Leveraging Hadoop with OBIEE 11g and ODI 11g - UKOUG Tech'13
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Leveraging Hadoop with OBIEE 11g and ODI 11g - UKOUG Tech'13

on

  • 2,945 views

The latest releases of OBIEE and ODI come with the ability to connect to Hadoop data sources, using MapReduce to integrate data from clusters of "big data" servers complementing traditional BI data ...

The latest releases of OBIEE and ODI come with the ability to connect to Hadoop data sources, using MapReduce to integrate data from clusters of "big data" servers complementing traditional BI data sources. In this presentation, we will look at how these two tools connect to Apache Hadoop and access "big data" sources, and share tips and tricks on making it all work smoothly.

Statistics

Views

Total Views
2,945
Views on SlideShare
2,858
Embed Views
87

Actions

Likes
2
Downloads
104
Comments
0

5 Embeds 87

https://twitter.com 43
http://dc1 15
http://192.168.137.100 13
http://www.linkedin.com 10
https://www.linkedin.com 6

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Leveraging Hadoop with OBIEE 11g and ODI 11g - UKOUG Tech'13 Presentation Transcript

  • 1. Leveraging Hadoop with OBIEE 11g and ODI 11g
 Mark Rittman, CTO, Rittman Mead UKOUG Tech’13 Conference, Manchester, December 2013 T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 2. About the Speaker • Mark Rittman, Co-Founder of Rittman Mead • Oracle ACE Director, specialising in Oracle BI&DW • 14 Years Experience with Oracle Technology • Regular columnist for Oracle Magazine • Author of two Oracle Press Oracle BI books • Oracle Business Intelligence Developers Guide • Oracle Exalytics Revealed • Writer for Rittman Mead Blog :
 http://www.rittmanmead.com/blog • Email : mark.rittman@rittmanmead.com • Twitter : @markrittman T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 3. About Rittman Mead • Oracle BI and DW Gold partner • Winner of five UKOUG Partner of the Year awards in 2013 - including BI • World leading specialist partner for technical excellence, 
 solutions delivery and innovation in Oracle BI • Approximately 80 consultants worldwide • All expert in Oracle BI and DW • Offices in US (Atlanta), Europe, Australia and India • Skills in broad range of supporting Oracle tools: ‣OBIEE, OBIA ‣ODIEE ‣Essbase, Oracle OLAP ‣GoldenGate ‣Endeca T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 4. Leveraging Hadoop with OBIEE 11g and ODI 11g
 Part 1 : Hadoop, Big Data and DW Architectures T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 5. Traditional Data Warehouse / BI Architectures • Three-layer architecture - staging, foundation and access/performance • All three layers stored in a relational database (Oracle) • ETL used to move data 
 from layer-to-layer Traditional Relational Data Warehouse Staging Foundation /
 ODS Performance /
 Dimensional Data
 Load Data
 Load Traditional structured
 data sources ETL ETL Data
 Load Data
 Load T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) Direct
 Read E : info@rittmanmead.com W : www.rittmanmead.com BI Tool (OBIEE)
 with metadata
 layer OLAP / In-Memory
 Tool with data load
 into own database
  • 6. Recent Innovations and Developments in DW Architecture • The rise of “big data” and “hadoop” ‣New ways to process, store and analyse data ‣New paradigm for TCO - low-cost servers, open-source software, cheap clustering • Explosion in potential data-source types ‣Unstructured data ‣Social media feeds ‣Schema-less and schema-on-read databases • New ways of hosting data warehouses ‣In the cloud ‣Do we even need an Oracle database or DW? • Lots of opportunities for DW/BI developers - make our systems cheaper, wider range of data T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 7. Introduction of New Data Sources : Unstructured, Big Data Data
 Load Traditional Relational Data Warehouse Staging Traditional structured
 data sources Data
 Load Schema-less / NoSQL
 data sources Unstructured/
 Social / Doc
 data sources Hadoop / Big Data
 data sources Foundation /
 ODS ETL Performance /
 Dimensional Direct
 Read ETL Data
 Load Data
 Load Data
 Load T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com BI Tool (OBIEE)
 with metadata
 layer OLAP / In-Memory
 Tool with data load
 into own database
  • 8. Unstructured, Semi-Structured and Schema-Less Data • Gaining access to the vast amounts of non-financial / application data out there ‣Data in documents, spreadsheets etc - Warranty claims, supporting documents, notes etc ‣Data coming from the cloud / social media ‣Data for which we don’t yet have a structure ‣Data who’s structure we’ll decide when we
 Schema-less / NoSQL
 choose to access it (“schema-on-read”) data sources • All of the above could be useful information
 Unstructured/
 Social / Doc
 to have in our DW and BI systems data sources ‣But how do we load it in? Hadoop / ‣And what if we want to access it directly? Big Data
 data sources T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 9. Hadoop, and the Big Data Ecosystem • Apache Hadoop is one of the most well-known Big Data technologies ‣Family of open-source products used to store, and analyze distributed datasets ‣Hadoop is the enabling framework, automatically parallelises and co-ordinates jobs ‣MapReduce is the programming framework 
 for filtering, sorting and aggregating data ‣Map : filter data and pass on to reducers ‣Reduce : sort, group and return results ‣MapReduce jobs can be written in any
