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BARC T2: Analytische Plattformen von IBM, Microsoft, Oracle, SAP und SAS im Vergleich München, 23.06.2015 Otto Görlich, Senior Analyst Timm Grosser, Senior Analyst Business Application Research Center (BARC) B A 140 Mitarbeiter, davon 80 Analysten an 17 Standorten in acht Ländern R Portfolio aus Research, Beratung und Weiterbildung C 29.06.2015 Europas führendes IT-Analysten- und -Beratungshaus für Business Software und IT Services (Analystengruppe CXP / PAC / BARC) Themen: Business Intelligence, Big Data, Datenmanagement, Customer Relationship Management, Enterprise Content Management, IT-Management, HR, Finance, ERP, IT Sourcing und IT Services © BARC 2015 2 BARC: Expertise für datengetriebene Unternehmen Beratung Strategie & Organisation Prozesse & ITArchitektur Softwareauswahl Data Science Weiterbildung Konferenzen Seminare Kamingespräche Expertenworkshops 29.06.2015 Datengetriebene Unternehmen Research Produktvergleiche Marktforschung BI Manager © BARC 2015 3 Was BARC von Systemintegratoren unterscheidet • • • • • • • Wir sind Experten für die Nutzung von Daten und Analytik zugunsten optimierter Kerngeschäfts- und Entscheidungsprozesse in Unternehmen. Die Synergie unserer betriebswirtschaftlichen und technologischen Erfahrungen befähigt uns in besonderem Maße, die Zusammenarbeit zwischen Fachbereichen und IT zu unterstützen und zu fördern. Unsere Architekturempfehlungen setzen auf Ihre konkreten, unternehmensindividuellen Anforderungen auf und folgen nicht dogmatischen Philosophien. Wir haben kein Interesse an Implementierungsprojekten, sondern konzentrieren uns auf Strategie- und Technologiearchitekturfragen. Wir arbeiten mit absolut technologieneutralen Methoden und Best Practices, die Unabhängigkeit von einzelnen Herstellern und Werkzeugen ihren Vorgehensweisen/Experten sicherstellen. Allein im BI & DM Markt kennen wir 250 Anbieter mit 600 Werkzeugen und aggregieren Erfahrungen von mehr als 1000 Anwendern. Unser Coaching-Ansatz vermittelt Hilfe zur Selbsthilfe anstatt ServiceAbhängigkeit. 29.06.2015 © BARC 2015 5 Agenda Analytische Plattformen im Vergleich • Marktentwicklung und -segmentierung • Analytische Plattformen im Vergleich Marktzahlen BI und Datenmanagement 29.06.2015 Hadoop-Survey: www.barc.de/umfrage © BARC 2015 7 BI-Angebot in Deutschland Anzahl Anbieter und Produkte auf dem deutschen BI-Markt 600 500 Anzahl 400 Produkte Anbieter 300 200 100 0 2007 2008 2009 2010 Jahr 2011 2012 2013 Quelle: BARC Guide BI 2013/2014 29.06.2015 © BARC 2014 8 Marktwachstum Umsatz in Millionen Euro Marktvolumen Business Intelligence Software Deutschland 2006-2013 + 11 % CAGR + 11%/Jahr + 13 % + 11 % +9% + 15 % 807 +8% + 10 % 1455 1314 1164 1051 963 874 703 2006 2007 2008 2009 2010 2011 2012 2013 Quelle: BARC, Der Markt für Business Intelligence in Deutschland 2013 29.06.2015 © BARC 2014 9 Verteilung Backend-Frontend Lizenz- und Wartungsumsätze in Mio. € DatenmanagementWerkzeuge 722 733 (50%) (50%) Anwenderwerkzeuge Quelle: BARC, Der Markt für Business Intelligence in Deutschland 2013 29.06.2015 © BARC 2014 10 Durchschnittliches Wachstum 2012-2013 Durchschnittliches Wachstum (pro Anbieter, nicht auf Umsatz gewichtet) 25% 20% Durchschnittl. ungew. Wachstum der Top 60 Anbieter: 16% 15% 19,6% 19,8% Top 21 - 30 Top 31 - 60 16,8% 10% 5% 7,9% 9,2% 0% Top 5 Top 6 - 10 Top 11 - 20 Anbieter Quelle: BARC, Der Markt für Business Intelligence in Deutschland 2013 29.06.2015 © BARC 2014 11 Marktstruktur 16% 12% 261 Anbieter 201 66% 50 5 Gesamtumsatz BI Software 2013: 1,46 Mrd. € 6% Kleinanbieter (Platz 61-261) Mittelfeld (Platz 11-60) Herausforderer (Platz 6-10) Top 5 Anbieter 5 Anzahl Anbieter Umsatz in % Quelle: BARC, Der Markt für Business Intelligence in Deutschland 2013 29.06.2015 © BARC 2014 12 Marktkonzentration Top 5 und Top 10 67% Marktanteil 62% 55% 70% 58% 74% 76% 75% 78% 75% 66% 60% 61% 60% 61% 42% Top 10 Anbieter Top 5 Anbieter 2006 2007 2008 2009 2010 2011 2012 2013 Quelle: BARC, Der Markt für Business Intelligence in Deutschland 2013 29.06.2015 © BARC 2014 13 Business-Intelligence-Gesamtmarkt Anbieter Deutschland 2013 Platz 1-10 Rang Firma BI & DM SoftwareUmsatz 2013 (in Mio. €) Veränderung zu 2012 Marktanteil in Prozent 1 SAP 371,0 22% 25,5% 2 Oracle 180,0 6% 12,4% 3 Microsoft 154,2 16% 10,6% 4 IBM 142,2 5% 9,8% 5 SAS 109,5 -10% 7,5% 6 Informatica 51,0 13% 3,5% 7 Qlik 35,0 19% 2,4% 8 MicroStrategy 34,0 3% 2,3% 9 Teradata 30,1 -5% 2,1% 10 Software AG/ IDS Scheer 19,2 16% 1,3% Quelle: BARC, Der Markt für Business Intelligence in Deutschland 2013 Der Softwaremarkt für Datenmanagement Die guten, alten Zeiten… vielversprechende Anbieter tauchen auf…. und gut subventionierte und schnell wachsende Herausforderer. 15 © BARC 2014 15 Date Investments im Markt für BI und DM Investments in Mio. US$ 2013 2014 BI BI SaaS Advanced Analytics CPM 474 75 87 102 300 40 187 163 Big Data Analytics BI total 39 776 50 739 NoSQL Hadoop Data Integration/DM 213 104 113 166 438 103 ADB DM total 13 443 35 742 1,219 1,481 BI/Big Data Invest 29.06.2015 2015 2-2015 1-2015 2014 12-2014 12-2014 12-2014 11-2014 11-2014 10-2014 10-2014 10-2014 9-2014 5-2014 9-2014 8-2014 8-2014 8-2014 8-2014 7-2014 7-2014 7-2014 6-2014 6-2014 6-2014 6-2014 6-2014 6-2014 5-2014 5-2014 5-2014 5-2014 5-2014 5-2014 4-2014 4-2014 4-2014 3-2014 3-2014 3-2014 3-2014 3-2014 2-2014 2-2014 2-2014 1-2014 1-2014 1-2014 Company Invest (M US$) Total Segment Invest Action Country 20.0 RapidMiner Neo Technologies Blue Yonder Predictix Metric Insight HostAnalytics InsightSquared SnapLogic Map-D Alteryx DataStax Context Relevant GoodData Guavus Splice Machine Glassbeam ICharts Hortonworks SnapLogic Tagetik MapR Couchbase ThougthSpot Visier Concurrent SiSense Trifacta Tamr Anaplan Sumo Logic Context Relevant App Annie Rosslyn Analytics Decisyon SnapLogic Cirro Cloudera Cloudera ClearStory Data Hortonworks WhereScape Splice Machine Domo Chartio MemSQL Contiamo 15.0 20.0 1,480.5 75.0 15.0 21.0 25.0 13.5 20.0 1.5 60.0 106.0 13.5 25.7 20.0 3.0 2.0 4.3 50.0 2.0 38.0 110.0 60.0 30.0 25.5 10.0 30.0 25.0 16.0 100.0 30.0 21.0 17.0 16.8 22.0 2.0 1.0 740 160.0 21.0 100.0 10.0 15.0 125.0 2.2 35.0 0.5 20AA VC 44.1NoSQL VC 75AA 15AA 21BI 86CPM 27BI 59DI 1.5BI 78AA 190NoSQL 44.3AA 