE Linux 11 SUSE Linux Copyright Teradata

GO BIG WITH DATA PLATFORMS: HADOOP AND TERADATA

GO BIG WITH DATA PLATFORMS: HADOOP AND TERADATA GO BIG WITH DATA PLATFORMS: HADOOP AND TERADATA Betsy C. Huntingdon Product Marketing Manager May 13, 2014 Columbus, OH Spring Teradata User Group Meetings AGENDA UDA and the Data Platform Teradata Appliance

More information

Teradata s Big Data Technology Strategy & Roadmap

Teradata s Big Data Technology Strategy & Roadmap Teradata s Big Data Technology Strategy & Roadmap Artur Borycki, Director International Solutions Marketing 18 March 2014 Agenda > Introduction and level-set > Enabling the Logical Data Warehouse > Any

More information

BIG DATA: FROM HYPE TO REALITY. Leandro Ruiz Presales Partner for C&LA Teradata

BIG DATA: FROM HYPE TO REALITY. Leandro Ruiz Presales Partner for C&LA Teradata BIG DATA: FROM HYPE TO REALITY Leandro Ruiz Presales Partner for C&LA Teradata Evolution in The Use of Information Action s ACTIVATING MAKE it happen! Insights OPERATIONALIZING WHAT IS happening now? PREDICTING

More information

Artur Borycki. Director International Solutions Marketing

Artur Borycki. Director International Solutions Marketing Artur Borycki Director International Solutions Agenda! Evolution of Teradata s Unified Architecture Analytical and Workloads! Teradata s Reference Information Architecture Evolution of Teradata s" Unified

More information

Investor Presentation. Second Quarter 2015

Investor Presentation. Second Quarter 2015 Investor Presentation Second Quarter 2015 Note to Investors Certain non-gaap financial information regarding operating results may be discussed during this presentation. Reconciliations of the differences

More information

INVESTOR PRESENTATION. First Quarter 2014

INVESTOR PRESENTATION. First Quarter 2014 INVESTOR PRESENTATION First Quarter 2014 Note to Investors Certain non-gaap financial information regarding operating results may be discussed during this presentation. Reconciliations of the differences

More information

Teradata Unified Big Data Architecture

Teradata Unified Big Data Architecture Teradata Unified Big Data Architecture Agenda Recap the challenges of Big Analytics The 2 analytical gaps for most enterprises Teradata Unified Data Architecture - How we bridge the gaps - The 3 core elements

More information

Welcome. Host: Eric Kavanagh. eric.kavanagh@bloorgroup.com. The Briefing Room. Twitter Tag: #briefr

Welcome. Host: Eric Kavanagh. eric.kavanagh@bloorgroup.com. The Briefing Room. Twitter Tag: #briefr The Briefing Room Welcome Host: Eric Kavanagh eric.kavanagh@bloorgroup.com Twitter Tag: #briefr The Briefing Room Mission! Reveal the essential characteristics of enterprise software, good and bad! Provide

More information

End to End Solution to Accelerate Data Warehouse Optimization. Franco Flore Alliance Sales Director - APJ

End to End Solution to Accelerate Data Warehouse Optimization. Franco Flore Alliance Sales Director - APJ End to End Solution to Accelerate Data Warehouse Optimization Franco Flore Alliance Sales Director - APJ Big Data Is Driving Key Business Initiatives Increase profitability, innovation, customer satisfaction,

More information

INVESTOR PRESENTATION. Third Quarter 2014

INVESTOR PRESENTATION. Third Quarter 2014 INVESTOR PRESENTATION Third Quarter 2014 Note to Investors Certain non-gaap financial information regarding operating results may be discussed during this presentation. Reconciliations of the differences

More information

The Future of Data Management

The Future of Data Management The Future of Data Management with Hadoop and the Enterprise Data Hub Amr Awadallah (@awadallah) Cofounder and CTO Cloudera Snapshot Founded 2008, by former employees of Employees Today ~ 800 World Class

More information

Einsatzfelder von IBM PureData Systems und Ihre Vorteile.

Einsatzfelder von IBM PureData Systems und Ihre Vorteile. Einsatzfelder von IBM PureData Systems und Ihre Vorteile demirkaya@de.ibm.com Agenda Information technology challenges PureSystems and PureData introduction PureData for Transactions PureData for Analytics

More information

HDP Hadoop From concept to deployment.