 language (Java etc), but it is complicated • Can be used as an extension of the DW staging layer - cheap processing & storage • And there may be data stored in Hadoop that our BI users might benefit from T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 10. HDFS: Low-Cost, Clustered, Fault-Tolerant Storage • The filesystem behind Hadoop, used to store data for Hadoop analysis ‣Unix-like, uses commands such as ls, mkdir, chown, chmod • Fault-tolerant, with rapid fault detection and recovery • High-throughput, with streaming data access and large block sizes • Designed for data-locality, placing data closed to where it is processed • Accessed from the command-line, via internet (hdfs://), GUI tools etc [oracle@bigdatalite mapreduce]$ hadoop fs -mkdir /user/oracle/my_stuff [oracle@bigdatalite mapreduce]$ hadoop fs -ls /user/oracle Found 5 items drwx------ oracle hadoop 0 2013-04-27 16:48 /user/oracle/.staging drwxrwxrwx - oracle hadoop 0 2012-09-18 17:02 /user/oracle/moviedemo drwxrwxrwx - oracle hadoop 0 2012-10-17 15:58 /user/oracle/moviework drwxr-xr-x - oracle hadoop 0 2013-05-03 17:49 /user/oracle/my_stuff drwxr-xr-x - oracle hadoop 0 2012-08-10 16:08 /user/oracle/stage T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 11. Hadoop & HDFS as a Low-Cost Pre-Staging Layer Data
 Load Traditional Relational Data Warehouse Hadoop Pre-ETL
 Filtering &
 Aggregation
 (MapReduce) Traditional structured
 data sources Staging Foundation /
 ODS Performance /
 Dimensional Direct
 Read BI Tool (OBIEE)
 with metadata
 layer Data
 Load Schema-less / NoSQL
 data sources Unstructured/
 Social / Doc
 data sources Hadoop / Big Data
 data sources Data
 Load Data
 Load ETL Data
 Load Low-cost
 file store
 (HDFS) Data
 Load T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) ETL E : info@rittmanmead.com W : www.rittmanmead.com OLAP / In-Memory
 Tool with data load
 into own database
  • 12. Big Data and the Hadoop “Data Warehouse” • Rather than load Hadoop data
 into the DW, access it directly • Hadoop has a “DW layer” called
 Hive, which provides SQL access • Could even be used instead of 
 a traditional DW or data mart • Limited functionality now • But products maturing • and unbeatable TCO Data
 Load Hadoop Cloud-Based
 data sources Pre-ETL
 Filtering &
 Aggregation
 (MapReduce) Direct
 Read Data
 Load Schema-less / NoSQL
 data sources Unstructured/
 Social / Doc
 data sources Hadoop / Big Data
 data sources T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) Data
 Load Data
 Load E : info@rittmanmead.com W : www.rittmanmead.com Hadoop DW 
 Layer (Hive) Low-cost
 file store
 (HDFS) BI Tool (OBIEE)
 with metadata
 layer
  • 13. Hive as the Hadoop “Data Warehouse” • MapReduce jobs are typically written in Java, but Hive can make this simpler • Hive is a query environment over Hadoop/MapReduce to support SQL-like queries • Hive server accepts HiveQL queries via HiveODBC or HiveJDBC, automatically
 creates MapReduce jobs against data previously loaded into the Hive HDFS tables • Approach used by ODI and OBIEE
 to gain access to Hadoop data • Allows Hadoop data to be accessed just like 
 any other data source (sort of...) T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 14. How Hive Provides SQL Access over Hadoop • Hive uses a RBDMS metastore to hold
 table and column definitions in schemas • Hive tables then map onto HDFS-stored files ‣Managed tables ‣External tables • Oracle-like query optimizer, compiler,
 executor HDFS • JDBC and OBDC drivers,
 plus CLI etc Hive Driver
 (Compile 
 Optimize, Execute) Metastore Managed Tables External Tables /user/hive/warehouse/ /user/oracle/ /user/movies/data/ HDFS or local files 
 loaded into Hive HDFS
 area, using HiveQL
 CREATE TABLE
 command T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com HDFS files loaded into HDFS
 using external process, then
 mapped into Hive using
 CREATE EXTERNAL TABLE
 command
  • 15. Transforming HiveQL Queries into MapReduce Jobs • HiveQL queries are automatically translated into Java MapReduce jobs • Selection and filtering part becomes Map tasks • Aggregation part becomes the Reduce tasks Map
 Task Map
 Task SELECT a, sum(b)
 FROM myTable
 WHERE a<100
 Map
 Task GROUP BY a Reduce
 Task Reduce
 Task Result T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 16. An example Hive Query Session: Connect and Display Table List [oracle@bigdatalite ~]$ hive
 Hive history file=/tmp/oracle/hive_job_log_oracle_201304170403_1991392312.txt hive> show tables;
 OK
 dwh_customer
 dwh_customer_tmp
 i_dwh_customer
 ratings
 src_customer
 src_sales_person
 weblog
 weblog_preprocessed
 weblog_sessionized
 Time taken: 2.925 seconds Hive Server lists out all “tables” that have been defined within the Hive
 environment T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 17. An example Hive Query Session: Display Table Row Count hive> select count(*) from src_customer;! Request count(*) from table 
 Total MapReduce jobs = 1
 Launching Job 1 out of 1
 Number of reduce tasks determined at compile time: 1
 Hive server generates In order to change the average load for a reducer (in bytes):
 set hive.exec.reducers.bytes.per.reducer=
 MapReduce job to “map” table In order to limit the maximum number of reducers:
 key/value pairs, and then set hive.exec.reducers.max=
 reduce the results to table In order to set a constant number of reducers:
 count set mapred.reduce.tasks=
 Starting Job = job_201303171815_0003, Tracking URL = 
 http://localhost.localdomain:50030/jobdetails.jsp?jobid=job_201303171815_0003
 Kill Command = /usr/lib/hadoop-0.20/bin/
 hadoop job -Dmapred.job.tracker=localhost.localdomain:8021 -kill job_201303171815_0003
 