101.2BI SaaS 99BDA 22Hadoop 5AA 10.8BI 248Hadoop 39DI CPM 174Hadoop 115NoSQL 40.7BI 46.5BI 14.9DM 44BI 41.3DI 16DI 144CPM 75BDA 30.8AA 39BI 21.6DI 45BI 37DI 9DI 1,040Hadoop 300Hadoop 30BI 198Hadoop DM 19Hadoop 249BI 7BI 45ADB 1BISaaS PE VC VC VC VC VC VC VC VC VC VC VC VC VC VC VC VC VC VC+Debt VC VC VC VC VC VC VC VC VC VC VC IPO VC VC VC Equity VC VC VC VC VC VC VC VC VC US US D US US US US US US US US US US US US US US US US I US US US US US ISR US US US US US US UK I / US US US US US US US NZ US US US US D Big Data Technologien verschiedener Architekturumgebungen Kerngeschäftsprozesse: ERP, CRM, SCM, „OLTP“ … Klassische BI: Reporting, Dashboards, OLAP Operative BI Explorative BI: Fortgeschrittene Analyse, erweiterter Datenhaushalt Taktische BI Data Mart DP Services CEP ADB Explorative BI SSBIData Data Mart Data Mart Expl. SSBI/DI Mart ADB Data Distribution Conformed Dimensions Conformed Facts Suchindex Data Distribution Data Warehouse Streaming IQ Services NoSQL ADB Hadoop Hadoop NoSQL Data Integration Staging Area Extraction ADB, Hadoop, Virtualisierung, DI Services ERP SCM CRM Strukturierte Geschäftsdaten 29.06.2015 Externe Systeme Sensor-Daten Web Logs Maschinengeneriert (strukt.) Social Media Dokumente Menschgeneriert (polystr.) © BARC 2015 17 Geplanter Einsatz von (Big Data) Technologien nimmt zu! Standard Relationale Datenbanken 91% Multidimensionale Datenbanken 66% Standard-Datenintegrationswerkzeuge 5% 7% 57% Individualentwicklung 5% 8% 53% 42% 7% Stand-alone Self-Service-BI-Lösungen 42% 9% Big Data Appliances NoSQL-Datenbanken Big-Data-Analyse-Anwendungen Streaming-Datenbanken 22% 7% 12% 4% 8% 4% 36% 15% 34% 16% 55% 65% 76% 74% 78% 18% Automatisiertes Data Warehousing 4 2 7% Im Einsatz 14% 15% 6%3% 13% Hadoop-Ökosystem 6% 7% 40% 20% 10% 4% 10% 22% 30% 4%3% Analytische Datenbanken Werkzeuge für die Datenvirtualisierung 1 2 5 Geplant innerhalb von 12 Monaten 69% 87% Langfristig geplant Nicht geplant Quelle: BARC Survey Datenmanagement im Wandel, n=337 29.06.2015 © BARC 2015 18 Technologiekarte – Überblick Werkzeugklassen der analytischen Infrastruktur 29.06.2015 © BARC 2015 19 Worin liegen für Sie heute die wesentlichen Aufgaben der Datenintegration? Klassisches ETL 84% Sicherstellung der Datenqualität 67% Abbildung des Single Point of Truth 54% Integration vieler unterschiedlicher Datenquellen 49% Datensicherheit 46% Aufbau/Verwaltung von Berechnungslogiken 44% Unterstützung der Kollaboration zwischen… 33% Kontrolle/Dokumentation der… 31% Sicherstellung der fachlichen/technischen… 21% Information Lifecycle Management 19% Bereitstellung von Echtzeitdaten 14% Schnelle Ad-hoc-Integration von Daten 14% Befähigung der Fachbereiche für Aufgaben der… Integration polystrukturierter Daten 13% 11% Quelle: BARC Survey Datenmanagement im Wandel, n=307 Worin liegen für Sie heute die wesentlichen Aufgaben der Datenintegration? 29.06.2015 © BARC 2015 20 Worin liegen für Sie zukünftig die wesentlichen Aufgaben der Datenintegration? Sicherstellung der Datenqualität 52% Integration vieler unterschiedlicher Datenquellen 52% Bereitstellung von Echtzeitdaten 52% Kontrolle/Dokumentation der Daten/datenverändernden… 50% Schnelle Ad-hoc-Integration von Daten 49% Integration polystrukturierter Daten 48% Abbildung des Single Point of Truth 47% Information Lifecycle Management 43% Unterstützung der Kollaboration zwischen Fachbereich… 41% Sicherstellung der fachlichen/technischen Skalierbarkeit… 40% Aufbau/Verwaltung von Berechnungslogiken 37% Befähigung der Fachbereiche für Aufgaben der… 37% Datensicherheit Klassisches ETL 36% 30% Quelle: BARC Survey Datenmanagement im Wandel, n=307 29.06.2015 © BARC 2015 21 Leistungsspektrum moderner Datenintegrationsplattformen im Zeitalter der Digitalisierung Drehscheibe Konsistenz durch Standardisierung und Wiederverwendbarkeit Methodenvielfalt Unterstützung DI und erweiterter Funktionen Produktivitätssteigerung Performance & Skalierbarkeit Datenschutz und Sicherheit Unterstützung erw. Data Governance Funktionen Sicherstellung Betrieb 29.06.2015 © BARC 2015 22 Anhaltender Trend hin zu Datenintegrationsplattformen – Markt ist relativ starr mit wenig Marktneueintritten und -austritten Follower Konnektor (DQ- / DI-) Spezialist Leader Integrationsplattform Funktionalität und Komplexität nimmt vom Konnektor hin zur Plattform zu Folie beinhaltet Auszug relevanter Hersteller von Datenintegrationswerkzeugen 29.06.2015 © BARC 2015 23 Funktionsabdeckung ausgewählter Lösungsangebote (Auszug) ETL ELT EAI EII DQM MDM MeDM Ab Initio X X X X X X X Cubeware X Composite X Denodo X IBM X X X X X X X Informatica X X X X X X X Microsoft X X X X X X X X X X X Oracle Pentaho X X Pervasive X X SAP X X SAS X X Syncsort X Talend X Theobald X X X X X X X X X X X X X X X X Legende: ETL (Extract-Transform-Load), ELT (Extract-Load-Transform), EAI (Enterprise Application Inegration), EII (Enterprise Information Integration), DQM (Data Quality Management), MDM (Master Data Management), MeDM (Metadata Management) 29.06.2015 © BARC 2015 24 Technologiekarte – Überblick Werkzeugklassen der analytischen Infrastruktur 29.06.2015 © BARC 2015 25 Analytische Datenbanken 29.06.2015 © BARC 2015 26 Trends im Markt analytischer Datenbanken • Technologische Innovationen • Hardware • Prozessoren • Speicher(-hierarchie) • Software • • Verschiedene Ausprägungen • Speicherform • Speicherort • Lieferform • Architektur Speicherformen Speicherorte Lieferformen für analytische Datenbanken für analytische Datenbanken für analytische Datenbanken Relational, zeilenorientiert Festplatte Relational, spaltenorientiert Solid State Disks Software Appliance Multidimensional RAM Sonstige Prozessor-Cache optimiert (Objekt-relational, Assoziativ, Datei, Streaming) Data as a Service Architekturen Single-Node SMP Cluster SMP-Cluster SMP2tierCluster MPP-symetr. MPP-asymetr. „Einer für alles“ versus „aufgabenspezifisch optimiert“ 29.06.2015 © BARC 2015 27 Marktübersicht Analytische Datenbanken Where stored Vendor/DB-Engine Disk (HDD/SSD) Actian Vectorwise Calpont InfiniDB EMC Greenplum (Pivotal) Exasol ExaSolution HP Vertica Analytics Platform IBM DB2 (PureData Op. Analytics) IBM Netezza (PureData Analytics) InfoBright Kognitio WX2 Microsoft SQL Server / PDW Oracle Database / Exadata ParAccel