HDP Hadoop From concept to deployment. HDP Hadoop From concept to deployment. Ankur Gupta Senior Solutions Engineer Rackspace: Page 41 27 th Jan 2015 Where are you in your Hadoop Journey? A. Researching our options B. Currently evaluating some

More information

Data Governance in the Hadoop Data Lake. Michael Lang May 2015

Data Governance in the Hadoop Data Lake. Michael Lang May 2015 Data Governance in the Hadoop Data Lake Michael Lang May 2015 Introduction Product Manager for Teradata Loom Joined Teradata as part of acquisition of Revelytix, original developer of Loom VP of Sales

More information

Advanced In-Database Analytics

Advanced In-Database Analytics Advanced In-Database Analytics Tallinn, Sept. 25th, 2012 Mikko-Pekka Bertling, BDM Greenplum EMEA 1 That sounds complicated? 2 Who can tell me how best to solve this 3 What are the main mathematical functions??

More information

Oracle Big Data SQL Technical Update

Oracle Big Data SQL Technical Update Oracle Big Data SQL Technical Update Jean-Pierre Dijcks Oracle Redwood City, CA, USA Keywords: Big Data, Hadoop, NoSQL Databases, Relational Databases, SQL, Security, Performance Introduction This technical

More information

HDP Enabling the Modern Data Architecture

HDP Enabling the Modern Data Architecture HDP Enabling the Modern Data Architecture Herb Cunitz President, Hortonworks Page 1 Hortonworks enables adoption of Apache Hadoop through HDP (Hortonworks Data Platform) Founded in 2011 Original 24 architects,

More information

Implement Hadoop jobs to extract business value from large and varied data sets

Implement Hadoop jobs to extract business value from large and varied data sets Hadoop Development for Big Data Solutions: Hands-On You Will Learn How To: Implement Hadoop jobs to extract business value from large and varied data sets Write, customize and deploy MapReduce jobs to

More information

Getting Started Practical Input For Your Roadmap

Getting Started Practical Input For Your Roadmap Getting Started Practical Input For Your Roadmap Mike Ferguson Managing Director, Intelligent Business Strategies BA4ALL Big Data & Analytics Insight Conference Stockholm, May 2015 About Mike Ferguson

More information

Elasticsearch on Cisco Unified Computing System: Optimizing your UCS infrastructure for Elasticsearch s analytics software stack

Elasticsearch on Cisco Unified Computing System: Optimizing your UCS infrastructure for Elasticsearch s analytics software stack Elasticsearch on Cisco Unified Computing System: Optimizing your UCS infrastructure for Elasticsearch s analytics software stack HIGHLIGHTS Real-Time Results Elasticsearch on Cisco UCS enables a deeper

More information

SAS and Teradata Partnership

SAS and Teradata Partnership SAS and Teradata Partnership Ed Swain Senior Industry Consultant Energy & Resources Ed.Swain@teradata.com 1 Innovation and Leadership Teradata SAS Magic Quadrant for Data Warehouse Database Management

More information

Enable your Modern Data Architecture by delivering Enterprise Apache Hadoop

Enable your Modern Data Architecture by delivering Enterprise Apache Hadoop Modern Data Architecture with Enterprise Apache Hadoop Hortonworks. We do Hadoop. Jeff Markham Technical Director, APAC jmarkham@hortonworks.com Page 1 Our Mission: Enable your Modern Data Architecture

More information

Please give me your feedback

Please give me your feedback Please give me your feedback Session BB4089 Speaker Claude Lorenson, Ph. D and Wendy Harms Use the mobile app to complete a session survey 1. Access My schedule 2. Click on this session 3. Go to Rate &

More information

UNIFY YOUR (BIG) DATA

UNIFY YOUR (BIG) DATA UNIFY YOUR (BIG) DATA ANALYTIC STRATEGY GIVE ANY USER ANY ANALYTIC ON ANY DATA Scott Gnau President, Teradata Labs scott.gnau@teradata.com t Unify Your (Big) Data Analytic Strategy Technology excitement:

More information

Actian SQL in Hadoop Buyer s Guide

Actian SQL in Hadoop Buyer s Guide Actian SQL in Hadoop Buyer s Guide Contents Introduction: Big Data and Hadoop... 3 SQL on Hadoop Benefits... 4 Approaches to SQL on Hadoop... 4 The Top 10 SQL in Hadoop Capabilities... 5 SQL in Hadoop