 2013-04-17 04:06:59,867 Stage-1 map 2013-04-17 04:07:03,926 Stage-1 map 2013-04-17 04:07:14,040 Stage-1 map 2013-04-17 04:07:15,049 Stage-1 map Ended Job = job_201303171815_0003
 OK
 = = = = 0%, reduce = 100%, reduce 100%, reduce 100%, reduce 0%
 = 0%
 = 33%
 = 100%
 MapReduce job automatically run by Hive Server ! 25
 Time taken: 22.21 seconds Results returned to user T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 18. Leveraging Hadoop with OBIEE 11g and ODI 11g
 Demonstration of Hive and HiveQL T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 19. DW 2013: The Mixed Architecture with Federated Queries • Where many organisations are going: • Traditional DW at core of strategy • Making increasing use of low-cost, 
 cloud/big data tech for storage / 
 pre-processing • Access to non-traditional data sources,
 usually via ETL in to the DW • Federated data access through
 OBIEE connectivity & metadata layer T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 20. Oracle’s Big Data Products • Oracle Big Data Appliance - Engineered System for Big Data Acquisition and Processing ‣Cloudera Distribution of Hadoop ‣Cloudera Manager ‣Open-source R ‣Oracle NoSQL Database Community Edition ‣Oracle Enterprise Linux + Oracle JVM • Oracle Big Data Connectors ‣Oracle Loader for Hadoop (Hadoop > Oracle RBDMS) ‣Oracle Direct Connector for HDFS (HFDS > Oracle RBDMS) ‣Oracle Data Integration Adapter for Hadoop ‣Oracle R Connector for Hadoop ‣Oracle NoSQL Database (column/key-store DB based on BerkeleyDB) T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 21. Oracle Loader for Hadoop • Oracle technology for accessing Hadoop data, and loading it into an Oracle database • Pushes data transformation, “heavy lifting” to the Hadoop cluster, using MapReduce • Direct-path loads into Oracle Database, partitioned and non-partitioned • Online and offline loads • Key technology for fast load of 
 Hadoop results into Oracle DB T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 22. Oracle Direct Connector for HDFS • Enables HDFS as a data-source for Oracle Database external tables • Effectively provides Oracle SQL access over HDFS • Supports data query, or import into Oracle DB • Treat HDFS-stored files in the same way as regular files ‣But with HDFS’s low-cost ‣… and fault-tolerance T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 23. Oracle Data Integration Adapter for Hadoop • ODI 11g Application Adapter (pay-extra option) for Hadoop connectivity • Works for both Windows and Linux installs of ODI Studio ‣Need to source HiveJDBC drivers and JARs from separate Hadoop install • Provides six new knowledge modules ‣IKM File to Hive (Load Data) ‣IKM Hive Control Append ‣IKM Hive Transform ‣IKM File-Hive to Oracle (OLH) ‣CKM Hive ‣RKM Hive T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 24. ODI as Part of Oracle’s Big Data Strategy • ODI is the data integration tool for extracting data from Hadoop/MapReduce, and loading 
 into Oracle Big Data Appliance, Oracle Exadata and Oracle Exalytics • Oracle Application Adaptor for Hadoop provides required data adapters ‣Load data into Hadoop from local filesystem,
 or HDFS (Hadoop clustered FS) ‣Read data from Hadoop/MapReduce using
 Apache Hive (JDBC) and HiveQL, load
 into Oracle RDBMS using
 Oracle Loader for Hadoop • Supported by Oracle’s Engineered Systems ‣Exadata ‣Exalytics ‣Big Data Appliance T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 25. Oracle Business Analytics and Big Data Sources • OBIEE 11g can also make use of big data sources ‣OBIEE 11.1.1.7+ supports Hive/Hadoop as a data source ‣Oracle R Enterprise can expose R models through DB functions, columns ‣Oracle Exalytics has InfiniBand connectivity to Oracle BDA • Endeca Information Discovery can analyze unstructured and semi-structured sources ‣Increasingly tighter-integration between