Analytic Platform (Actian) ParStream SAND Analytics Server SAP HANA Sybase IQ Teradata x x x (x) x x x x (x) x x x x x (x) x x How provided InMemory Software Appliance How stored DaaS Architecture Column Row SMP MPP x x x x x x (BLU) (x) x x x x x x x x A S S S A (RAM) x x (BLU) x (x) (x) x x x x x x x x x x x x x x x (x) x (x) x x x x x x x x x (x) x x x x x x x x x x x x (x) x x x (x) x x x x x x x x x (X) x x C/S 2TC x x x x C x S x x x x S S Auszug BARC-Studie Analytische Datenbanken 2013 X= ja 29.06.2015 C=SMP Cluster 2TC=2-tier-Cluster S=symetrisch A=asymetrisch © BARC 2015 28 Schnellere Abfrageperformance durch den Einsatz Analytischer Datenbank als Data Warehouse oder Data Mart Reporting & Analyse Junge versus reife ADBs – wichtige Punkte: ? Analytische Datenbanken? • • • • • • • ? ? Datenaufbereitung Staging Area ERP SCM CRM Externe Systeme Abfrage-Leistungen Schreibe-Leistung Lastmanagement Skalierfähigkeit Einfache Wartbarkeit Einsatzbreite Kosten Nicht nur die Datenbank, vor allem auch die Datenarchitektur spielt eine entscheidende Rolle. Strukturierte Geschäftsdaten 29.06.2015 © BARC 2015 29 Wie schätzen Sie den Nutzen folgender Technologien zu einer schnelleren und einfacheren Umsetzung von DWH-Anforderungen ein? In-Memory-Datenbanksysteme 55% 30% Automatisiertes Data Warehousing 34% 36% Eigenentwicklung 34% 36% Datenvirtualisierung/Logical Data Warehouse 24% Referenzdatenmodelle 18% Verknüpfung von Best-in-Class-Lösungen 17% Lösungsbausteine vom Softwarehersteller 16% Einkauf fertiger Lösungen Cloud-basierte Data-Warehouse-Lösungen Hoher Nutzen Mittlerer Nutzen 26% 19% 49% 21% 35% 38% 47% 24% Geringer Nutzen 11% 6% 9% 36% 35% 4 9% 26% 43% 11% 8% 11% 4 5% 42% 12% 40% 28% Kein Nutzen Quelle: BARC Survey Datenmanagement im Wandel, n=312 29.06.2015 © BARC 2015 30 Hadoop 29.06.2015 © BARC 2015 31 Einsatzszenarien für Hadoop nehmen weiter zu Ablaufumgebung für fortgeschrittene Analysen/Exploration 24% 25% 40% Staging/Landing Area für semi-/unstrukturierte Daten 20% Datenaufbereitung/Datenintegration für semi/unstrukturierte Daten 20% 32% 35% Abfragbarer Speicher/Datenarchiv für semi/unstrukturierte Daten 19% 30% 42% Datenaufbereitung/Datenintegration für strukturierte Daten 18% 27% Staging/Landing Area für strukturierte Daten 18% 27% Abfragbarer Speiche/Datenarchiv für strukturierte DWH-Daten zur Ergänzung… Ablaufumgebung für klassisches BI Unterstützung operationaler Anwendungen Abfragbarer Speiche/Datenarchiv für Content/Dokumente Abfragbarer Speiche/Datenarchiv für strukturierte DWH-Daten als Ersatz für das… Im Einsatz 29.06.2015 Geplant in 12 Monaten 16% 37% 10% 30% 32% 24% 15% 12% 15% 35% 19% 44% 32% 34% 25% 8% 15% 44% 34% Langfristig geplant Wie nutzen bzw. planen Sie Hadoop zu nutzen? Quelle: BARC Survey „Hadoop 2015“, n=67 © BARC 2015 32 Marktübersicht Hadoop Distributionen Marktübersicht Hauptdistributionen Spezialisten Cloudera Cloudera S Cloudera X A Oracle X NetApp X C HortonWorks MapR Apache Hadoop HortonWorks S HortonWorks X Microsoft X A X Rackspace Generalisten IBM X Teradata X MapR S MapR X A Amazon Pivotal IBM Pivotal C C X S A C X X X X Legende: S: Software, A: Appliance, C: Cloud 29.06.2015 © BARC 2015 33 Top 5 Erkenntnisse aus der Marktumfrage zu Hadoop 1. Der Einsatz von Hadoop wird vorwiegend von der IT für vorwiegend technische Aufgabenstellungen diskutiert 2. Die Bedeutung von Hadoop wächst hin zu einem strategischen Baustein. 3. Einsatzszenarien für Hadoop nehmen weiter zu 4. Fehlendes Know-How und Unsicherheit verhindern Nutzung von Hadoop Potentialen 5. Die Voraussetzungen für den Einsatz von Hadoop fehlen größtenteils noch in den Unternehmen. 29.06.2015 © BARC 2015 34 Chancen und Risiken Chancen des Hadoop-Einsatzes • Open Source Angebot : niedrige Lizenzkosten, Offenheit, CommunityWeiterentwicklung • Speicherung und Verarbeitung sehr großer Datenmengen • Geringe Kosten in puncto Skalierbarkeit, eingebaute Redundanz/Verteilung, Parallelität • Performance bei Batch-Verarbeitung • Flexibilität zur Aufnahme polystrukturierter Daten • Umsetzung von „late binding“ Konzepten: Daten mit unbekannten oder veränderlichen Datenmodellen können integriert werden. • Unterstützung von Individualentwicklung Risiken • Enterprise-Reife und Stabilität (bspw. Datenschutz/fehlende Verschlüsselung) • Geringe Eignung zur Verarbeitung kleiner Datenmengen • DBMS Integration • Verfügbarkeit von Experten • Nutzbarkeit von Hadoop 29.06.2015 © BARC 2015 35 NoSQL 29.06.2015 © BARC 2015 36 Was ist NoSQL? Eine Variante von Datenbanken oder Datenspeichern auf die häufig folgende Definitionen zutreffen: • Nicht relationale Datenspeicherung • Distributed (über mehrere Rechner (auch Geographisch) verteilt) • Open Source oder Open Source Derivate • Horizontal Skalierbar Weitere Charakteristika sind: • Schemafrei • Vermeidung von Joins (->skalierbar) • Einfache Unterstützung für Datenreplikation • Einfache oder limitierte APIs • Datenkonsistenz von „last update wins“ bis ACID • Große Datenmengen 29.06.2015 © BARC 2015 37 Beispiele für NoSQL Datenbanken Key-value store Key Value [email protected] Yining, Hu, , 13 March 1987 [email protected] John, Smith [email protected] Mary, Doherty, 071 546 98999, 654 328 1298, 03 July 1976 Document-oriented stores generalisieren das Key-value Konzept mit multiplen Keys JSON {(Key : Value), (Key : Value), ...