More information

Using Tableau Software with Hortonworks Data Platform

Using Tableau Software with Hortonworks Data Platform Using Tableau Software with Hortonworks Data Platform September 2013 2013 Hortonworks Inc. http:// Modern businesses need to manage vast amounts of data, and in many cases they have accumulated this data

More information

Microsoft Big Data. Solution Brief

Microsoft Big Data. Solution Brief Microsoft Big Data Solution Brief Contents Introduction... 2 The Microsoft Big Data Solution... 3 Key Benefits... 3 Immersive Insight, Wherever You Are... 3 Connecting with the World s Data... 3 Any Data,

More information

ADVANCED ANALYTICS AND FRAUD DETECTION THE RIGHT TECHNOLOGY FOR NOW AND THE FUTURE

ADVANCED ANALYTICS AND FRAUD DETECTION THE RIGHT TECHNOLOGY FOR NOW AND THE FUTURE ADVANCED ANALYTICS AND FRAUD DETECTION THE RIGHT TECHNOLOGY FOR NOW AND THE FUTURE Big Data Big Data What tax agencies are or will be seeing! Big Data Large and increased data volumes New and emerging

More information

Oracle Big Data Building A Big Data Management System

Oracle Big Data Building A Big Data Management System Oracle Big Building A Big Management System Copyright 2015, Oracle and/or its affiliates. All rights reserved. Effi Psychogiou ECEMEA Big Product Director May, 2015 Safe Harbor Statement The following

More information

Oracle Database 12c Plug In. Switch On. Get SMART.

Oracle Database 12c Plug In. Switch On. Get SMART. Oracle Database 12c Plug In. Switch On. Get SMART. Duncan Harvey Head of Core Technology, Oracle EMEA March 2015 Safe Harbor Statement The following is intended to outline our general product direction.

More information

#TalendSandbox for Big Data

#TalendSandbox for Big Data Evalua&on von Apache Hadoop mit der #TalendSandbox for Big Data Julien Clarysse @whatdoesdatado @talend 2015 Talend Inc. 1 Connecting the Data-Driven Enterprise 2 Talend Overview Founded in 2006 BRAND

More information

Data Warehousing and Analytics Infrastructure at Facebook. Ashish Thusoo & Dhruba Borthakur athusoo,dhruba@facebook.com

Data Warehousing and Analytics Infrastructure at Facebook. Ashish Thusoo & Dhruba Borthakur athusoo,dhruba@facebook.com Data Warehousing and Analytics Infrastructure at Facebook Ashish Thusoo & Dhruba Borthakur athusoo,dhruba@facebook.com Overview Challenges in a Fast Growing & Dynamic Environment Data Flow Architecture,

More information

Modern Data Architecture for Predictive Analytics

Modern Data Architecture for Predictive Analytics Modern Data Architecture for Predictive Analytics David Smith VP Marketing and Community - Revolution Analytics John Kreisa VP Strategic Marketing- Hortonworks Hortonworks Inc. 2013 Page 1 Your Presenters

More information

Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing

Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing Wayne W. Eckerson Director of Research, TechTarget Founder, BI Leadership Forum Business Analytics

More information

Datenverwaltung im Wandel - Building an Enterprise Data Hub with

Datenverwaltung im Wandel - Building an Enterprise Data Hub with Datenverwaltung im Wandel - Building an Enterprise Data Hub with Cloudera Bernard Doering Regional Director, Central EMEA, Cloudera Cloudera Your Hadoop Experts Founded 2008, by former employees of Employees

More information

Platfora Big Data Analytics

Platfora Big Data Analytics Platfora Big Data Analytics ISV Partner Solution Case Study and Cisco Unified Computing System Platfora, the leading enterprise big data analytics platform built natively on Hadoop and Spark, delivers

More information

TERADATA QUERY GRID. Teradata User Group September 2014

TERADATA QUERY GRID. Teradata User Group September 2014 TERADATA QUERY GRID Teradata User Group September 2014 2 9/15/2014 Teradata Confidential Teradata s View Big Data and Data in General DATA enables INSIGHTS which drive ACTIONS to provide BUSINESS ADVANTAGE

More information

AGENDA. What is BIG DATA? What is Hadoop? Why Microsoft? The Microsoft BIG DATA story. Our BIG DATA Roadmap. Hadoop PDW