 OBIEE and Endeca T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 26. Opportunities for OBIEE and ODI with Big Data Sources and Tools • Load data from a Hadoop/HDFS/NoSQL environment into a structured DW for analysis • Provide OBIEE as an alternative to 
 Java coding or HiveQL for analysts • Leverage Hadoop & HDFS for
 massively-parallel staging-layer
 number crunching • Make use of low-cost, fault-tolerant
 hardware for parts of your BI platform • Provide the reporting and analysis
 for customers who have bought
 Oracle Big Data Appliance T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 27. OBIEE and ODI Access to Hive: MapReduce with no Java Coding • Requests in HiveQL arrive via HiveODBC, HiveJDBC
 or through the Hive command shell • JDBC and ODBC access requires Thift server ‣Provides RPC call interface over Hive for external procs • All queries then get parsed, optimized and compiled, then
 sent to Hadoop NameNode and Job Tracker • Then Hadoop processes the query, generating MapReduce
 jobs and distributing it to run in parallel across all data nodes • Hadoop access can still be performed procedurally if needed,
 typically coded by hand in Java, or through Pig, etc ‣The equivalent of PL/SQL compared to SQL ‣But Hive works well with the OBIEE/ODI paradigm T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 28. Complementary Technologies: HDFS, Cloudera Manager, Hue etc • You can download your own Hive binaries, libraries etc from Apache Hadoop website • Or use pre-built VMs and distributions from the likes of Cloudera ‣Cloudera CDH3/4 is used on Oracle Big Data Appliance ‣Open-source + proprietary tools (Cloudera Manager) • Other tools for managing Hive, HFDS etc including ‣Hue (HDFS file browser + management) ‣Beeswax (Hive administration + querying) • Other complementary/required Hadoop tools ‣Sqoop ‣HDFS ‣Thrift T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 29. Leveraging Hadoop with OBIEE 11g and ODI 11g
 Part 2 : ODI 11g and Hadoop / Big Data Sources T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 30. How ODI Accesses Hadoop Data • ODI accesses data in Hadoop clusters through Apache Hive ‣Metadata and query layer over MapReduce ‣Provides SQL-like language (HiveQL) and a data dictionary ‣Provides a means to define “tables”, into 
 which file data is loaded, and then queried 
 Hadoop Cluster via MapReduce ‣Accessed via Hive JDBC driver(separate 
 MapReduce Hadoop install required
 on ODI server, for client libs) Hive Server • Additional access through
 HiveQL Oracle Direct Connector for HDFS
 and Oracle Loader for Hadoop ODI 11g T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com Oracle RDBMS Direct-path loads using 
 Oracle Loader for Hadoop, 
 transformation logic in MapReduce
  • 31. Relationship Between ODI and OBIEE with Big Data Sources • OBIEE now has the ability to report
 against Hadoop data, via Hive ‣Assumes that data is already loaded
 into the Hive warehouse tables • ODI therefore can be used to load
 the Hive tables, through either: ‣Loading Hive from files ‣Joining and loading from Hive-Hive ‣Loading and transforming via 
 shell scripts (python, perl etc) • ODI could also extract the Hive data
 and load into Oracle, if more appropriate T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 32. Configuring ODI 11.1.1.6+ for Hadoop Connectivity • Obtain an installation of Hadoop/Hive from somewhere (Cloudera CDH3/4 for example) • Copy the following files into a temp directory, archive and transfer to ODI environment
 