} z.B. Google Bigtable, Amazon SimpleDB, Hbase, Cassandra, etc. Graph Datenbanken mit Nodes, Edges und Eigenschaften repräsentieren und speichern assoziative Daten (Analyse von sozialen Netzwerken) { E-mail : [email protected] First name : John , Surname : Smith } { E-mail : [email protected] First name : Mary , Surname : Doherty , Date of birth : 03 July 1976 , Cell-phone : 071 546 98999 , Landline : 654 328 1298 } { E-mail : [email protected] First name : Yining , Surname : Hu , Date-of-birth : 13 March 1987 } z.B. CouchDB, MongoDB, Terrastore, etc. z.B. Neo4J, Infinite Graph, etc. 38 Technologiekarte – Überblick Werkzeugklassen der analytischen Infrastruktur 29.06.2015 © BARC 2015 39 IBM, Informatica, Microsoft, Oracle, SAP, SAS and Teradata in direct comparison Strategy & roadmap Portfolio Architecture and Hadoop Integration Conclusion 29.06.2015 © BARC 2015 40 IBM 29.06.2015 © BARC 2015 41 Total consideration IBM • Leading provider of hardware, software and services in the IT field, especially around information management (Big Data) • Selective expansion in the SW area through in-house development and acquisitions towards a broad data management portfolio • $24B Investment in both organic development and 30+ acquisitions Market development (*) Revenue 2013: 142 Mio. US$ Growth 2013: 5% Market share: 9,8% • Partial function overlaps between the different tools or overlap of tool categories Development priorities • Noticeable maturity of the Big Data middleware (besides Oracle) compared to the other generalists, especially concerning performance, scalability and development support • • • • • Strong commitment in data governance, Big Data and Cloud • Pioneering IBM Watson (cognitive computing) technology and solutions • Significant contribution to the Spark Apache OSS project • Investment of multiple 100 Mio US$ and manpower • Roadmap: • Expansion into the Big Data solution segment • Tightly integrated Big Data platform with „one stop shopping“ approach • To be a leading cloud provider 29.06.2015 Big Data & Analytics Cognitive Computing (Watson) In-Memory SQL interface/integration for Hadoop • Complete Cloud provider • Big Data & Analytics Consulting Did you know that: …, IBM is also one of the largest SW companies. WW revenue ~ $30B. (*) Source: BARC Market Research 2014, All numbers are estimated by BARC and are focused on the German market in 2013 © BARC 2015 42 Technology Map – Overview Tool Classes of Analytical Infrastructure for IBM Search & Discovery Predictive Modeling Text Analysis CPM Sandboxing Specialized DB Calculation Engine Analytical DB Streaming Hadoop Data Integration Virtualization Data Quality Management Master Data Man. Meta Data Man. Classic Analysis Data Modeling Reporting & Dashboards Data Governance & Data Collaboration Full support 29.06.2015 Partial support No support © BARC 2015 43 Technology Map – Overview Tool Classes of Analytical Infrastructure for IBM Watson Explorer, Information Catalog Sandboxing Information Server (DataClick) & BigInsights / PDA / DB2 / ... Data Integration Information Server, DataWorks SPSS, BigInsight & Watson Content Analytics Specialized DB TM1, Informix Time Series, Cloudant NoSQL Analytical DB PDA, DB2 BLU, DashDB SPSS, Watson Analytics Text Analysis Virtualization Fluid Query, Big SQL, DB2, Federation Server Cognos TM1 Calculation Engine Algorithmics CPLEX/Optimization Streaming InfoSphere Streams CPM Hadoop BigInsights, BigSQL, BigSQL Federation, Big Data Appliance Data Quality Management Information Server Master Data Man. Information Server Cognos,BigInsights (BigSheets), SPSS Watson Analytics Predictive Modeling Data Architect Search & Discovery Meta Data Man. Cognos, Watson Analytics Classic Analysis Data Modeling Reporting & Dashboards MDM Server Data Governance & Data Collaboration Information Server 29.06.2015 © BARC 2015 44 IBM Information Management & Analytics 29.06.2015 © BARC 2015 45 Packaging Structure IBM BigInsights for Apache Hadoop Data Scientist IBM BigInsights Data Scientist Administrator Text Analytics Business Analyst IBM BigInsights Analyst Machine Learning on Big R Big R IBM BigInsights Enterprise Management Big SQL Big SQL POSIX Distributed Filesystem BigSheets BigSheets Multi-workload, Multi-tenant scheduling IBM Open Platform with Apache Hadoop* Developer *IBM Open Platform with Apache Hadoop is IBMs own 100% open source Apache Hadoop distribution. IBM will include the ODP common kernel once available (future). 29.06.2015 © BARC 2015 46 Hadoop-SQL(RDBMS)-Integration Import-/Export-Konnektoren In-Database MapReduce MR External Tables MR RDBMS RDBMS MR HDFS HDFS Hive/Impala Hadoop DBs HiveQL MR HDFS 29.06.2015 RDBMS Hybride Systeme RDBMS RDBMS HDFS MR HDFS © BARC 2015 47 BigInsights Big SQL – Federation example Big SQL Federation Layer Oracle 29.06.2015 Big SQL on Hadoop © BARC 2015 48 IBM – key message IBM is transforming the business to a cloud-first strategy and offers in the Big Data and Analytics area a complete stack of products and applications. The intent to be a leading provider for Big Data analytics is expressed by the new organisational units, IBM Analytics, IBM Commerce, and IBM Watson. To support the data driven enterprise with Big Data middleware and solution is IBMs strategic goal. The IBM consulting and implementation services backing up these strategy. 29.06.2015 © BARC 2015 49 Informatica 29.06.2015 © BARC 2015 50 Total consideration Informatica • Leading data integration specialist that provides technology to unleash the power of your data with a strong customer base • Informatica traditionally offers premium technology for more complex integration challenges within large companies. Current developments display additional offerings for the mid market (PowerCenter Express, Cloud offerings) • Informatica works cooperates intensively with partners in order to deliver more overarching solutions to customers (e.g. partner of all major Apache Hadoop distributions) • Roadmap: • Provider of methodology and technology to support overarching Data Governance. Informatica technology is moving forward from a pure infrastructure platform towards a more business user centric Data Intelligence Platform for analytical and operational scenarios - „DI is easy as Excel“ • Further development in direction of Intelligent Data Lake and Self Service Data Integration to provide simple and easy-to-use data solutions also for the business user to enable them to get value from data • Enhance platform integration, flexibility and connectivity in new technology 29.06.2015 Market development (*) Revenue 2013: 51 Mio. US$ Growth: 13% Market share: 3,5% Development priorities • • • • Big Data Self Service Data Integration Data Governance Meta Data Management Did you know that: …, founded in 1993, today Informatica operates in 28 countries around the world (*) Source: BARC Market Research 2014, All numbers are estimated by BARC and are focused on the German market in 2013 © BARC 2015 51 Technology Map – Overview Tool Classes of Analytical Infrastructure for Informatica Search & Discovery Predictive Modeling Text Analysis CPM Sandboxing Specialized DB Calculation Engine