AGENDA. What is BIG DATA? What is Hadoop? Why Microsoft? The Microsoft BIG DATA story. Our BIG DATA Roadmap. Hadoop PDW AGENDA What is BIG DATA? What is Hadoop? Why Microsoft? The Microsoft BIG DATA story Hadoop PDW Our BIG DATA Roadmap BIG DATA? Volume 59% growth in annual WW information 1.2M Zetabytes (10 21 bytes) this

More information

Big Data Analytics. with EMC Greenplum and Hadoop. Big Data Analytics. Ofir Manor Pre Sales Technical Architect EMC Greenplum

Big Data Analytics. with EMC Greenplum and Hadoop. Big Data Analytics. Ofir Manor Pre Sales Technical Architect EMC Greenplum Big Data Analytics with EMC Greenplum and Hadoop Big Data Analytics with EMC Greenplum and Hadoop Ofir Manor Pre Sales Technical Architect EMC Greenplum 1 Big Data and the Data Warehouse Potential All

More information

Apache Hadoop: The Big Data Refinery

Apache Hadoop: The Big Data Refinery Architecting the Future of Big Data Whitepaper Apache Hadoop: The Big Data Refinery Introduction Big data has become an extremely popular term, due to the well-documented explosion in the amount of data

More information

Introducing Oracle Exalytics In-Memory Machine

Introducing Oracle Exalytics In-Memory Machine Introducing Oracle Exalytics In-Memory Machine Jon Ainsworth Director of Business Development Oracle EMEA Business Analytics 1 Copyright 2011, Oracle and/or its affiliates. All rights Agenda Topics Oracle

More information

HIGH PERFORMANCE ANALYTICS FOR TERADATA

HIGH PERFORMANCE ANALYTICS FOR TERADATA F HIGH PERFORMANCE ANALYTICS FOR TERADATA F F BORN AND BRED IN FINANCIAL SERVICES AND HEALTHCARE. DECADES OF EXPERIENCE IN PARALLEL PROGRAMMING AND ANALYTICS. FOCUSED ON MAKING DATA SCIENCE HIGHLY PERFORMING

More information

Integrated Big Data: Hadoop + DBMS + Discovery for SAS High Performance Analytics

Integrated Big Data: Hadoop + DBMS + Discovery for SAS High Performance Analytics Paper 1828-2014 Integrated Big Data: Hadoop + DBMS + Discovery for SAS High Performance Analytics John Cunningham, Teradata Corporation, Danville, CA ABSTRACT SAS High Performance Analytics (HPA) is a

More information

Ganzheitliches Datenmanagement

Ganzheitliches Datenmanagement Ganzheitliches Datenmanagement für Hadoop Michael Kohs, Senior Sales Consultant @mikchaos The Problem with Big Data Projects in 2016 Relational, Mainframe Documents and Emails Data Modeler Data Scientist

More information

Offload Historical Data to Big Data Lake. Ample White Paper

Offload Historical Data to Big Data Lake. Ample White Paper Offload Historical Data to Big Data Lake The Need to Offload Historical Data for Compliance Queries How often have heard that the legal or compliance department group needs to have access to your company

More information

Data Warehouse as a Service. Lot 2 - Platform as a Service. Version: 1.1, Issue Date: 05/02/2014. Classification: Open

Data Warehouse as a Service. Lot 2 - Platform as a Service. Version: 1.1, Issue Date: 05/02/2014. Classification: Open Data Warehouse as a Service Version: 1.1, Issue Date: 05/02/2014 Classification: Open Classification: Open ii MDS Technologies Ltd 2014. Other than for the sole purpose of evaluating this Response, no

More information

The Future of Data Management with Hadoop and the Enterprise Data Hub

The Future of Data Management with Hadoop and the Enterprise Data Hub The Future of Data Management with Hadoop and the Enterprise Data Hub Amr Awadallah Cofounder & CTO, Cloudera, Inc. Twitter: @awadallah 1 2 Cloudera Snapshot Founded 2008, by former employees of Employees

More information

Luncheon Webinar Series May 13, 2013

Luncheon Webinar Series May 13, 2013 Luncheon Webinar Series May 13, 2013 InfoSphere DataStage is Big Data Integration Sponsored By: Presented by : Tony Curcio, InfoSphere Product Management 0 InfoSphere DataStage is Big Data Integration