 $HIVE_HOME/lib/*.jar $HADOOP_HOME/hadoop-*-core*.jar, 
 $HADOOP_HOME/Hadoop-*-tools*.jar 
 for example... ! /usr/lib/hive/lib/*.jar /usr/lib/hadoop-0.20/hadoop-*-core*.jar, ! /usr/lib/hadoop-0.20/Hadoop-*-tools*.jar ! • Copy JAR files into userlib directory and (standalone) agent lib directory c:UsersAdministratorAppDataRoamingodioraclediuserlib ! ! • Restart ODI Studio T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 33. Registering HDFS and Hive Sources and Targets in ODI • For Hive sources and targets, use Hive technology ‣JDBC Driver : Apache Hive JDBC Driver ‣JDBC URL : jdbc:hive://[server_name]:10000/default ‣(Flexfield Name) Hive Metastore URIs : thrift://[server_name]:10000 ! • For HFDS sources, use File technology ‣JDBC URL : 
 hdfs://[server_name]:port ‣Special HDFS “trick” to use File tech
 (no specific HDFS technology) T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 34. Reverse Engineering Hive, HDFS and Local File Datastores + Models • Hive tables reverse-engineer just like regular tables • Define model in Designer navigator, uses Hive RKM to retrieve table metadata • Information on Hive-specific metadata stored in flexfields ‣Hive Buckets ‣Hive Partition Column ‣Hive Cluster Column ‣Hive Sort Column T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 35. Leveraging Hadoop with OBIEE 11g and ODI 11g
 Demonstration of ODI 11.1.1.6 Configured for Hadoop Access, with Hive/HFDS source and targets registered T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 36. Oracle Data Integration Adapter for Hadoop • ODI 11g Application Adapter (pay-extra option) for Hadoop connectivity • Works for both Windows and Linux installs of ODI Studio ‣Need to source HiveJDBC drivers and JARs from separate Hadoop install • Provides six new knowledge modules ‣IKM File to Hive (Load Data) ‣IKM Hive Control Append ‣IKM Hive Transform ‣IKM File-Hive to Oracle (OLH) ‣CKM Hive ‣RKM Hive T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 37. Oracle Loader for Hadoop • Oracle technology for accessing Hadoop data, and loading it into an Oracle database • Pushes data transformation, “heavy lifting” to the Hadoop cluster, using MapReduce • Direct-path loads into Oracle Database, partitioned and non-partitioned • Online and offline loads • Key technology for fast load of 
 Hadoop results into Oracle DB T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 38. Oracle Direct Connector for HDFS • Enables HDFS as a data-source for Oracle Database external tables • Effectively provides Oracle SQL access over HDFS • Supports data query, or import into Oracle DB • Treat HDFS-stored files in the same way as regular files ‣But with HDFS’s low-cost ‣… and fault-tolerance T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 39. IKM File to Hive (Load Data): Loading Hive Tables from File or HDFS • Uses the Hive Load Data command to load 
 from local or HDFS files • Calls Hadoop FS commands for simple 
 copy/move into/around HDFS • Commands generated by ODI through 
 IKM File to Hive (Load Data) hive> load data inpath '/user/oracle/movielens_src/u.data'
 > overwrite into table movie_ratings;
 