Analytical DB Streaming Hadoop Data Integration Virtualization Data Quality Management Master Data Man. Meta Data Man. Classic Analysis Data Modeling Reporting & Dashboards Data Governance & Data Collaboration Full support 29.06.2015 Partial support No support © BARC 2015 52 Technology Map – Overview Tool Classes of Analytical Infrastructure for Informatica Search & Discovery Predictive Modeling Text Analysis CPM Profiler, Analyst, Intelligent Data Lake Sandboxing Specialized DB Calculation Engine Streaming Hadoop Analytical DB Vibe Data Stream, CEP Data Integration PowerCenter Data Integration Hub, PowerExchange, Big Data Edition, Big Data Relationship Manager Virtualization Data Services Big Data Edition, Big Data Parser Data Quality Management Data Quality Manager, Data as a service Master Data Man. Master Data Manager, Product Information Management Data Modeling Rev (Springbok), PowerCenter, Test Data Management Data Governance & Data Collaboration BPM, VIBE Virtual Data Machine, Application Information Lifecycle Management , Cloud Integration, Intelligent Data Lake, Live Data Map Full support 29.06.2015 Partial support No support © BARC 2015 53 Metadata Manager Classic Analysis Meta Data Man. Reporting & Dashboards Informatica tools Informatica Business Glossary Informatica Metadata Manager Informatica Masterdata Manager Informatica Data Quality Informatica Power Center Source: Informatica Intelligence Data Platform Source: Informatica 29.06.2015 © BARC 2015 56 Hadoop Integration Ext. HIVE Read/write connectivity enabled by HIVE interface. Informatica utilizes this interface to initiate Informatica-specific that is implemented in Hadoop to improve performance or enhance functionality (e.g. DQ). Also support of Tez/Spark. Connection logic is built within PowerCenter. 29.06.2015 © BARC 2015 58 Informatica – key message Informatica is a market leading and independent provider of data integration solutions. Informatica provides technology to establish a robust and secure infrastructure in support of value extraction of enterprise data. Informatica does not provide own data storage or analytics technology, but rather focuses on integrating many different kinds of sources and systems and managing the data integration process by providing a flexible and high performant integration platform. In DACH region we see Informatica primary as middleware in analytical and in operational scenarios. 29.06.2015 © BARC 2015 59 Microsoft 29.06.2015 © BARC 2015 60 Total consideration Microsoft • One of the major providers of business software, in particular office products and software for the infrastructure (OS, other middleware) Market development (*) • The strategy "Devices and Services" drives mentioned topics and provides expansion of the Microsoft BI (Big Data) services portfolio towards a flexible self service offering (Office 360, PowerBI, Azure (Cloud)) Revenue 2013: 154,2 Mio. US$ Growth 2013: 16% Market share: 3,5% • Development of BI as an add-on to existing components, always based on Microsoft software (OS, complementary products), which is mandatory for use. • Integrate ML into BI • Integrate PolyBase into SQL Server • Hadoop becomes more embedded • > 640,000 partners (ww) complement the Microsoft product portfolio to industry-specific solutions and technologies and bridge the gaps • Unlike the other generalists, Microsoft frequently finds an entry point via the departmental users (business users) to BI (Excel, SQL Server). • Attractive pricing, specifically for SMB • Roadmap • Cloud • Extend SQL Server and PDW In-Memory capabilities • Integrate Machine Learning capabilities into the font end tools 29.06.2015 Development priorities Did you know that: …, the Microsoft WW revenue for 2014 in software was $86B (*) Source: BARC Market Research 2014, All numbers are estimated by BARC and are focused on the German market in 2013 © BARC 2015 61 Technology Map – Overview Tool Classes of Analytical Infrastructure for Microsoft Search & Discovery Predictive Modeling Text Analysis CPM Sandboxing Specialized DB Calculation Engine Analytical DB Streaming Hadoop Data Integration Virtualization Data Quality Management Master Data Man. Meta Data Man. Classic Analysis Data Modeling Reporting & Dashboards Data Governance & Data Collaboration Full support 29.06.2015 Partial support No support © BARC 2015 62 Technology Map – Overview Tool Classes of Analytical Infrastructure for Microsoft SS Reporting Services, Power BI Classic Analysis Search & Discovery Power BI, Excel Power Pivot/Query/View/ Map SS Analysis Services Data Mining, Revolution R, Sandboxing Text Analysis CPM Machine Lerning (Azure ML) Specialized DB Calculation Engine SS Analysis Services, Document DB, Analytical DB Analytics Platform System, SS Parallel DWH, SQL Server, Azure Data Warehouse Data Integration SS Integration Services Streaming StreamInsight, Azure Storm Virtualization Hadoop HDInsight (Hadoop-Distribution) Analytics Platform System (Appliance) with PolyBase Data Quality Management SS Data Quality Services Azure Data Factory Master Data Man. Data Modeling Power Pivot SS Analytics Services SQL Server Predictive Modeling Meta Data Man. Reporting & Dashboards SS Master Data Services Data Governance & Data Collaboration SS Integration Services 29.06.2015 © BARC 2015 63 Microsoft Analytics Platform System PDW & HDInsight as Appliance = Microsoft Analytics Platform System •Phase 1: ohne M/R, Ablaufumgebung ist SS •Phase 2: Cost-based Optimizer, Ablaufumgebung M/R und RDB gemischt SQL Zugriff auf Hadoop/ HDInsight Hadoop Distrib. (Hortonworks), auch über Azure Quelle: Microsoft 29.06.2015 © BARC 2015 64 Parallel Datatransfer between PDW Compute Nodes and HDFS Data Nodes Quelle: Microsoft 29.06.2015 © BARC 2015 65 Hadoop-SQL(RDBMS)-Integration Import-/Export-Konnektoren In-Database MapReduce MR External Tables MR RDBMS RDBMS MR HDFS HDFS Hive/Impala Hadoop DBs HiveQL MR HDFS 29.06.2015 RDBMS Hybride Systeme RDBMS RDBMS HDFS MR HDFS © BARC 2015 66 Microsoft – key message Microsoft is the largest software company worldwide and the offerings range from desktop software to business applications and middleware. With the advent of Big Data, Microsoft has opened up its „closed shop“ to integrate Hadoop and