ODI12c as your Big Data Integration Hub

Oracle
Data
Integrator
12c
As
Your
Big
Data
Data
Integration
Hub
Mark
Rittman
Chief
Technical
Officer
Rittman
Mead
Month
00,
2014
Copyright
©
2014
Oracle
and/or
its
affiliates.
All
rights
reserved.
| Oracle
Confidential
–
Internal/Restricted/Highly
Restricted

ODI12c as your Big Data Integration Hub
Mark Rittman, CTO, Rittman Mead
July 2014
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+61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India)
E : info@rittmanmead.com
W : www.rittmanmead.com

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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

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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

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
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Staging Foundation /
ODS
Performance /
Dimensional
ETL ETL
BI Tool (OBIEE)
with metadata
layer
OLAP / In-Memory
Tool with data load
into own database
Direct
Read
Data
Load
Traditional structured
data sources
Data
Load
Data
Load
Data
Load
Traditional Relational Data Warehouse

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
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Introduction of New Data Sources : Unstructured, Big Data
ETL ETL
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ODS
Performance /
Dimensional
BI Tool (OBIEE)
with metadata
layer
OLAP / In-Memory
Tool with data load
into own database
Direct
Read
Data
Load
data sources
Data
Load
Data
Load
Data
Load
Schema-less / NoSQL
data sources
Unstructured/
Social / Doc
data sources
Hadoop /
Big Data
data sources
Data
Load

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
choose to access it (“schema-on-read”)
•All of the above could be useful information
to have in our DW and BI systems
‣But how do we load it in?
‣And what if we want to access it directly?
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Schema-less / NoSQL
data sources
Unstructured/
Social / Doc
data sources
Hadoop /
Big Data
data sources

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
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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
drwxrwxrwx - oracle hadoop 0 2013-05-03 17:49 /user/oracle/my_stuff
drwxrwxrwx - oracle hadoop 0 2012-08-10 16:08 /user/oracle/stage
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Hadoop & HDFS as a Low-Cost Pre-Staging Layer
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ODS
Performance /
Dimensional
ETL ETL
BI Tool (OBIEE)
with metadata
layer
OLAP / In-Memory
Tool with data load
into own database
Direct
Read
Data
Load
data sources
Data
Load
Data
Load
Data
Load
Schema-less / NoSQL
data sources
Unstructured/
Social / Doc
data sources
Hadoop /
Big Data
data sources Data
Load
Pre-ETL
Filtering &
Aggregation
(MapReduce)
Low-cost
file store
(HDFS)
Data
Load
Hadoop

Big Data and the Hadoop “Data Warehouse”
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BI Tool (OBIEE)
with metadata
layer
Direct
Read
Data
Load
Data
Load
Data
Load
Schema-less / NoSQL
data sources
Unstructured/
Social / Doc
data sources
Hadoop /
Big Data
data sources Data
Load
Hadoop
Pre-ETL
Filtering &
Aggregation
(MapReduce)
Low-cost
file store
(HDFS)
Hadoop DW
Layer (Hive)
Cloud-Based
data sources
•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

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...)
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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
•JDBC and OBDC drivers,
plus CLI etc
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Hive Driver
(Compile
Optimize, Execute)
Managed Tables
/user/hive/warehouse/
External Tables
/user/oracle/
/user/movies/data/
HDFS
HDFS or local files
loaded into Hive HDFS
area, using HiveQL
CREATE TABLE
command
HDFS files loaded into HDFS
using external process, then
mapped into Hive using
CREATE EXTERNAL TABLE
command
Metastore

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
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SELECT a, sum(b)
FROM myTable
WHERE a<100
GROUP BY a
Map
Task
Map
Task
Map
Task
Reduce
Task
Reduce
Task
Result

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
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Hive Server lists out all
“tables” that have been
defined within the Hive
environment

An example Hive Query Session: Display Table Row Count
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hive> select count(*) from src_customer;!
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
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 = 0%, reduce = 0%
2013-04-17 04:07:03,926 Stage-1 map = 100%, reduce = 0%
2013-04-17 04:07:14,040 Stage-1 map = 100%, reduce = 33%
2013-04-17 04:07:15,049 Stage-1 map = 100%, reduce = 100%
Ended Job = job_201303171815_0003
OK !
25
Time taken: 22.21 seconds
Request count(*) from table
Hive server generates
MapReduce job to “map” table
key/value pairs, and then
reduce the results to table
count
MapReduce job automatically
run by Hive Server
Results returned to user