 Loading data to table default.movie_ratings
 Deleted hdfs://localhost.localdomain/user/hive/warehouse/
 movie_ratings
 
 OK
 Time taken: 0.341 seconds T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 40. IKM File to Hive (Load Data): Loading Hive Tables from File or HDFS • IKM File to Hive (Load Data) generates the
 required HiveQL commands using a script template • Executed over HiveJDBC interface • Success/Failure/Warning returned to ODI T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 41. Load Data and Hadoop SerDe (Serializer-Deserializer) Transforms • Hadoop SerDe transformations can be 
 accessed, for example to transform weblogs • Hadoop interface that contains: ‣Deserializer - converts incoming data
 into Java objects for Hive manipulation ‣Serializer - takes Hive Java objects &
 converts to output for HDFS • Library of SerDe transformations readily
 available for use with Hive • Use the OVERRIDE_ROW_FORMAT
 option in IKM to override regular column
 mappings in Mapping tab T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 42. IKM Hive Control Append: Load, Join & Filtering Between Hive Tables • Hive source and target, transformations according to HiveQL
 functionality (aggregations, functions etc) • Ability to join data sources • Other data sources can be used, 
 but will involve staging tables and 
 additional KMs (as per any multi-source join) T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 43. IKM Hive Transform: Use Custom Shell Scripts to Integrate into Hive Table • Gives developer the ability
 to transform data 
 programmatically using
 Python, Perl etc scripts • Options to map output
 of script to columns in
 Hive table • Useful for more 
 programmatic and complex
 data transformations T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 44. IKM File-Hive to Oracle: Extract from Hive into Oracle Tables • Uses Oracle Loaded for Hadoop (OLH) to process
 any filtering, aggregation, transformation in Hadoop,
 using MapReduce • OLH part of Oracle Big Data Connectors (additional cost) • High-performance loader into Oracle DB • Optional sort by primary key, pre-partioning of data • Can utilise the two OLH loading modes: • JDBC or OCI direct load into Oracle • Unload to files, Oracle DP into Oracle DB T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 45. Leveraging Hadoop with OBIEE 11g and ODI 11g
 Demonstration of Integration Tasks using ODIAAH Hadoop KMs T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 46. NoSQL Data Sources and Targets with ODI 11g • No specific technology or driver for NoSQL databases, but can use Hive external tables • Requires a specific “Hive Storage Handler” for key/value store sources ‣Hive feature for accessing data from other DB systems, for example MongoDB, Cassandra ‣For example, https://github.com/vilcek/HiveKVStorageHandler • Additionally needs Hive collect_set aggregation method to aggregate results ‣Has to be defined in Languages panel in Topology T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 47. Pig, Sqoop and other Hadoop Technologies, and Hive • Future versions of ODI might use other Hadoop technologies ‣Apache Sqoop for bulk transfer between Hadoop and RBDMSs • Other technologies are not such an obvious fit ‣Apache Pig - the equivalent of PL/SQL for Hive’s SQL • Commercial vendors may produce “better” versions of Hive, MapReduce etc ‣Cloudera Impala - more “real-time” version of Hive ‣MapR - solves many current issues with MapReduce, 100% Hadoop API compatibility • Watch this space...! T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 48. Leveraging Hadoop with OBIEE 11g and ODI 11g
 Part 3 : OBIEE 11g and Hadoop / Big Data Sources T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 49. OBIEE 11g and Hadoop/Big Data Access • Two main scenarios for OBIEE 11g accessing “big data” sources 1. Through the data warehouse - no different to any other data provided through the DW 2. Directly - through OBIEE 11.1.1.7+ Hadoop/Hive connectivity 1 T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) 2 E : info@rittmanmead.com W : www.rittmanmead.com
  • 50. New in OBIEE 11.1.1.7 : Hadoop Connectivity through Hive • MapReduce jobs are typically written in Java, but Hive can make this simpler • Hive is a query environment over Hadoop/MapReduce to support SQL-like queries • Hive server accepts HiveQL queries via HiveODBC or HiveJDBC, automatically
