Implementing the Logical Data Warehouse with Oracle Big Data SQL

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1 Implementing the Logical Data Warehouse with Oracle Big Data SQL Matthias Fuchs DWH Architekt ISE Information Systems Engineering GmbH

2 ISE Information Systems Engineering Gegründet 1991 Mitarbeiteranzahl: 60 Hauptsitz in Gräfenberg, Niederlassungen in München und Nürnberg Schwerpunkte: Oracle Engineered Systems (Exadata / Exalogic / Exalytics) Data Warehousing & Business Intelligence Oracle DB Migrationen, Optimierungen, Hochverfügbarkeit Managed Service für Datenbanken, BI und Middlewareapplikationen Oracle Partner Engineered Systems Award 2013 Copyright (C) ISE GmbH - All Rights Reserved 2

3 ISE Oracle Technology Center Copyright (C) ISE GmbH - All Rights Reserved 3

4 ISE Oracle Technology Center Erstes und einziges Exastack Technology Center in Deutschland in Nürnberg Coming soon ODA X5 Copyright (C) ISE GmbH - All Rights Reserved 4

5 Agenda LDW - Logical Datawarehouse Big Data SQL Infrastructure Sqoop - der Anfang Customer case Copyright (C) ISE GmbH - All Rights Reserved 5

6 LDW Logical Datawarehouse Copyright (C) ISE GmbH - All Rights Reserved 6

7 Logical Data Warehouse Gartner Hype Cycle for Information Infrastructure, 2012, the Logical Data Warehouse (LDW) is a new data management architecture for analytics which combines the strengths of traditional repository warehouses with alternative data management and access strategy. The LDW will form a new best practices by the end of Copyright (C) ISE GmbH - All Rights Reserved 7

8 Gartner: Logical Dataware House Repository Management Verschiedene Typen u.a. Metadaten Konsolidierung Data Virtualization Virtuelle Daten Schicht Distributed Processes Aufruf externer Prozesse z.b. Bilder oder Content Analyse, aber auch MapReduce Cloud Auditing statistics and performance Evaluation Statistik über Performance End User, Applikationen oder Verbindungen SLA Management Metadataset über erwartete Ausführungenzeiten etc. Überwachung und ggf. Änderung der Ausführung Taxonomy - Ontology resolution a taxonomy tree in an ontological forest Metadata Management Copyright (C) ISE GmbH - All Rights Reserved 8

9 Gartner: Logical Dataware House Repository Management Verschiedene Typen u.a. Metadaten Konsolidierung Data Virtualization Virtuelle Daten Schicht Distributed Processes Aufruf externer Prozesse z.b. Bilder oder Content Analyse, aber auch MapReduce Cloud Inhalte einzubeziehen Auditing statistics and performance Evaluation Statistik über Performance End User, Applikationen Höhere oder Verbindungen Flexibilität SLA Management Metadataset über erwartete Ausführungenzeiten etc. Überwachung und ggf. Änderung der Ausführung Taxonomy - Ontology resolution a taxonomy tree in an ontological forest Metadata Management Data-to-insight cycle ' schneller günstiges Framework um neue Copyright (C) ISE GmbH - All Rights Reserved 9

10 Gartner: Übersicht Aus Gartner Newsletter Logical Data Warehousing for Big Data Copyright (C) ISE GmbH - All Rights Reserved 10

11 Virtualisation & Query Federation Information Management Reference Architecture Oracle Data Reservoir & Enterprise Information Store complete view Data Sources Enterprise Performance Management Data Engines & Poly-structured sources Structured Data Sources Operational Data COTS Data Streaming & BAM Data Ingestion Access & Performance Layer Foundation Data Layer Past, current and future interpretation of enterprise data. Structured to support agile access & navigation Immutable modelled data. Business Process Neutral form. Abstracted from business process changes Pre-built & Ad-hoc BI Assets Master & Reference Data Sources Raw Data Reservoir Immutable raw data reservoir Raw data at rest is not interpreted Information Interpretation Information Services Content Docs SMS Web & Social Media Discovery Lab Sandboxes Project based data stores to support specific discovery objectives Rapid Development Sandboxes Project based data stored to facilitate rapid content / presentation delivery Data Science Auditing statistics/performance Evaluation SLA Management Copyright (C) ISE GmbH - All Rights Reserved 11

