EMC Greenplum. Big Data meets Big Integration. Wolfgang Disselhoff Sr. Technology Architect, Greenplum. André Münger Sr. Account Manager, Greenplum

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1 EMC Greenplum Big Data meets Big Integration Wolfgang Disselhoff Sr. Technology Architect, Greenplum André Münger Sr. Account Manager, Greenplum 1

2 2

3 GREENPLUM DATABASE Industry-Leading Massively Parallel Processing (MPP) Performance 3

4 Overview MPP Systems / Must-Haves High Availability, Fault tolerance, Failover, Backup and Recovery Simpler Architecture needed Decreased maintenance Performance improvements (by factors for Load and Enduser) Highly improved cost / performance ration Linear, unlimited scalability Fully supporting semi- and unstructured data (e.g. Hadoop) Rich Ecosystem (Interfaces / Partner / Migration) Company stability / strategy 4

5 GREENPLUM DATABASE Extreme Performance for Analytics Optimized for BI and analytics Deep integration with statistical packages High performance parallel implementations Simple and automatic Just load and query like any database Tables are automatically distributed across nodes Extremely scalable MPP shared-nothing architecture All nodes can scan and process in parallel Linear scalability by adding nodes 5

6 Massively Parallel Processing And Linear Performance Scalability SQL BI tools Analytical tools Hadoop MapReduce Standard Business Intelligence and Analytical tools and new gen tools: R, rattle, perl, python, PL/java etc Master Server Query planning & dispatch primary server, plus hot failover Clients sees a single database Segment Servers Query processing & data storage Network Interconnect Queries distributed across all available resources Shared Nothing, Massively Parallel Processing means no bottlenecks and linear scalability. Data Sources Loading, streaming, etc. External Files, URLs, Hadoop (HDFS), WebServices (including from other DBs), O/S Pipes (including from other DBs) Data loading also takes advantage of MPP architecture Greenplum handles structured, semistructured and unstructured data 6

7 GREENPLUM DATABASE High Availability Master Server Data Protection Replicated transaction logs for server failure Optional RAID protection for drive failures Upon server failure Standby server activated Administrator alerted Orchestrated failover Master Master Segment Server Data Protection Mirrored segments for server failures Optional RAID protection for drive failures Upon server failure Mirrored segments take over with no loss of service Fast online differential recovery Segment Segment Segment Segment 7

8 Migration using Informatica BI Tools Data Integration Reporting Analytics Complete set of migration best practices Simple 3 rd -party reporting and analytic tool integration Greenplum training specifically for Oracle DBAs Oracle Data Warehouse High speed data export, transformation, and load Support for proprietary Oracle SQL functions for continuing use of Oracle DB talent Oracle Environment EMC GP DCA 8

9 GREENPLUM HD Delivering Enterprise-Ready Apache Hadoop 9

10 GREENPLUM HD Greenplum HD Architecture GREENPLUM COMMAND CENTER Hadoop Tools (Pig, Hive, HBase, Zookeeper, Mahout, etc ) MapReduce Layer Pluggable Storage Layer (HDFS API) Apache HDFS Greenplum Chorus Isilon OneFS 10

11 GREENPLUM CHORUS The World s First Agile Analytics Productivity Platform 11

12 GREENPLUM CHORUS Agile Analytics Faster, Easier with Chorus Explore the Data Project Workspaces Collaboration Publish and Iterate Data Analysis 12

13 GREENPLUM CHORUS Collaborative Analytics Iterate faster for accelerated insights with real-time social collaboration Make projects more transparent Collaborate within projects, share information across teams 13

14 The Power of Data Co-Processing 14

15 THE ANSWER MACHINE DATA IN. DECISIONS OUT. Greenplum Modular Data Computing Appliance 15

16 GREENPLUM DCA Start With a High Speed Interconnect Functional Administrative Switch Functional Functional 2 10GE Switches 2 GPDB Master Servers Functional 16