Demonstration of Hive and HiveQL
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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
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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)
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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

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
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Oracle Data Integration Adapter for Hadoop
•ODI 11g/12c 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
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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
via MapReduce
‣Accessed via Hive JDBC driver(separate
Hadoop install required
on ODI server, for client libs)
•Additional access through
and Oracle Loader for Hadoop
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Hadoop Cluster
MapReduce
Hive Server
ODI 11g
Oracle RDBMS
HiveQL
Direct-path loads using
Oracle Loader for Hadoop,
transformation logic in
MapReduce

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
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Support for Heterogenous Sources and Targets
•ODI12c isn’t just a big data ETL tool though
•Technology adapters for most RDBMSs, file types, OBIEE, application sources
•Multidimensional servers such as Oracle Essbase, and associated EPM apps
•XML sources, and JMS queues
•SOA environments, using messaging
and service buses, typically in real-time
•All enabled through “knowledge module”
approach - ODI acts as orchestrator and
code generator, uses E-L-T approach
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The Key to ODI Extensibility - Knowledge Modules
•Divides the ETL process into separate steps - extract (load), integrate, check constraints etc
•ODI generates native code for each platform, taking a template for each step + adding
table names, column names, join conditions etc
‣Easy to extend
‣Easy to read the code
‣Makes it possible for ODI to
support Spark, Pig etc in future
‣Uses the power of the target
platform for integration tasks
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-Hadoop-native ETL

Part of the Wider Oracle Data Integration Platform
•Oracle Data Integrator for large-scale data integration across heterogenous sources and
targets
•Oracle GoldenGate for heterogeneous data replication and changed data capture
•Oracle Enterprise Data Quality for data profiling and cleansing
•Oracle Data Services Integrator
for SOA message-based
data federation
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ODI and Big Data Integration Example
•In this example, we’ll show an end-to-end ETL process on Hadoop using ODI12c & BDA
•Scenario: load webserver log data into Hadoop, process enhance and aggregate,
then load final summary table into Oracle Database 12c
‣Process using Hadoop framework
‣Leverage Big Data Connectors
‣Metadata-based ETL development
using ODI12c
‣Real-world example
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ETL & Data Flow through BDA System
•Five-step process to load, transform, aggregate and filter incoming log data
•Leverage ODI’s capabilities where possible
•Make use of Hadoop power
+ scalability
Flume
Agent
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Sqoop extract
!
posts
(Hive Table)
IKM Hive Control Append
(Hive table join & load into
target hive table)
categories_sql_
extract
(Hive Table)
hive_raw_apache_
access_log
(Hive Table)
Flume
Agent
!!!!!!
Apache HTTP
Server
Log Files (HDFS)
Flume Messaging
TCP Port 4545
(example)
IKM File to Hive
1 using RegEx SerDe
log_entries_
and post_detail
(Hive Table)
IKM Hive Control Append
(Hive table join & load into
target hive table)
hive_raw_apache_
access_log
(Hive Table)
2 3
Geocoding
IP>Country list
(Hive Table)
IKM Hive Transform
(Hive streaming through
Python script)
4 5
hive_raw_apache_
access_log
(Hive Table)
IKM File / Hive to Oracle
(bulk unload to Oracle DB)

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Five-Step ETL Process
1. Take the incoming log files (via Flume) and load into a structured Hive table
2. Enhance data from that table to include details on authors, posts from other Hive tables
3. Join to some additional ref. data held in an Oracle database, to add author details
4. Geocode the log data, so that we have the country for each calling IP address
5. Output the data in summary form to an Oracle database

Using Flume to Transport Log Files to BDA
•Apache Flume is the standard way to transport log files from source through to target
•Initial use-case was webserver log files, but can transport any file from A>B
•Does not do data transformation, but can send to multiple targets / target types
•Mechanisms and checks to ensure successful transport of entries
•Has a concept of “agents”, “sinks” and “channels”
•Agents collect and forward log data
•Sinks store it in final destination
•Channels store log data en-route
•Simple configuration through INI files
•Handled outside of ODI12c
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GoldenGate for Continuous Streaming to Hadoop
•Oracle GoldenGate is also an option, for streaming RDBMS transactions to Hadoop
•Leverages GoldenGate & HDFS / Hive Java APIs
•Sample Implementations on MOS Doc.ID 1586210.1 (HDFS) and 1586188.1 (Hive)
•Likely to be formal part of GoldenGate in future release - but usable now
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Using Flume for Distributed Log Capture to Single Target
•Multiple agents can be used to capture logs from many sources, combine into one output
•Needs at least one source agent, and a target agent
•Agents can be multi-step, handing-off data across the topology
•Channels store data in files, or in RAM, as a buffer between steps
•Log files being continuously written to have
contents trickle-fed across to source
•Sink types for Hive, HBase and many others
•Free software, part of Hadoop platform
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Configuring Flume for Log Transport to the BDA
•Conf file for source system agent
•TCP port, channel size+type, source type
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•Conf file for target system agent
•TCP port, channel size+type, sink type