 creates MapReduce jobs against data previously loaded into the Hive HDFS tables • Approach used by ODI and OBIEE to gain access to Hadoop data • Allows Hadoop data to be accessed just like any other data source T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 51. Importing Hadoop/Hive Metadata into RPD • HiveODBC driver has to be installed into Windows environment, so that 
 BI Administration tool can connect to Hive and return table metadata • Import as ODBC datasource, change physical DB type to Apache Hadoop afterwards • Note that OBIEE queries cannot span >1 Hive schema (no table prefixes) 2 1 3 T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 52. Set up ODBC Connection at the OBIEE Server • OBIEE 11.1.1.7+ ships with HiveODBC drivers, need to use 7.x versions though (only Linux supported) • Configure the ODBC connection in odbc.ini, name needs to match RPD ODBC name • BI Server should then be able to connect to the Hive server, and Hadoop/MapReduce [ODBC Data Sources]
 AnalyticsWeb=Oracle BI Server
 Cluster=Oracle BI Server
 SSL_Sample=Oracle BI Server
 bigdatalite=Oracle 7.1 Apache Hive Wire Protocol [bigdatalite]
 Driver=/u01/app/Middleware/Oracle_BI1/common/ODBC/
 Merant/7.0.1/lib/ARhive27.so
 Description=Oracle 7.1 Apache Hive Wire Protocol
 ArraySize=16384
 Database=default
 DefaultLongDataBuffLen=1024
 EnableLongDataBuffLen=1024
 EnableDescribeParam=0
 Hostname=bigdatalite
 LoginTimeout=30
 MaxVarcharSize=2000
 PortNumber=10000
 RemoveColumnQualifiers=0
 StringDescribeType=12
 TransactionMode=0
 UseCurrentSchema=0 T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 53. Leveraging Hadoop with OBIEE 11g and ODI 11g
 Demonstration of OBIEE 11.1.1.7 accessing Hadoop
 through Hive Connectivity T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 54. Dealing with Hadoop / Hive Latency Option 1 : Exalytics • Hadoop access through Hive can be slow - due to inherent latency in Hive • Hive queries use MapReduce in the background to query Hadoop • Spins-up Java VM on each query • Generates MapReduce job • Runs and collates the answer • Great for large, distributed queries ... • ... but not so good for “speed-of-thought” dashboards • So what if we could use Exalytics to speed-up Hadoop queries? T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 55. Oracle Exalytics In-Memory Machine • Engineered system, complements Oracle Exadata Database Machine (but can work standalone) • Combination of high-end hardware (Sun x86_64 architecture, 3RU rack-mountable, 1-2TB RAM)
 and optimized versions of Oracle’s BI, In-Memory Database and OLAP software • Delivers “in-memory analytics” focusing on analysis, aggregation and UI ‣Rich, interactive dashboards with split-second response times ‣1-2TB (and now 4TB) of RAM, to run your analysis in-memory ‣Infiniband connection to Exadata and Oracle BDA ‣40 CPU cores (and now 128) to support high user numbers ‣Lower TCO through known configuration, 
 combined patch sets ‣Contains software features only licensable through
 Exalytics package T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 56. Exalytics as the Query Performance Enhancer Aggregates Data Warehouse Detail-level
 Data T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com Exalytics • In conjunction with a well-tuned data warehouse, Exalytics adds an in-memory analysis layer • Based around Oracle TimesTen for Exalytics, Oracle’s In-Memory Database • Aggregates are recommended based on query patterns, automatically created in TimesTen • Summary Advisor makes recommendations, which adapt as queries change • Meant to be “plug-and-play” - no need for 
 expensive data warehouse tuning TimesTen BI Server • So can we use this for speeding-up Hadoop/Hive queries?
  • 57. Summary Advisor for Aggregate Recommendation & Creation • Utility within Oracle BI Administrator tool that recommends aggregates • Bases recommendations on usage tracking and summary statistics data • Captured based on past activity • Runs an iterative algorithm that searches,
 each iteration, for the best aggregate T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 58. Running Some Sample Hadoop / Hive Queries • A simple Hadoop / Hive BMM was created, based off of a single Hive table • Queries run against that BMM that requested aggregates • Query details, and requested aggregates, go in usage tracking & summary statistics tables • Avg. query response time = 30 secs+ select avg(T44678.age) as c1, T44678.sales_pers as c2, sum(T44678.age) as c3, count(T44678.age) as c4 from dwh_customer T44678 group by T44678.sales_pers T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 59. Generate Aggregate Recommendations using Summary Advisor • Ensure BMM has one or more logical dimensions + 2 or more logical levels • Ensure S_NQ_SUMMARY_ADVISOR table has aggregate recordings + level details • Generate summary recommendations using Summary Advisor, output as nqcmd script T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 60. Implement Recommendations, Review Updated RPD • Run generated logical SQL (Aggregate Persistence) script to create & populate TT tables • Automatically updates RPD to “plug-in” new TimesTen aggregate tables T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 61. Re-run Reports, now with TimesTen for Exalytics Acceleration • Reports can now be re-run to test improvements from 