More information

Navigating the Big Data infrastructure layer Helena Schwenk

Navigating the Big Data infrastructure layer Helena Schwenk mwd a d v i s o r s Navigating the Big Data infrastructure layer Helena Schwenk A special report prepared for Actuate May 2013 This report is the second in a series of four and focuses principally on explaining

More information

Big Data must become a first class citizen in the enterprise

Big Data must become a first class citizen in the enterprise Big Data must become a first class citizen in the enterprise An Ovum white paper for Cloudera Publication Date: 14 January 2014 Author: Tony Baer SUMMARY Catalyst Ovum view Big Data analytics have caught

More information

Microsoft Analytics Platform System. Solution Brief

Microsoft Analytics Platform System. Solution Brief Microsoft Analytics Platform System Solution Brief Contents 4 Introduction 4 Microsoft Analytics Platform System 5 Enterprise-ready Big Data 7 Next-generation performance at scale 10 Engineered for optimal

More information

Safe Harbor Statement

Safe Harbor Statement Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment

More information

Hadoop in the Hybrid Cloud

Hadoop in the Hybrid Cloud Presented by Hortonworks and Microsoft Introduction An increasing number of enterprises are either currently using or are planning to use cloud deployment models to expand their IT infrastructure. Big

More information

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Managing Big Data with Hadoop & Vertica A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Copyright Vertica Systems, Inc. October 2009 Cloudera and Vertica

More information

Oracle Big Data Handbook

Oracle Big Data Handbook ORACLG Oracle Press Oracle Big Data Handbook Tom Plunkett Brian Macdonald Bruce Nelson Helen Sun Khader Mohiuddin Debra L. Harding David Segleau Gokula Mishra Mark F. Hornick Robert Stackowiak Keith Laker

More information

Big Data Realities Hadoop in the Enterprise Architecture

Big Data Realities Hadoop in the Enterprise Architecture Big Data Realities Hadoop in the Enterprise Architecture Paul Phillips Director, EMEA, Hortonworks pphillips@hortonworks.com +44 (0)777 444 3857 Hortonworks Inc. 2012 Page 1 Agenda The Growth of Enterprise

More information

Big Data: Making Sense of it all!

Big Data: Making Sense of it all! Big Data: Making Sense of it all! Jamie Engesser E-mail : jamie@hortonworks.com Page 1 Data Driven Business? Facts not Intuition! Data driven decisions are better decisions its as simple as that. Using

More information

W H I T E P A P E R. Deriving Intelligence from Large Data Using Hadoop and Applying Analytics. Abstract

W H I T E P A P E R. Deriving Intelligence from Large Data Using Hadoop and Applying Analytics. Abstract W H I T E P A P E R Deriving Intelligence from Large Data Using Hadoop and Applying Analytics Abstract This white paper is focused on discussing the challenges facing large scale data processing and the

More information

Big Data and Your Data Warehouse Philip Russom

Big Data and Your Data Warehouse Philip Russom Big Data and Your Data Warehouse Philip Russom TDWI Research Director for Data Management May 7, 2013 Sponsor Speakers Philip Russom TDWI Research Director, Data Management Chris Twogood VP, Product and

More information

ORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION

ORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION ORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION EXECUTIVE SUMMARY Oracle business intelligence solutions are complete, open, and integrated. Key components of Oracle business intelligence

More information

BIG DATA ANALYTICS REFERENCE ARCHITECTURES AND CASE STUDIES

BIG DATA ANALYTICS REFERENCE ARCHITECTURES AND CASE STUDIES BIG DATA ANALYTICS REFERENCE ARCHITECTURES AND CASE STUDIES Relational vs. Non-Relational Architecture Relational Non-Relational Rational Predictable Traditional Agile Flexible Modern 2 Agenda Big Data

More information

Hadoop Introduction. Olivier Renault Solution Engineer - Hortonworks

Hadoop Introduction. Olivier Renault Solution Engineer - Hortonworks Hadoop Introduction Olivier Renault Solution Engineer - Hortonworks Hortonworks A Brief History of Apache Hadoop Apache Project Established Yahoo! begins to Operate at scale Hortonworks Data Platform 2013

More information

Using Hadoop, Cloud and Tiered Storage For Peak Performance

Using Hadoop, Cloud and Tiered Storage For Peak Performance Using Hadoop, Cloud and Tiered Storage For Peak Performance Presented by: David Gorbet, Vice President, Engineering, MarkLogic Corporation AGILITY SLIDE: 2 Local Disk SAN NAS SLIDE: 3 TIERED STORAGE ELASTICITY