Starting the Agents, Check Files Landing in HDFS Directory
•Start the Flume agents on source and target (BDA) servers
•Check that incoming file data starts appearing in HDFS
‣Note - files will be continuously written-to as
entries added to source log files
‣Channel size for source, target agents
determines max no. of events buffered
‣If buffer exceeded, new events dropped
until buffer < channel size
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Load Incoming Log Files into Hive Table
•First step in process is to load the incoming log files into a Hive table
‣Also need to parse the log entries to extract request, date, IP address etc columns
‣Hive table can then easily be used in
downstream transformations
•Use IKM File to Hive (LOAD DATA) KM
‣Source can be local files or HDFS
‣Either load file into Hive HDFS area,
or leave as external Hive table
‣Ability to use SerDe to parse file data
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1

First Though … Need to Setup Topology and Models
•HDFS data servers (source) defined using generic File technology
•Workaround to support IKM Hive Control Append
•Leave JDBC driver blank, put HDFS URL in JDBC URL field
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Defining Physical Schema and Model for HDFS Directory
•Hadoop processes typically access a whole directory of files in HDFS, rather than single one
•Hive, Pig etc aggregate all files in that directory and treat as single file
•ODI Models usually point to a single file though -
how do you set up access correctly?
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Defining Physical Schema and Model for HDFS Directory
•ODI appends file name to Physical Schema name for Hive access
•To access a directory, set physical
schema to parent directory
•Set model Resource Name to
directory you want to use as source
•Note - need to manually enter file/
resource names, and “Test” button
does not work for HDFS sources
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Defining Topology and Model for Hive Sources
•Hive supported “out-of-the-box” with ODI12c (but requires ODIAAH license for KMs)
•Most recent Hadoop distributions use HiveServer2 rather than HiveServer
•Need to ensure JDBC drivers support Hive version
•Use correct JDBC URL format (jdbc:hive2//…)
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Hive Tables and Underlying HDFS Storage Permissions
•Hadoop by default has quite loose security
•Files in HDFS organized into directories, using Unix-like permissions
•Hive tables can be created by any user, over directories they have read-access to
‣But that user might not have write permissions on the underlying directory
‣Causes mapping execution failures in ODI if directory read-only
•Therefore ensure you have read/write access to directories used by Hive,
and create tables under the HDFS user you’ll access files through JDBC
‣Simplest approach - create Hue user for “oracle”, create Hive tables under that user
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Final Model and Datastore Definitions
•HDFS files for incoming log data, and any other input data
•Hive tables for ETL targets and downstream processing
•Use RKM Hive to reverse-engineer column definition from Hive
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Using IKM File to Hive to Load Web Log File Data into Hive
•Create mapping to load file source (single column for weblog entries) into Hive table
•Target Hive table should have column for incoming log row, and parsed columns
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Specifying a SerDe to Parse Incoming Hive Data
•SerDe (Serializer-Deserializer) interfaces give Hive the ability to process new file formats
•Distributed as JAR file, gives Hive ability to parse semi-structured formats
•We can use the RegEx SerDe to parse the Apache CombinedLogFormat file into columns
•Enabled through OVERRIDE_ROW_FORMAT IKM File to Hive (LOAD DATA) KM option
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Distributing SerDe JAR Files for Hive across Cluster
•Hive SerDe functionality typically requires additional JARs to be made available to Hive
•Following steps must be performed across ALL BDA nodes:
‣Add JAR reference to HIVE_AUX_JARS_PATH in /usr/lib/hive/conf/hive.env.sh
!
!
!
‣Add JAR file to /usr/lib/hadoop
!
!
!
‣Restart YARN / MR1 TaskTrackers across cluster
export HIVE_AUX_JARS_PATH=/usr/lib/hive/lib/hive-contrib-0.12.0-cdh5.0.1.jar:$
(echo $HIVE_AUX_JARS_PATH…
[root@bdanode1 hadoop]# ls /usr/lib/hadoop/hive-*
/usr/lib/hadoop/hive-contrib-0.12.0-cdh5.0.1.jar
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Executing First ODI12c Mapping
•EXTERNAL_TABLE option chosen in IKM File to Hive (LOAD DATA) as Flume will continue
writing to it until source log rotate
•View results of data load in ODI Studio
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Join to Additional Hive Tables, Transform using HiveQL
•IKM Hive to Hive Control Append can be used to perform Hive table joins, filtering, agg. etc.
•INSERT only, no DELETE, UPDATE etc
•Not all ODI12c mapping operators supported, but basic functionality works OK
•Use this KM to join to other Hive tables,
adding more details on post, title etc
•Perform DISTINCT on join output, load
into summary Hive table
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2