 in-memory aggregation • Response time is now instantaneous • Aggregates will need to be refreshed once new data is 
 loaded into Hadoop • Can also be used to improve speed of federated 
 RDBMS - Hadoop - OLAP queries too ‣But - relies on query caching - doesn’t make
 Hadoop “faster”… T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 62. Dealing with Hadoop / Hive Latency Option 2 : Use Impala • Hive is slow - because it’s meant to be used for batch-mode queries • Many companies / projects are trying to improve Hive - one of which is Cloudera • Cloudera Impala is an open-source but 
 commercially-sponsored in-memory MPP platform • Replaces Hive and MapReduce in the Hadoop stack • Can we use this, instead of Hive, to access Hadoop? ‣It will need to work with OBIEE ‣Warning - it won’t be a supported data source (yet…) T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 63. How Impala Works • A replacement for Hive, but uses Hive concepts and
 data dictionary (metastore) • MPP (Massively Parallel Processing) query engine
 that runs within Hadoop ‣Uses same file formats, security,
 resource management as Hadoop • Processes queries in-memory • Accesses standard HDFS file data • Option to use Apache AVRO, RCFile,
 LZO or Parquet (column-store) • Designed for interactive, real-time
 SQL-like access to Hadoop BI Server Presentation Svr Cloudera Impala
 ODBC Driver Impala Impala Hadoop Hadoop HDFS etc Hadoop HDFS etc Impala Hadoop HDFS etc T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) Impala E : info@rittmanmead.com W : www.rittmanmead.com HDFS etc Impala Hadoop HDFS etc Multi-Node
 Hadoop Cluster
  • 64. Connecting OBIEE 11.1.1.7 to Cloudera Impala • Warning - unsupported source - limited testing and no support from MOS • Requires Cloudera Impala ODBC drivers - Windows or Linux (RHEL etc/SLES) - 32/64 bit • ODBC Driver / DSN connection steps similar to Hive T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 65. Importing Impala Metadata • Import Impala tables (via the Hive metastore) into RPD • Set database type to “Apache Hadoop” ‣Warning - don’t set ODBC type to Hadoop- leave at ODBC 2.0 ‣Create physical layer keys, joins etc as normal T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 66. Importing RPD using Impala Metadata • Create BMM layer, Presentation layer as normal • Use “View Rows” feature to check connectivity back to Impala / Hadoop T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 67. Impala / OBIEE Issue with ORDER BY Clause • Although checking rows in the BI Administration tool worked, any query that aggregates
 data in the dashboard will fail • Issue is that Impala requires LIMIT with all ORDER BY clauses ‣OBIEE could use LIMIT, but doesn’t for Impala 
 at the moment (because not supported) • Workaround - disable ORDER BY in 
 Database Features, have the BI Server do sorting ‣Not ideal - but it works, until Impala supported T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 68. So Does Impala Work, as a Hive Substitute? • With ORDER BY disabled in DB features, it appears to • But not extensively tested by me, or Oracle • But it’s certainly interesting • Reduces 30s, 180s queries down to 1s, 10s etc • Impala, or one of the competitor projects
 (Drill, Dremel etc) assumed to be the
 real-time query replacement for Hive, in time ‣Oracle announced planned support for 
 Impala at OOW2013 - watch this space T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 69. Thank You for Attending! • Thank you for attending this presentation, and more information can be found at http:// www.rittmanmead.com • Contact us at info@rittmanmead.com or mark.rittman@rittmanmead.com • Look out for our book, “Oracle Business Intelligence Developers Guide” out now! • Follow-us on Twitter (@rittmanmead) or Facebook (facebook.com/rittmanmead) T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com
  • 70. T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com