More information

Integrating Cloudera and SAP HANA

Integrating Cloudera and SAP HANA Integrating Cloudera and SAP HANA Version: 103 Table of Contents Introduction/Executive Summary 4 Overview of Cloudera Enterprise 4 Data Access 5 Apache Hive 5 Data Processing 5 Data Integration 5 Partner

More information

HADOOP SOLUTION USING EMC ISILON AND CLOUDERA ENTERPRISE Efficient, Flexible In-Place Hadoop Analytics

HADOOP SOLUTION USING EMC ISILON AND CLOUDERA ENTERPRISE Efficient, Flexible In-Place Hadoop Analytics HADOOP SOLUTION USING EMC ISILON AND CLOUDERA ENTERPRISE Efficient, Flexible In-Place Hadoop Analytics ESSENTIALS EMC ISILON Use the industry's first and only scale-out NAS solution with native Hadoop

More information

James Serra Sr BI Architect JamesSerra3@gmail.com http://jamesserra.com/

James Serra Sr BI Architect JamesSerra3@gmail.com http://jamesserra.com/ James Serra Sr BI Architect JamesSerra3@gmail.com http://jamesserra.com/ Our Focus: Microsoft Pure-Play Data Warehousing & Business Intelligence Partner Our Customers: Our Reputation: "B.I. Voyage came

More information

GAIN BETTER INSIGHT FROM BIG DATA USING JBOSS DATA VIRTUALIZATION

GAIN BETTER INSIGHT FROM BIG DATA USING JBOSS DATA VIRTUALIZATION GAIN BETTER INSIGHT FROM BIG DATA USING JBOSS DATA VIRTUALIZATION Syed Rasheed Solution Manager Red Hat Corp. Kenny Peeples Technical Manager Red Hat Corp. Kimberly Palko Product Manager Red Hat Corp.

More information

Apache Hadoop's Role in Your Big Data Architecture

Apache Hadoop's Role in Your Big Data Architecture Apache Hadoop's Role in Your Big Data Architecture Chris Harris EMEA, Hortonworks charris@hortonworks.com Twi

More information

Capitalize on Big Data for Competitive Advantage with Bedrock TM, an integrated Management Platform for Hadoop Data Lakes

Capitalize on Big Data for Competitive Advantage with Bedrock TM, an integrated Management Platform for Hadoop Data Lakes Capitalize on Big Data for Competitive Advantage with Bedrock TM, an integrated Management Platform for Hadoop Data Lakes Highly competitive enterprises are increasingly finding ways to maximize and accelerate

More information

Oracle s Big Data solutions. Roger Wullschleger.

Oracle s Big Data solutions. Roger Wullschleger. <Insert Picture Here> s Big Data solutions Roger Wullschleger DBTA Workshop on Big Data, Cloud Data Management and NoSQL 10. October 2012, Stade de Suisse, Berne 1 The following is intended to outline

More information

BIG DATA TECHNOLOGY. Hadoop Ecosystem

BIG DATA TECHNOLOGY. Hadoop Ecosystem BIG DATA TECHNOLOGY Hadoop Ecosystem Agenda Background What is Big Data Solution Objective Introduction to Hadoop Hadoop Ecosystem Hybrid EDW Model Predictive Analysis using Hadoop Conclusion What is Big

More information

Bringing Big Data to People

Bringing Big Data to People Bringing Big Data to People Microsoft s modern data platform SQL Server 2014 Analytics Platform System Microsoft Azure HDInsight Data Platform Everyone should have access to the data they need. Process

More information

The Enterprise Data Hub and The Modern Information Architecture

The Enterprise Data Hub and The Modern Information Architecture The Enterprise Data Hub and The Modern Information Architecture Dr. Amr Awadallah CTO & Co-Founder, Cloudera Twitter: @awadallah 1 2013 Cloudera, Inc. All rights reserved. Cloudera Overview The Leader

More information

Well packaged sets of preinstalled, integrated, and optimized software on select hardware in the form of engineered systems and appliances

Well packaged sets of preinstalled, integrated, and optimized software on select hardware in the form of engineered systems and appliances INSIGHT Oracle's All- Out Assault on the Big Data Market: Offering Hadoop, R, Cubes, and Scalable IMDB in Familiar Packages Carl W. Olofson IDC OPINION Global Headquarters: 5 Speen Street Framingham, MA

More information

Building Your Big Data Team

Building Your Big Data Team Building Your Big Data Team With all the buzz around Big Data, many companies have decided they need some sort of Big Data initiative in place to stay current with modern data management requirements.