12 Big Data SQL Infrastructure Copyright (C) ISE GmbH - All Rights Reserved 12

13 Big Data Sql - Übersicht Cloudera Hadoop NOSQL R Advanced Analytics Oracle Big Data SQL Connectors ODI Exadata Advanced Analytics Advanced Security Or BigData Lite VM Copyright (C) ISE GmbH - All Rights Reserved 13

14 Big Data Systemübersicht Processing Layer Big Data SQL Resource Management YARN + MapReduce Storage Layer Filesystem (HDFS) Copyright (C) ISE GmbH - All Rights Reserved 14

15 Big Data und DB im LDW Repository Management Oracle Big Data Appliance Data Virtualization Distributed Processes Auditing statistics and performance SLA Management ODI, BPM, SOA Enterprise Metadata Management Taxonomy - Ontology resolution Copyright (C) ISE GmbH - All Rights Reserved 15

16 Sqoop - der Anfang Copyright (C) ISE GmbH - All Rights Reserved 16

17 Sqoop Sqoop = SQL- to Hadoop Paralleles kopieren von JDBC <-> HDFS MapReduce jobs zum Daten laden/schreiben HDFS DB Map Reduce Copyright (C) ISE GmbH - All Rights Reserved 17

18 Sqoop mit Oracle OraOOP Guy Harrison team Quest (Dell) Ab version (CDH 5.1) Oracle direct path (non-buffered) IO for all reads Auf mappers werden Anzahl Blöcke verteilt Bei partitionierten Tabellen, kann der Mapper pro Partition arbeiten HDFS HADOOP MAPPER ORACLE SESSION ORACLE TABLE HADOOP MAPPER ORACLE SESSION Copyright (C) ISE GmbH - All Rights Reserved 18

19 Real Time Oracle Change Data Capture Supported in 11.2 but not recommended by Oracle Desupported in 12.1 Oracle Golden Gate 1. RDBMS to HIVE 2. RDBMS to Flume 3. RDBMS to HDFS Andere Hersteller: (Dell) Quest SharePlex Auslesen redologs (VMWare) Continuent Tungsten uses CDC im Hintergrund Libelle Copyright (C) ISE GmbH - All Rights Reserved 19

20 Customer case Copyright (C) ISE GmbH - All Rights Reserved 20

21 Analyse von Infrastrukturdaten Ziel Daten von Servicecalls (OSB) auswerten Daten Historisieren Feststellen von Anomalien Mappen von Strukturierten und Unstrukturierten Daten Tabellen/View und Datei Import Auswertung mit ausgewählten Werkzeugen Analytic output R Elasticsearch YARN/MR Weblogs Flume HDFS SQOOP CC RDBMS Copyright (C) ISE GmbH - All Rights Reserved 21

22 Vorbereitung Wahl der Hadoop Distribution Cloudera Oracle supported Ohne -> sehr aufwendig Filedaten Flume Weblogic und Apache Logs Gut dokumentiert im Netz Ggf. Realtime Auswertung mit Elasticsearch or Solr Hive CDH 5.1 OCRFile Format Copyright (C) ISE GmbH - All Rights Reserved 22

23 Hive ORCFile Optimized Row Columnar File Format light-weight indexes bereits im Fileformat block-mode compression auf basis des Datentyps Größenvergleich über verschiedene Typen Encoded Text CSV File RCFile Record Columnar File Parquet Columnar Storage Format, impala ORCFile Hive TPC-DS Scale 500 Dataset GB, Hortonworks Copyright (C) ISE GmbH - All Rights Reserved 23