Hadoop components into its own proprietary product stack to allow analysis and processing of very diverse and heterogeneous data sources. Positioning of SQL Server PDW and SQL Server however is not clear within the overall Microsoft product portfolio. A clear focus area of Microsoft is cloud (Azure) 29.06.2015 © BARC 2015 67 Oracle 29.06.2015 © BARC 2015 68 Total consideration Oracle • Leading provider of hardware and software in the IT field. “One-stop shop for CIO's." and global market leader in number of sold database licenses. Oracle is also a leading provider of business SW (ERP, CRM, SCM) • Oracle seeks to provide tools and products to enable the data driven enterprise (Oracle World 2013), focus is on developing business applications in the BI / DWH area • Numerous acquisitions characterize Oracle's portfolio and development, leading to diverse approaches and technologies with some overlaps • Strong technology-driven sales results in less visibility for new topics: digital transformation, Big Data, predictive analytics • Strong cross- and upselling business within the currently very broad customer base Market development (*) Revenue 2013: 180 Mio. US$ Growth 2013: 6% Market share: 12,4% Development priorities • Tight integration of HW & SW • In-Memory • SQL interface/integration for Hadoop (Big Data SQL) • Visual Analytics • Complete Cloud provider • Strong partner network, also for SMB Did you know that: • Roadmap: • Capitalize on tight integration of Oracle HW (former SUN) and SW • Integration of functions into Oracle DB with the aim at Big Data and Hadoop • To be a leading cloud provider 29.06.2015 …, Oracle WW Revenue for 2014 was ~ $38B. (*) Source: BARC Marktzahlenstudie 2014, All numbers are estimated by BARC and are focused on the German market in 2013 © BARC 2015 69 Technology Map – Overview Tool Classes of Analytical Infrastructure for Oracle Search & Discovery Predictive Modeling Text Analysis CPM Sandboxing Specialized DB Calculation Engine Analytical DB Streaming Hadoop Data Integration Virtualization Data Quality Management Master Data Man. Meta Data Man. Classic Analysis Data Modeling Reporting & Dashboards Data Governance & Data Collaboration Full support 29.06.2015 Partial support No support © BARC 2015 70 Technology Map – Overview Tool Classes of Analytical Infrastructure for Oracle Search & Discovery OBIEE with Dasbords and publishing function OBIEE, Oracle Advanced Analytics with Oracle Data Mining +R Integration Oracle Big Data Discovery Sandboxing Oracle Data Mining + SQL Predict in the Oracle DB Oracle Text Mining in the Oracle DB Essbase, Times Ten, Oracle NoSQL Analytical DB Golden Gate, Oracle Data Integrator, Oracle Soa Suite, Realtime Decison Virtualization Oracle Data Service Integrator Oracle Database Links CPM Oracle BI suite Calculation Engine Functions integrated in Oracle DB Streaming Oracle DB with In-Memory feature Golden Gate, Oracle Data Integrator Text Analysis Specialized DB Oracle DB with multi-tennant wizzard Data Integration Predictive Modeling Meta Data Man. Enterprise Metadata Management Classic Analysis Hadoop Cloudera, Big Data SQL, Big Data Appliance Data Quality Management Oracle Data Quality Suite Master Data Man. Oracle Master Data Management + Product Hub + Customer Hub Data Modeling SQL Developer Data Modeler Reporting & Dashboards Data Governance & Data Collaboration Oracle Relationship Management ( Metadata / Realtionship Management Solution) 29.06.2015 © BARC 2015 71 Oracle Big Data Management System Quelle: Oracle 29.06.2015 © BARC 2015 72 Oracle Engineered Systems – Auszug Based onOracle Berkeley DB Java Edition; Key/value storage; JSON Data Format; focus on transactional use cases. O. SQL Connector for Hadoop O. Loader for Haoop O. XQuery for Hadoop O. R Advanced Analytics for Hadoop O. Data Integrator Application Adapter for Hadoop Cloudera Enterprise Big Data SQL: „smart scan for Hadoop“ Federation of Hadoop (Hive MeD) NoSQL and relational w/ext. Tables, Oracle Data Integrator 29.06.2015 Quelle: Oracle © BARC 2015 73 Big Data SQL: A New Hadoop Processing Engine Quelle: Oracle 29.06.2015 © BARC 2015 74 Hadoop-SQL(RDBMS)-Integration Import-/Export-Konnektoren In-Database MapReduce MR External Tables MR RDBMS RDBMS MR HDFS HDFS Hive/Impala Hadoop DBs HiveQL MR HDFS 29.06.2015 RDBMS Hybride Systeme RDBMS RDBMS HDFS MR HDFS © BARC 2015 75 Oracle – key message Oracle is one of the largest vendors for middleware and business software. Oracle capitalizes on the tight integration of its own HW and SW, from which the „Oracle Engineered Eystems“ (Exadata, Exalytics, etc.) have emerged. Goal is to reduce costs and complexity, and on the other hand to increase performance and productivity. Oracle offers a complete software stack for Big Data and is one of the leading cloud providers. The main focal point of the Oracle strategy is the Oracle DB. Oracle positions it self against the rival SAP (HANA), for example, by Exalytics and Oracle DB InMemory option. The Oracle consulting division is not as strong as the competitors’. 29.06.2015 © BARC 2015 76 SAP 29.06.2015 © BARC 2015 77 Total consideration SAP • One of the largest providers of business software Market development (*) • Committed to enhancing the overall offering towards an overarching data management platform with broad capabilities, with SAP ERP an it‘s core Revenue 2013: 371,0 Mio. US$ Growth 2013: 22% Market share 2013: 25,3% • Most efforts are currently focused on integration among the various components of the portfolio, i.e. SAP ERP, SAP HANA, SAP BW Development priorities • Clear roadmap and messaging for future positioning of SAP HANA and impact on the SAP tool portfolio • • • • Self-Service BI Predictive Analytics Mobile Cloud Did you know that: • In comparison to other software vendors, SAP has a shorter …, is the first vendor to history as a provider of middleware and analytical successfully market an extensive databases (SAP HANA) sematic layer in the DBMS • Driver of discussion concerning evolution of positioning of BI and DWHing • Cloud orientation with SAP HANA, next to SaaS, PaaS, no IaaS-Angebot 29.06.2015 © BARC 2015 78 Technology Map – Overview Tool Classes of Analytical Infrastructure for SAP Search & Discovery Predictive