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Joining Hive Tables
•Only equi-joins supported
•Must use ANSI syntax
•More complex joins may not produce
valid HiveQL (subqueries etc)

Filtering, Aggregating and Transforming Within Hive
•Aggregate (GROUP BY), DISTINCT, FILTER, EXPRESSION, JOIN, SORT etc mapping
operators can be added to mapping to manipulate data
•Generates HiveQL functions, clauses etc
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Executing Second Mapping
•ODI IKM Hive to Hive Control Append generates HiveQL to perform data loading
•In the background, Hive on BDA creates MapReduce job(s) to load and transform HDFS data
•Automatically runs across the cluster, in parallel and with fault tolerance, HA
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Bring in Reference Data from Oracle Database
•In this third step, additional reference data from Oracle Database needs to be added
•In theory, should be able to add Oracle-sourced datastores to mapping and join as usual
•But … Oracle / JDBC-generic LKMs don’t get work with Hive
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3

Options for Importing Oracle / RDBMS Data into Hadoop
•Using ODI, only KM option currently is IKM File to Hive (LOAD DATA)
•But this involves an unnecessary export to file before loading
•One option is to use Apache Sqoop, and call from an ODI Procedure
•Hadoop-native, automatically runs in parallel
•Uses native JDBC drivers, or OraOop (for example)
•Bi-directional in-and-out of Hadoop to RDBMS
•Run from OS command-line
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Creating an ODI Procedure to Invoke Sqoop
•Create an OS task, can then reference whole Oracle tables, or an SQL SELECT
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Sqoop Command-Line Parameters
sqoop import —connect jdbc:oracle:thin:@centraldb11gr2.rittmandev.com:1521/
ctrl11g.rittmandev.com —username blog_refdata —password password —query ‘SELECT
p.post_id, c.cat_name from post_one_cat p, categories c where p.cat_id = c.cat_id
and $CONDITIONS’ —target_dir /user/oracle/post_categories —hive-import —hive-overwrite
—hive-table post_categories —split-by p.post_id
•—username, —password : database account username and password
•—query : SELECT statement to retrieve data (can use —table instead, for single table)
•$CONDITIONS, —split-by : column by which MapReduce jobs can be run in parallel
•—hive-import, —hive-overwrite, —hive-table : name and load mode for Hive table
•— target_dir : target HDFS directory to land data in initially (required for SELECT)
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Initial Sqoop Invocation to Create Hive Target Table
•Run Sqoop once (from command-line, or from ODI Procedure) to create the target Hive table
•Can then reverse-engineer the table metadata using RKM Hive, to add to Model
•Thereafter, run as part of Package or Load Plan
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Join Oracle-Sourced Hive Table to Existing Hive Table
•Oracle-sourced reference data in Hive can then be joined to existing Hive table as normal
•Filters, aggregation operators etc can be added to mapping if required
•Use IKM Hive Control Append as integration KM
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Note - New in ODI12c 12.1.3 - Sqoop KM and HBase KMs
•At the time of writing (May 2013) there was no
official Sqoop support in ODI12c
•ODI 12.1.3 (introduced July 2013) introduced a
number of new KMs including
‣IKM SQL to Hive-HBase-File (Sqoop)
‣LKM HBase to Hive
‣IKM Hive to HBase
‣RKM HBase
‣IKM File-Hive to SQL (Sqoop)
•See http://www.ateam-oracle.com/importing-data-
from-sql-databases-into-hadoop-with-sqoop-
and-oracle-data-integrator-odi/ for details
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ODI Static and Flow Control : Data Quality and Error Handling
•CKM Hive can be used with IKM Hive to Hive Control Append to filter out erroneous data
•Static controls can be used to create “data firewalls”
•Flow control used in Physical mapping view to handle errors, exceptions
•Example: Filter out rows where IP address is from a test harness
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Enabling Flow Control in IKM Hive to Hive Control Append
•Check the ENABLE_FLOW_CONTROL option in KM settings
•Select CKM Hive as the check knowledge module
•Erroneous rows will get moved to E_ table in Hive, not loaded into target Hive table
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Using Hive Streaming and Python for Geocoding Data
•Another requirement we have is to “geocode” the webserver log entries
•Allows us to aggregate page views by country
•Based on the fact that IP ranges can usually be attributed to specific countries
•Not functionality normally found in Hive etc, but can be done with add-on APIs
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4