17 GREENPLUM DCA Greenplum Data Computing Appliance A Revolutionary Modular Architecture Greenplum Database Standard GPDB 9TB capacity (uncompressed) Each server contains: 2 sockets/12cores - 48GB Memory 12x 600GB storage Greenplum Database High Capacity GPDB 31TB capacity (uncompressed) Each server contains: 2 sockets/12cores - 48GB Memory 12x 2TB storage Greenplum HD HD 28TB capacity (3 copies, uncompressed) Each server contains: 2 sockets/12cores - 48GB Memory 12x 2TB storage Greenplum Data Integration Accelerator (DIA) DIA 70TB capacity Each server contains: 2 sockets/12cores - 48GB Memory 12x 2TB storage 17

18 Scale to Multiple Racks In Granular Quarter Rack Increments 1st Rack Aggregation Rack Functional Greenplum Database s Functional Greenplum Database s Functional or Greenplum DIA + Functional or Greenplum DIA + or or Functional Greenplum HD Functional Greenplum HD Greenplum Database (required) Add ¼ rack Increments Functional Add ¼ rack Increments 18

19 GREENPLUM DCA Seamless Infrastructure Integration EMC Data Domain Efficient Backup & Restore Isilon Scale Out Storage For Big Data Staging EMC VMAX or VNX SAN Mirror For Advanced Storage Management EMC VMAX SRDF EMC Data Domain Replication For Disaster Recovery 19

20 20

21 Examples using Informatica PowerCenter PowerConnect for Greenplum 21

22 Greenplum und Infa Produktives Mapping Es werden ca. 600 Mio-Datensätze mit einem Parallelitätsgrad 20 aus Files gelesen. Die Ergebnisse werden mit einem Parallelitätsgrad 20 in 6 Datenbanktabellen geschrieben ==: 6*20 = 120 parallele Datenstreams Gelesen aus der Quelle: Records Geschrieben in Greenplum: Records Fehler : 0 Verarbeitungszeit: ~45min Davon Informatica: 43 min Greenplum: 2 35 (aus den Loaderlogs) Ca rec/s 22

23 Daten aus Greenplum lesen: Produktives Mapping Step 2 Lesen der von Schritt 1 geschriebenen Daten und weitere Verarbeitung. Wieder 20fach parallel. 23

24 Blick auf die Session: Laufzeit für ~360Mio Datensätze: 7 15 mit 20*~ rec/s = rec/s 24

25 Blick auf den Greenplum Monitor: Durchsatz 10Gb Network: 10Gb entspricht bit/s Byte/s kb/s MB/s Bruttorate nutzbar für Protokoll+Daten Damit ist bei ca. 15% Auslastung der SegmentServer eine 90% Auslastung eines 10Gb Interfaces erreicht. 85% Freie DB-Rechenkapazität ist für Analytic verfügbar! 25

26 Ergebnisse der Tests Informatica lastet einen DIA-ETL-Server mit 2-CPU und 12 Kernen > 90% aus. Möglich durch den Durchsatz über die genutzten 10Gb-Interfaces, DB-Performance. 26

27 Pushdown Technology Mapping : Zwei Datenmengen werden gejoint, eine Expression und ein Aggregator ausgeführt Message: Optimizer generated SQL statement for target [ev.test_install]: INSERT INTO dev.test_install_pd ( Integer_FLD, Bigint, Date_FLD, varchar_fld ) SELECT CAST(COUNT(test_install1.integer_fld) AS FLOAT), CAST(COUNT(test_install1.bigint) AS FLOAT), MAX(test_install2.date_fld), MIN(UPPER(test_install2.varchar_fld)) FROM (test_install test_install1 INNER JOIN test_install test_install2 ON ((( (test_install2.integer_fld = test_install1.integer_fld) AND (test_install2.bigint = test_install1.bigint)) AND (test_install2.date_fld = test_install1.date_fld)) AND (test_install2.varchar_fld = test_install1.varchar_fld))) GROUP BY test_install1.integer_fld, test_install1.bigint, test_install2.date_fld, UPPER(test_install2.varchar_fld) Fazit PushDown Informatica kann Logik vollständig in die Greenplum Datenbank abgeben. D.h. es findet kein Datentransfer zwischen DB und ETL statt. Dabei ist die Datenbank unabhängig von Mapping/Session immer massiv parallel. 27

28 Fragen? 28

29 Vielen Dank! 29

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