Modeling Text Analysis CPM Sandboxing Specialized DB Calculation Engine Analytical DB Streaming Hadoop Data Integration Virtualization Data Quality Management Master Data Man. Meta Data Man. Classic Analysis Data Modeling Reporting & Dashboards Data Governance & Data Collaboration Full support 29.06.2015 Partial support No support © BARC 2015 79 Technology Map – Overview Tool Classes of Analytical Infrastructure for SAP Crystal Reports, Web Intelligence, Design Studio Analysis for OLAP/Office Web Intelligence Explorer Search & Discovery Lumira, Predictive Analysis Sandboxing Predictive Modeling Text Analysis Predictive Analysis, InfiniteInsight, CPM Planning and Consolidation, Integrated Planning Specialized DB Calculation Engine Streaming Hadoop Analytical DB SAP HANA, Sybase IQ Sybase Event Stream Processor Data Integration Data Services, Landscape Transformation, Smart Data Access, Smart Data Integration Virtualization Data Federator, Smart Data Access HANA Smart Data Access Data Quality Management Data Services, Information Steward Master Data Man. Netweaver MDM, Master Data Governance, Signal Management Data Modeling SAP BW Data Governance & Data Collaboration Information Steward Full support 29.06.2015 Partial support No support © BARC 2015 80 Information Steward Classic Analysis Meta Data Man. Reporting & Dashboards Journey to a SAP HANA based Enterprise Target Architecture Vision: Simplify Your SAP Enterprise Architecture SAP HANA Information models: • Analytic perspectives are logical, semantic views on normalized, detailed data! SAP S4HANA: • Reduction of the operational data model by 90%! 29.06.2015 © BARC 2015 81 Journey to a SAP HANA based Enterprise Target Architecture Vision: Extend across all data locations with SAP Smart Data Access 29.06.2015 © BARC 2015 82 SAP HANA Smart Data Access Mögliche Hadoop Distributionen: Intel, Hortonworks, Cloudera, MapR Quelle: SAP 29.06.2015 © BARC 2015 83 Hadoop-SQL(RDBMS)-Integration Import-/Export-Konnektoren In-Database MapReduce MR External Tables MR RDBMS RDBMS MR HDFS RDBMS HDFS Hive Hadoop DBs Hybride Systeme RDBMS HiveQL RDBMS MR HDFS MR HDFS HDFS 29.06.2015 © BARC 2015 84 SAP – key message SAP is a software generalist. SAP HANA has become the core component of the SAP portfolio and all strategic SAP developments appear to be focused on the HANA technology and overall latfrm, esp. for ERP, BI, EPM and Predictive Analytics. HANA is a major investment for SAP and a driving factor for the overall SW portfolio, spanning ERP modules, BW and recebt big data solutions. Recent developments concerning S4/HANA have underlined this approach. Important to understand is that SAP HANA offers special interfaces to exploit the full power of the in-database analytical capabilities. 29.06.2015 © BARC 2015 85 SAS 29.06.2015 © BARC 2015 86 Total consideration SAS • Leading independant software vendor and BI specialist with focus on BI and data management. • Comprehensive solution portfolio (various predefined analytical applications) • Focus on analytics for “everybody”, also for non-data scientists or non-experts in statistics • Vision to enable Hadoop as an data operating System (core component) for nextgen BI- & analytics strategy • Vision: SAS technology should be THE analytics and data management solution for Hadoop • Roadmap: • Tighter integration of SAS analytics in operational processes • Offering of an integrated Big Data Platform • Further development of Big Data applications/solutions 29.06.2015 Market development (*) Revenue 2013: 109,5 Mio. US$ Growth: -10% (ww +5%) Market share: 7,5% Development priorities • • • • • Big Data Real-time In-Memory Cloud User Experience Didi you know that: …, in 2014 SAS did high investments to integrate Hadoop into the SAS portfolio (*) Source: BARC Marktzahlenstudie 2014, All numbers are estimated by BARC and are focused on the German market in 2013 © BARC 2015 87 Technology Map – Overview Tool Classes of Analytical Infrastructure for SAS Search & Discovery Predictive Modeling Text Analysis CPM Sandboxing Specialized DB Calculation Engine Analytical DB Streaming Hadoop Data Integration Virtualization Data Quality Management Master Data Man. Meta Data Man. Classic Analysis Data Modeling Reporting & Dashboards Data Governance & Data Collaboration Full support 29.06.2015 Partial support No support © BARC 2015 88 Technology Map – Overview Tool Classes of Analytical Infrastructure for SAS Enterprise Guide, Visual Analytics, Visual Statistics Visual statistics, Enterprise Miner Sandboxing Predictive Modeling Visual Analytics Contextual Analytics Specialized DB Enterprise Guide, Big Data Lab SPDS, LASR Server, Base, OLAP Analytical DB Data Integration Virtualization Federation Server CPM Financial Management Calculation Engine various Streaming Event Stream Processing Data Management, Data Loader, Access Software, Enterprise Guide Text Analysis Hadoop Data Management, Data Loader for Hadoop, Access Software, Federation Server Data Quality Management Data Quality, Data Loader for Hadoop Master Data Man. MDM Data Governance & Data Collaboration SAS Data Governance Full support 29.06.2015 Partial support No support © BARC 2015 89 Metadata Server Search & Discovery Meta Data Man. Web Report studio, BI dashboards Classic Analysis Data Modeling Reporting & Dashboards Integration, interoperability and interfaces Source: SAS 29.06.2015 © BARC 2015 90 Hadoop Integration Source: SAS 29.06.2015 © BARC 2015 91 SAS – key message Market leading BI and analytics specialist that is committed to supporting Hadoop as an data operating/storage system and generating added value by adding next-gen analytics and data management capabilities to simplify and enhance the „user-experience”, while saving business continuity. SAS does not provide an own relational database solution but rather focuses on leveraging market-available technology with sophisticated analytics and data management capabilities. Therefore, integration with widespread, market-established data storage technologies is one of the main characteristics and objectives of the emerging SAS portfolio. 