24 Ablauf Datenintegration Teil 1 Datenladen DB HDFS HIVE Oracle Big Data SQL Teil 2 Create Big Data SQL Layer Copyright (C) ISE GmbH - All Rights Reserved 24

25 Prozess Teil 1 DB Start sqoop job to HDFS Create external table on HDFS Files insert as select in hive ocr data table HDFS HIVE Import parallel 1, da view daten Kein primary key, keine parallelen MapReduce Prozesse Direct read notwendig, da sonst tmp Tablespace zu klein Start mit sqoop2, ende mit sqoop1 inklusiv Optimierung ODI statt oozie Copyright (C) ISE GmbH - All Rights Reserved 25

26 Prozess Teil 2 Suche Tabelle in Hive aus DB select table_name, input_format, Location from ALL_HIVE_tables where table_name like '%oem%'; Copyright (C) ISE GmbH - All Rights Reserved 26

27 Prozess Teil 2 Create Table in DB (nur in Test VM) DDL mit CREATE_EXTDDL_FOR_HIVE erzeugen DDL ausführen DDL Erzeugen dbms_hadoop.create_extddl_for_hive( CLUSTER_ID=>'bigdatalite', DB_NAME=>'default', HIVE_TABLE_NAME=>'oem_data', HIVE_PARTITION=>FALSE, TABLE_NAME=>'oem_data', PERFORM_DDL=>FALSE, TEXT_OF_DDL=>DDLout ); DDL Asuführen CREATE TABLE OEM_DATA ( target_name VARCHAR2(4000), target_guid.. key_value6 VARCHAR2(4000), collection_timestamp VARCHAR2(4000)) ORGANIZATION EXTERNAL (TYPE ORACLE_HIVE DEFAULT DIRECTORY DEFAULT_DIR ACCESS PARAMETERS ( com.oracle.bigdata.cluster=bigdatalite com.oracle.bigdata.tablename=default.oem_ data) ) ; Copyright (C) ISE GmbH - All Rights Reserved 27

28 Ausführungsplan Copyright (C) ISE GmbH - All Rights Reserved 28

29 Ergebnisse: Laden der Daten Daten für einen Tag ~ Zeilen/12 Spalten TXT Files ~100 G unkomprimiert Ladezeit ca. 1h aus CC DB OCR Files in hive ~ 27 M komprimiert ~ Ladezeit ca. 30 Minuten Teil 1 Type Größe Select count Where Oem_data BigDataSQL 2,8 MB 2,1 Mio 11s 8s Teil 2 Oem_data local kopiert Oracle 558 MB 2,1 Mio 0,5s 0,5s Oem_data Hive 57s 50s Copyright (C) ISE GmbH - All Rights Reserved 29

30 Lastverteilung Big Data SQL Only data retrieval (TABLE ACCESS FULL und Filter ) werden offloaded! Datenbearbeitung im DB Layer GROUP BY, ORDER BY, JOIN, PL/SQL etc BigDataSQL 2.0 (Aggregation in Hadoop?) Alternativ Connect über ODBC Tool Beschreibung Decompress CPU Filtering CPU Datatype Conversion Sqoop Hadoop Oracle Oracle Oracle SQL Connector für HDFS Big Data SQL Text Dateien HDFS oder DataPump HDFS 12c Exadata&BDA Oracle Oracle Hadoop Hadoop Hadoop ODBC Hadoop Hadoop Oracle Copyright (C) ISE GmbH - All Rights Reserved 30

31 Zusammenfassung Vorher: Exadata DB/EMC Nacher: Hadoop Integration Layer Exadata DB/EMC Copyright (C) ISE GmbH - All Rights Reserved 31

32 Q & A Copyright (C) ISE GmbH - All Rights Reserved 32

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