How GeoIP Geocoding Works
•Uses free Geocoding API and database from Maxmind
•Convert IP address to an integer
•Find which integer range our IP address sits within
•But Hive can’t use BETWEEN in a join…
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Solution : IKM Hive Transform
•IKM Hive Transform can pass the output of a Hive SELECT statement through
a perl, python, shell etc script to transform content
•Uses Hive TRANSFORM … USING … AS functionality
hive> add file file:///tmp/add_countries.py;
Added resource: file:///tmp/add_countries.py
hive> select transform (hostname,request_date,post_id,title,author,category)
> using 'add_countries.py'
> as (hostname,request_date,post_id,title,author,category,country)
> from access_per_post_categories;
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Creating the Python Script for Hive Streaming
•Solution requires a Python API to be installed on all Hadoop nodes, along with geocode DB
wget !
https://raw.github.com/pypa/pip/master/contrib/get-pip.py
python !
get-pip.py pip
install pygeoip
!
•Python script then parses incoming stdin lines using tab-separation of fields, outputs same
(but with extra field for the country)
#!/usr/bin/python
import sys
sys.path.append('/usr/lib/python2.6/site-packages/')
import pygeoip
gi = pygeoip.GeoIP('/tmp/GeoIP.dat')
for line in sys.stdin:
line = line.rstrip()
hostname,request_date,post_id,title,author,category = line.split('t')
country = gi.country_name_by_addr(hostname)
print hostname+'t'+request_date+'t'+post_id+'t'+title+'t'+author
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+'t'+country+'t'+category

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Setting up the Mapping
•Map source Hive table to target, which includes column for extra “country” column
!
!
!
!
!
!
!
•Copy script + GeoIP.dat file to every node’s /tmp directory
•Ensure all Python APIs and libraries are installed on each Hadoop node

Configuring IKM Hive Transform
•TRANSFORM_SCRIPT_NAME specifies name of
script, and path to script
•TRANSFORM_SCRIPT has issues with parsing;
do not use, leave blank and KM will use existing one
•Optional ability to specify sort and distribution
columns (can be compound)
•Leave other options at default
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Executing the Mapping
•KM automatically registers the script with Hive (which caches it on all nodes)
•HiveQL output then runs the contents of the first Hive table through the script, outputting
results to target table

Bulk Unload Summary Data to Oracle Database
•Final requirement is to unload final Hive table contents to Oracle Database
•Several use-cases for this:
•Use Hadoop / BDA for ETL offloading
•Use analysis capabilities of BDA, but then output results to RDBMS data mart or DW
•Permit use of more advanced SQL query tools
•Share results with other applications
•Can use Sqoop for this, or use Oracle Big Data Connectors
•Fast bulk unload, or transparent Oracle access to Hive
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5