More information

III Big Data Technologies

III Big Data Technologies III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution

More information

Oracle Database - Engineered for Innovation. Sedat Zencirci Teknoloji Satış Danışmanlığı Direktörü Türkiye ve Orta Asya

Oracle Database - Engineered for Innovation. Sedat Zencirci Teknoloji Satış Danışmanlığı Direktörü Türkiye ve Orta Asya Oracle Database - Engineered for Innovation Sedat Zencirci Teknoloji Satış Danışmanlığı Direktörü Türkiye ve Orta Asya Oracle Database 11g Release 2 Shipping since September 2009 11.2.0.3 Patch Set now

More information

Big Data

<Insert Picture Here> Big Data Big Data Kevin Kalmbach Principal Sales Consultant, Public Sector Engineered Systems Program Agenda What is Big Data and why it is important? What is your Big

More information

Data Refinery with Big Data Aspects

Data Refinery with Big Data Aspects International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 655-662 International Research Publications House http://www. irphouse.com /ijict.htm Data

More information

Integrating a Big Data Platform into Government:

Integrating a Big Data Platform into Government: Integrating a Big Data Platform into Government: Drive Better Decisions for Policy and Program Outcomes John Haddad, Senior Director Product Marketing, Informatica Digital Government Institute s Government

More information

SAS and Oracle: Big Data and Cloud Partnering Innovation Targets the Third Platform

SAS and Oracle: Big Data and Cloud Partnering Innovation Targets the Third Platform SAS and Oracle: Big Data and Cloud Partnering Innovation Targets the Third Platform David Lawler, Oracle Senior Vice President, Product Management and Strategy Paul Kent, SAS Vice President, Big Data What

More information

OWB Users, Enter The New ODI World

OWB Users, Enter The New ODI World OWB Users, Enter The New ODI World Kulvinder Hari Oracle Introduction Oracle Data Integrator (ODI) is a best-of-breed data integration platform focused on fast bulk data movement and handling complex data

More information

Big Data Big Deal for Public Sector Organizations

Big Data Big Deal for Public Sector Organizations Big Data Big Deal for Public Sector Organizations Hoàng Xuân Hiếu Director, FAB & Government Business Indochina & Myanmar 1 Copyright 2013, Oracle and/or its affiliates. All rights reserved. The following

More information

Extend your analytic capabilities with SAP Predictive Analysis

Extend your analytic capabilities with SAP Predictive Analysis September 9 11, 2013 Anaheim, California Extend your analytic capabilities with SAP Predictive Analysis Charles Gadalla Learning Points Advanced analytics strategy at SAP Simplifying predictive analytics

More information

Beyond Lambda - how to get from logical to physical. Artur Borycki, Director International Technology & Innovations

Beyond Lambda - how to get from logical to physical. Artur Borycki, Director International Technology & Innovations Beyond Lambda - how to get from logical to physical Artur Borycki, Director International Technology & Innovations Simplification & Efficiency Teradata believe in the principles of self-service, automation

More information

MDM for the Enterprise: Complementing and extending your Active Data Warehousing strategy. Satish Krishnaswamy VP MDM Solutions - Teradata

MDM for the Enterprise: Complementing and extending your Active Data Warehousing strategy. Satish Krishnaswamy VP MDM Solutions - Teradata MDM for the Enterprise: Complementing and extending your Active Data Warehousing strategy Satish Krishnaswamy VP MDM Solutions - Teradata 2 Agenda MDM and its importance Linking to the Active Data Warehousing

More information

Tap into Hadoop and Other No SQL Sources

Tap into Hadoop and Other No SQL Sources Tap into Hadoop and Other No SQL Sources Presented by: Trishla Maru What is Big Data really? The Three Vs of Big Data According to Gartner Volume Volume Orders of magnitude bigger than conventional data

More information

Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012. Viswa Sharma Solutions Architect Tata Consultancy Services

Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012. Viswa Sharma Solutions Architect Tata Consultancy Services Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012 Viswa Sharma Solutions Architect Tata Consultancy Services 1 Agenda What is Hadoop Why Hadoop? The Net Generation is here Sizing the