29.06.2015 © BARC 2015 92 Teradata 29.06.2015 © BARC 2015 93 Total consideration Teradata • Teradata is the veteran in the data warehousing arena and expanding its offers continuously through in-house development and acquisitions towards Big Data. Examples are the acquisitions of Rainstor (Big Data archiving) and Loom (comprehensive metadata for the Teradata Unified Data Architecture) and Presto as an Open Source initiative (SQL on Hadoop) of Teradata • Preference of Teradata Aster DB over Hadoop stack for explorative analysis and data discovery (efficiency and simplification reasons) • Teradata installations are more likely to be found in the high end data warehouse market, ranging from multiple TB to PB • Flexible solution availability (SW Only, Cloud, Commodity Prebuilt) • Teradata has a relatively small, but very knowledgable consulting division • Roadmap: • Extend the Integrated Data Warehouse to address new analytic capabilities and data types • Enable additional analytic needs and architectural requirements via workload specific platforms and cloud based solutions • Expand UNIFIED DATA ARCHITECTURE to support design, architecture, and implementation, and leverage multiple best-of breed engines, new tools, and emerging technologies to provide analytics on all data 29.06.2015 Market development SW D Revenue 2013: 30,1 Mio. US$ Growth 2013: -5% Market share: 2,1% Development priorities • Integrate all data of the Big Data space via UDA • Expand in database analytics • Expand Query Grid capabilities Did you know that: …, Teradata WW Revenue for 2014 was ~ $2.7B. ….,Teradata released the world's first parallel data warehouse DB in 1984 (*) Source: BARC Marktzahlenstudie 2014, All numbers are estimated by BARC and are focused on the German market in 2013 © BARC 2015 94 Technology Map – Overview Tool Classes of Analytical Infrastructure for Teradata Search & Discovery Predictive Modeling Text Analysis CPM Sandboxing Specialized DB Calculation Engine Analytical DB Streaming Hadoop Data Integration Virtualization Data Quality Management Master Data Man. Meta Data Man. Classic Analysis Data Modeling Reporting & Dashboards Data Governance & Data Collaboration Full support 29.06.2015 Partial support No support © BARC 2015 95 Technology Map – Overview Tool Classes of Analytical Infrastructure for Teradata Teradata Aster DB & Applications (Aster Lens) Sandboxing Predictive Modeling Teradata Warehouse Miner Text Analysis Teradata Aster DB & Applications Specialized DB Teradata DB/Teradata Aster DB CPM Calculation Engine Engines for Risk, Pricing, Profitability, ... Analytical DB Streaming Teradata DB Hadoop Teradata Distribution for Hadoop (based on Horton Works, MapR, Cloudera), Presto Data Integration Virtualization Data Quality Management Teradata Query Grid Master Data Man. Teradata MDM (framework) Data Governance & Data Collaboration Teradata Loom, Teradata Rainstor 29.06.2015 © BARC 2015 96 Teradata Loom Search & Discovery Meta Data Man. Classic Analysis Data Modeling Reporting & Dashboards Teradata Unified Data Architecture (UDA) 29.06.2015 © BARC 2015 97 Teradata Query Grid (Virtualization) Enabling the UDA Quelle: Oracle 29.06.2015 © BARC 2015 98 Hadoop-SQL(RDBMS)-Integration Import-/Export-Konnektoren In-Database MapReduce MR External Tables MR RDBMS RDBMS MR HDFS HDFS Hive/Impala Hadoop DBs HiveQL MR HDFS 29.06.2015 RDBMS Hybride Systeme RDBMS RDBMS HDFS MR HDFS © BARC 2015 99 Teradata – key message Teradata is the market leader in the high end data warehousing area and has extended its offerings (i.e. Terdata Aster DB) to address the Big Data requirements for explorative analytics and data discovery. Teradata Aster has to be specifally licensed, which entails additional costs for the overall BI/analytics architecture. From an overarching perspective, the different elements and engines of the Teradata solution architecture are tied together by the Teradata Unified Data Architecture (UDA). Query Grid provides federated access across various different data sources, both Teradata-specific as well as nonTeradata-specific. Teradata‘s focus is providing a solid backbone for all analytic needs. 29.06.2015 © BARC 2015 100 Overview Strategy & Roadmap Portfolio (analyst perception of major driver for roadmap) Cognitive Intelligence Generalist Data Management Specialist Data Integration Cloud / Open Source Generalist Integrated systems Generalist SAP SAP HANA Generalist SAS Analytics for Hadoop Specialist Analytics Teradata Únified Data Platform Specialist Database IBM Informatica Microsoft Oracle 29.06.2015 © BARC 2015 101 Ihr Kontakt bei BARC Otto Görlich Senior Analyst BI & Datenmanagement Tel +49 (0) 931-880651-0 [email protected] BARC GmbH Berliner Platz 7 97080 Würzburg www.barc.de @BARC_Research Timm Grosser Senior Analyst BI & Datenmanagement Tel +49 (0) 931-880651-0 [email protected] BARC GmbH Berliner Platz 7 97080 Würzburg www.barc.de @BARC_Research 29.06.2015 © BARC 2015 102 BARC-Tagung: Data Governance Day 2015 • • 03.September 2015 in Baden (Schweiz) Themen: • • • • • • • • • • • Datenstrategie und Governance Data Life Cycle Data Driven Organisation Data Integration Data Quality Master Data Management Data Life Cycle Document Management Fachvorträge der BARC-Analysten und Fachverbände Seminardokumentation mit Fachartikeln Herstellerpräsentationen 29.06.2015 © BARC 2015 103 BARC-Tagung: Advanced und Predictive Analytics • • 29. September 2015 in Frankfurt Themenschwerpunkte: • Anwendungsgebiete (z.B. Auslastungsoptimierung, Wartung, Forschung, CRM, Social Media) • Praxisbeispiele und Erfolgsfaktoren • Management des Analytischen Prozesses • Marktübersicht Software- und Serviceanbieter • • Fach-, Praxis-und Produktvorträge zu den verschiedenen Anwendungsmöglichkeiten Herstellerpräsentationen 29.06.2015 © BARC 2015 104 BARC Congress Business Intelligence & Datenmanagement • • am 10.+11. November 2015 in Würzburg Highlights & News: • Anbietervorträge, Case Studies, Analystenvorträgen von BARC und PAC, Best Practice Award uvm. • Neuer Track mit Fokus auf Datenmanagement und Big Data • Videoaufzeichnung aller Vorträge • BARC-Service zur Terminvereinbarung im Vorfeld zwischen Teilnehmern und Ausstellern • Attraktive Abendveranstaltung mit Verleihung des BI Best Practice Awards • Erweiterung der Zielgruppe durch den parallel stattfindenden CRM Summit 29.06.2015 © BARC 2015 105