•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
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Oracle Loader for Hadoop (OLH)
•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
•Load from HDFS or Hive tables
•Key technology for fast load of
Hadoop results into Oracle DB
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IKM File/Hive to Oracle (OLH/ODCH)
•KM for accessing HDFS/Hive data from Oracle
•Either sets up ODCH connectivity, or bulk-unloads via OLH
•Map from HDFS or Hive source to Oracle tables (via Oracle technology in Topology)
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Environment Variable Requirements
•Hardest part in setting up OLH / IKM File/Hive to Oracle is getting environment variables
correct - OLH needs to be able to see correct JARs, configuration files
•Set in /home/oracle/.bashrc - see example below
export HIVE_HOME=/usr/lib/hive
export HADOOP_CLASSPATH=/home/oracle/oracle/product/oraloader-3.0.0-h2/jlib/*:/etc/hive/conf:$HIVE_HOME/lib/
hive-metastore-0.12.0-cdh5.0.1.jar:$HIVE_HOME/lib/libthrift.jar:$HIVE_HOME/lib/libfb303-0.9.0.jar:$HIVE_HOME/
lib/hive-common-0.12.0-cdh5.0.1.jar:$HIVE_HOME/lib/hive-exec-0.12.0-cdh5.0.1.jar
export OLH_HOME=/home/oracle/oracle/product/oraloader-3.0.0-h2
export HADOOP_HOME=/usr/lib/hadoop
export JAVA_HOME=/usr/java/jdk1.7.0_60
export ODI_HIVE_SESSION_JARS=/usr/lib/hive/lib/hive-contrib.jar
export ODI_OLH_JARS=/home/oracle/oracle/product/oraloader-3.0.0-h2/jlib/ojdbc6.jar,/home/oracle/oracle/
product/oraloader-3.0.0-h2/jlib/orai18n.jar,/home/oracle/oracle/product/oraloader-3.0.0-h2/jlib/orai18n-utility.
jar,/home/oracle/oracle/product/oraloader-3.0.0-h2/jlib/orai18n-mapping.jar,/home/oracle/oracle/
product/oraloader-3.0.0-h2/jlib/orai18n-collation.jar,/home/oracle/oracle/product/oraloader-3.0.0-h2/jlib/
oraclepki.jar,/home/oracle/oracle/product/oraloader-3.0.0-h2/jlib/osdt_cert.jar,/home/oracle/oracle/product/
oraloader-3.0.0-h2/jlib/osdt_core.jar,/home/oracle/oracle/product/oraloader-3.0.0-h2/jlib/commons-math-
2.2.jar,/home/oracle/oracle/product/oraloader-3.0.0-h2/jlib/jackson-core-asl-1.8.8.jar,/home/oracle/
oracle/product/oraloader-3.0.0-h2/jlib/jackson-mapper-asl-1.8.8.jar,/home/oracle/oracle/product/
oraloader-3.0.0-h2/jlib/avro-1.7.3.jar,/home/oracle/oracle/product/oraloader-3.0.0-h2/jlib/avro-mapred-1.7.3-
hadoop2.jar,/home/oracle/oracle/product/oraloader-3.0.0-h2/jlib/oraloader.jar,/usr/lib/hive/lib/hive-metastore.
jar,/usr/lib/hive/lib/libthrift-0.9.0.cloudera.2.jar,/usr/lib/hive/lib/libfb303-0.9.0.jar,/usr/lib/
hive/lib/hive-common-0.12.0-cdh5.0.1.jar,/usr/lib/hive/lib/hive-exec.jar
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Configuring the KM Physical Settings
•For the access table in Physical view, change LKM to LKM SQL Multi-Connect
•Delegates the multi-connect capabilities to the downstream node, so you can use a multi-connect
IKM such as IKM File/Hive to Oracle
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Configuring the KM Physical Settings
•For the target table, select IKM File/Hive to Oracle
•Only becomes available to select once
LKM SQL Multi-Connect selected for access table
•Key option values to set are:
•OLH_OUTPUT_MODE (use JDBC initially, OCI
if Oracle Client installed on Hadoop client node)
•MAPRED_OUTPUT_BASE_DIR (set to directory
on HFDS that OS user running ODI can access)
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Executing the Mapping
•Executing the mapping will invoke
OLH from the OS command line
•Hive table (or HDFS file) contents
copied to Oracle table

Create Package to Sequence ETL Steps
•Define package (or load plan) within ODI12c to orchestrate the process
•Call package / load plan execution from command-line, web service call, or schedule
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Execute Overall Package
•Each step executed in sequence
•End-to-end ETL process, using ODI12c’s metadata-driven development process,
data quality handing, heterogenous connectivity, but Hadoop-native processing

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Conclusions
•Hadoop, and the Oracle Big Data Appliance, is an excellent platform for data capture,
analysis and processing
•Hadoop tools such as Hive, Sqoop, MapReduce and Pig provide means to process and
analyse data in parallel, using languages + approach familiar to Oracle developers
•ODI12c provides several benefits when working with ETL and data loading on Hadoop
‣Metadata-driven design; data quality handling; KMs to handle technical complexity
•Oracle Data Integrator Adapter for Hadoop provides several KMs for Hadoop sources
•In this presentation, we’ve seen an end-to-end example of big data ETL using ODI
‣The power of Hadoop and BDA, with the ETL orchestration of ODI12c

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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)

ODI12c as your Big Data Integration Hub

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