More information

Eric Ledu, The Createch Group, a BELL company

Eric Ledu, The Createch Group, a BELL company Eric Ledu, The Createch Group, a BELL company Intelligence Analytics maturity Past Present Future Predictive Modeling Optimization What is the best that could happen? Raw Data Cleaned Data Standard Reports

More information

Parallel Data Warehouse

Parallel Data Warehouse MICROSOFT S ANALYTICS SOLUTIONS WITH PARALLEL DATA WAREHOUSE Parallel Data Warehouse Stefan Cronjaeger Microsoft May 2013 AGENDA PDW overview Columnstore and Big Data Business Intellignece Project Ability

More information

Conquering Big Data Analytics with SAS, Teradata and Hadoop

Conquering Big Data Analytics with SAS, Teradata and Hadoop Paper BI15-2014 Conquering Big Data Analytics with SAS, Teradata and Hadoop John Cunningham, Teradata Corporation, Danville, California Tho Nguyen, Teradata Corporation, Raleigh, North Carolina Paul Segal,

More information

SAP and Hortonworks Reference Architecture

SAP and Hortonworks Reference Architecture SAP and Hortonworks Reference Architecture Hortonworks. We Do Hadoop. June Page 1 2014 Hortonworks Inc. 2011 2014. All Rights Reserved A Modern Data Architecture With SAP DATA SYSTEMS APPLICATIO NS Statistical

More information

CERULIUM TERADATA COURSE CATALOG

CERULIUM TERADATA COURSE CATALOG CERULIUM TERADATA COURSE CATALOG Cerulium Corporation has provided quality Teradata education and consulting expertise for over seven years. We offer customized solutions to maximize your warehouse. Prepared

More information

Executive Summary... 2 Introduction... 3. Defining Big Data... 3. The Importance of Big Data... 4 Building a Big Data Platform...

Executive Summary... 2 Introduction... 3. Defining Big Data... 3. The Importance of Big Data... 4 Building a Big Data Platform... Executive Summary... 2 Introduction... 3 Defining Big Data... 3 The Importance of Big Data... 4 Building a Big Data Platform... 5 Infrastructure Requirements... 5 Solution Spectrum... 6 Oracle s Big Data

More information

The Evolving Apache Hadoop Eco-System

The Evolving Apache Hadoop Eco-System The Evolving Apache Hadoop Eco-System What it means for Big Data Analytics and Storage Sanjay Radia Architect/Founder, Hortonworks Inc. All Rights Reserved Page 1 Outline Hadoop and Big Data Analytics

More information

A Modern Data Architecture with Apache Hadoop

A Modern Data Architecture with Apache Hadoop Modern Data Architecture with Apache Hadoop Talend Big Data Presented by Hortonworks and Talend Executive Summary Apache Hadoop didn t disrupt the datacenter, the data did. Shortly after Corporate IT functions

More information

Cost-Effective Business Intelligence with Red Hat and Open Source

Cost-Effective Business Intelligence with Red Hat and Open Source Cost-Effective Business Intelligence with Red Hat and Open Source Sherman Wood Director, Business Intelligence, Jaspersoft September 3, 2009 1 Agenda Introductions Quick survey What is BI?: reporting,

More information

An Integrated Analytics & Big Data Infrastructure September 21, 2012 Robert Stackowiak, Vice President Data Systems Architecture Oracle Enterprise

An Integrated Analytics & Big Data Infrastructure September 21, 2012 Robert Stackowiak, Vice President Data Systems Architecture Oracle Enterprise An Integrated Analytics & Big Data Infrastructure September 21, 2012 Robert Stackowiak, Vice President Data Systems Architecture Oracle Enterprise Solutions Group The following is intended to outline our

More information

Optimized for the Industrial Internet: GE s Industrial Data Lake Platform

Optimized for the Industrial Internet: GE s Industrial Data Lake Platform Optimized for the Industrial Internet: GE s Industrial Lake Platform Agenda The Opportunity The Solution The Challenges The Results Solutions for Industrial Internet, deep domain expertise 2 GESoftware.com

More information

Leveraging Machine Data to Deliver New Insights for Business Analytics

Leveraging Machine Data to Deliver New Insights for Business Analytics Copyright 2015 Splunk Inc. Leveraging Machine Data to Deliver New Insights for Business Analytics Rahul Deshmukh Director, Solutions Marketing Jason Fedota Regional Sales Manager Safe Harbor Statement

More information